Effects of secondary loudspeaker properties on broadband feedforward active duct noise control.

PubMed

Chan, Yum-Ji; Huang, Lixi; Lam, James

2013-07-01

Dependence of the performance of feedforward active duct noise control on secondary loudspeaker parameters is investigated. Noise reduction performance can be improved if the force factor of the secondary loudspeaker is higher. For example, broadband noise reduction improvement up to 1.6 dB is predicted by increasing the force factor by 50%. In addition, a secondary loudspeaker with a larger force factor was found to have quicker convergence in the adaptive algorithm in experiment. In simulations, noise reduction is improved in using an adaptive algorithm by using a secondary loudspeaker with a heavier moving mass. It is predicted that an extra broadband noise reduction of more than 7 dB can be gained using an adaptive filter if the force factor, moving mass and coil inductance of a commercially available loudspeaker are doubled. Methods to increase the force factor beyond those of commercially available loudspeakers are proposed.

A hybrid algorithm for speckle noise reduction of ultrasound images.

PubMed

Singh, Karamjeet; Ranade, Sukhjeet Kaur; Singh, Chandan

2017-09-01

Medical images are contaminated by multiplicative speckle noise which significantly reduce the contrast of ultrasound images and creates a negative effect on various image interpretation tasks. In this paper, we proposed a hybrid denoising approach which collaborate the both local and nonlocal information in an efficient manner. The proposed hybrid algorithm consist of three stages in which at first stage the use of local statistics in the form of guided filter is used to reduce the effect of speckle noise initially. Then, an improved speckle reducing bilateral filter (SRBF) is developed to further reduce the speckle noise from the medical images. Finally, to reconstruct the diffused edges we have used the efficient post-processing technique which jointly considered the advantages of both bilateral and nonlocal mean (NLM) filter for the attenuation of speckle noise efficiently. The performance of proposed hybrid algorithm is evaluated on synthetic, simulated and real ultrasound images. The experiments conducted on various test images demonstrate that our proposed hybrid approach outperforms the various traditional speckle reduction approaches included recently proposed NLM and optimized Bayesian-based NLM. The results of various quantitative, qualitative measures and by visual inspection of denoise synthetic and real ultrasound images demonstrate that the proposed hybrid algorithm have strong denoising capability and able to preserve the fine image details such as edge of a lesion better than previously developed methods for speckle noise reduction. The denoising and edge preserving capability of hybrid algorithm is far better than existing traditional and recently proposed speckle reduction (SR) filters. The success of proposed algorithm would help in building the lay foundation for inventing the hybrid algorithms for denoising of ultrasound images. Copyright © 2017 Elsevier B.V. All rights reserved.

Denoising and 4D visualization of OCT images

PubMed Central

Gargesha, Madhusudhana; Jenkins, Michael W.; Rollins, Andrew M.; Wilson, David L.

2009-01-01

We are using Optical Coherence Tomography (OCT) to image structure and function of the developing embryonic heart in avian models. Fast OCT imaging produces very large 3D (2D + time) and 4D (3D volumes + time) data sets, which greatly challenge ones ability to visualize results. Noise in OCT images poses additional challenges. We created an algorithm with a quick, data set specific optimization for reduction of both shot and speckle noise and applied it to 3D visualization and image segmentation in OCT. When compared to baseline algorithms (median, Wiener, orthogonal wavelet, basic non-orthogonal wavelet), a panel of experts judged the new algorithm to give much improved volume renderings concerning both noise and 3D visualization. Specifically, the algorithm provided a better visualization of the myocardial and endocardial surfaces, and the interaction of the embryonic heart tube with surrounding tissue. Quantitative evaluation using an image quality figure of merit also indicated superiority of the new algorithm. Noise reduction aided semi-automatic 2D image segmentation, as quantitatively evaluated using a contour distance measure with respect to an expert segmented contour. In conclusion, the noise reduction algorithm should be quite useful for visualization and quantitative measurements (e.g., heart volume, stroke volume, contraction velocity, etc.) in OCT embryo images. With its semi-automatic, data set specific optimization, we believe that the algorithm can be applied to OCT images from other applications. PMID:18679509

Novel Signal Noise Reduction Method through Cluster Analysis, Applied to Photoplethysmography.

PubMed

Waugh, William; Allen, John; Wightman, James; Sims, Andrew J; Beale, Thomas A W

2018-01-01

Physiological signals can often become contaminated by noise from a variety of origins. In this paper, an algorithm is described for the reduction of sporadic noise from a continuous periodic signal. The design can be used where a sample of a periodic signal is required, for example, when an average pulse is needed for pulse wave analysis and characterization. The algorithm is based on cluster analysis for selecting similar repetitions or pulses from a periodic single. This method selects individual pulses without noise, returns a clean pulse signal, and terminates when a sufficiently clean and representative signal is received. The algorithm is designed to be sufficiently compact to be implemented on a microcontroller embedded within a medical device. It has been validated through the removal of noise from an exemplar photoplethysmography (PPG) signal, showing increasing benefit as the noise contamination of the signal increases. The algorithm design is generalised to be applicable for a wide range of physiological (physical) signals.

Evaluation of model-based versus non-parametric monaural noise-reduction approaches for hearing aids.

PubMed

Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker

2012-08-01

Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. The perceptual investigation was performed with fourteen hearing-impaired subjects. The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.

Comparing Binaural Pre-processing Strategies III

PubMed Central

Warzybok, Anna; Ernst, Stephan M. A.

2015-01-01

A comprehensive evaluation of eight signal pre-processing strategies, including directional microphones, coherence filters, single-channel noise reduction, binaural beamformers, and their combinations, was undertaken with normal-hearing (NH) and hearing-impaired (HI) listeners. Speech reception thresholds (SRTs) were measured in three noise scenarios (multitalker babble, cafeteria noise, and single competing talker). Predictions of three common instrumental measures were compared with the general perceptual benefit caused by the algorithms. The individual SRTs measured without pre-processing and individual benefits were objectively estimated using the binaural speech intelligibility model. Ten listeners with NH and 12 HI listeners participated. The participants varied in age and pure-tone threshold levels. Although HI listeners required a better signal-to-noise ratio to obtain 50% intelligibility than listeners with NH, no differences in SRT benefit from the different algorithms were found between the two groups. With the exception of single-channel noise reduction, all algorithms showed an improvement in SRT of between 2.1 dB (in cafeteria noise) and 4.8 dB (in single competing talker condition). Model predictions with binaural speech intelligibility model explained 83% of the measured variance of the individual SRTs in the no pre-processing condition. Regarding the benefit from the algorithms, the instrumental measures were not able to predict the perceptual data in all tested noise conditions. The comparable benefit observed for both groups suggests a possible application of noise reduction schemes for listeners with different hearing status. Although the model can predict the individual SRTs without pre-processing, further development is necessary to predict the benefits obtained from the algorithms at an individual level. PMID:26721922

Hearing through the noise: Biologically inspired noise reduction

NASA Astrophysics Data System (ADS)

Lee, Tyler Paul

Vocal communication in the natural world demands that a listener perform a remarkably complicated task in real-time. Vocalizations mix with all other sounds in the environment as they travel to the listener, arriving as a jumbled low-dimensional signal. A listener must then use this signal to extract the structure corresponding to individual sound sources. How this computation is implemented in the brain remains poorly understood, yet an accurate description of such mechanisms would impact a variety of medical and technological applications of sound processing. In this thesis, I describe initial work on how neurons in the secondary auditory cortex of the Zebra Finch extract song from naturalistic background noise. I then build on our understanding of the function of these neurons by creating an algorithm that extracts speech from natural background noise using spectrotemporal modulations. The algorithm, implemented as an artificial neural network, can be flexibly applied to any class of signal or noise and performs better than an optimal frequency-based noise reduction algorithm for a variety of background noises and signal-to-noise ratios. One potential drawback to using spectrotemporal modulations for noise reduction, though, is that analyzing the modulations present in an ongoing sound requires a latency set by the slowest temporal modulation computed. The algorithm avoids this problem by reducing noise predictively, taking advantage of the large amount of temporal structure present in natural sounds. This predictive denoising has ties to recent work suggesting that the auditory system uses attention to focus on predicted regions of spectrotemporal space when performing auditory scene analysis.

Development of adaptive noise reduction filter algorithm for pediatric body images in a multi-detector CT

NASA Astrophysics Data System (ADS)

Nishimaru, Eiji; Ichikawa, Katsuhiro; Okita, Izumi; Ninomiya, Yuuji; Tomoshige, Yukihiro; Kurokawa, Takehiro; Ono, Yutaka; Nakamura, Yuko; Suzuki, Masayuki

2008-03-01

Recently, several kinds of post-processing image filters which reduce the noise of computed tomography (CT) images have been proposed. However, these image filters are mostly for adults. Because these are not very effective in small (< 20 cm) display fields of view (FOV), we cannot use them for pediatric body images (e.g., premature babies and infant children). We have developed a new noise reduction filter algorithm for pediatric body CT images. This algorithm is based on a 3D post-processing in which the output pixel values are calculated by nonlinear interpolation in z-directions on original volumetric-data-sets. This algorithm does not need the in-plane (axial plane) processing, so the spatial resolution does not change. From the phantom studies, our algorithm could reduce SD up to 40% without affecting the spatial resolution of x-y plane and z-axis, and improved the CNR up to 30%. This newly developed filter algorithm will be useful for the diagnosis and radiation dose reduction of the pediatric body CT images.

Denoising of polychromatic CT images based on their own noise properties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Ji Hye; Chang, Yongjin; Ra, Jong Beom, E-mail: jbra@kaist.ac.kr

Purpose: Because of high diagnostic accuracy and fast scan time, computed tomography (CT) has been widely used in various clinical applications. Since the CT scan introduces radiation exposure to patients, however, dose reduction has recently been recognized as an important issue in CT imaging. However, low-dose CT causes an increase of noise in the image and thereby deteriorates the accuracy of diagnosis. In this paper, the authors develop an efficient denoising algorithm for low-dose CT images obtained using a polychromatic x-ray source. The algorithm is based on two steps: (i) estimation of space variant noise statistics, which are uniquely determinedmore » according to the system geometry and scanned object, and (ii) subsequent novel conversion of the estimated noise to Gaussian noise so that an existing high performance Gaussian noise filtering algorithm can be directly applied to CT images with non-Gaussian noise. Methods: For efficient polychromatic CT image denoising, the authors first reconstruct an image with the iterative maximum-likelihood polychromatic algorithm for CT to alleviate the beam-hardening problem. We then estimate the space-variant noise variance distribution on the image domain. Since there are many high performance denoising algorithms available for the Gaussian noise, image denoising can become much more efficient if they can be used. Hence, the authors propose a novel conversion scheme to transform the estimated space-variant noise to near Gaussian noise. In the suggested scheme, the authors first convert the image so that its mean and variance can have a linear relationship, and then produce a Gaussian image via variance stabilizing transform. The authors then apply a block matching 4D algorithm that is optimized for noise reduction of the Gaussian image, and reconvert the result to obtain a final denoised image. To examine the performance of the proposed method, an XCAT phantom simulation and a physical phantom experiment were conducted. Results: Both simulation and experimental results show that, unlike the existing denoising algorithms, the proposed algorithm can effectively reduce the noise over the whole region of CT images while preventing degradation of image resolution. Conclusions: To effectively denoise polychromatic low-dose CT images, a novel denoising algorithm is proposed. Because this algorithm is based on the noise statistics of a reconstructed polychromatic CT image, the spatially varying noise on the image is effectively reduced so that the denoised image will have homogeneous quality over the image domain. Through a simulation and a real experiment, it is verified that the proposed algorithm can deliver considerably better performance compared to the existing denoising algorithms.« less

Noise reduction and image enhancement using a hardware implementation of artificial neural networks

NASA Astrophysics Data System (ADS)

David, Robert; Williams, Erin; de Tremiolles, Ghislain; Tannhof, Pascal

1999-03-01

In this paper, we present a neural based solution developed for noise reduction and image enhancement using the ZISC, an IBM hardware processor which implements the Restricted Coulomb Energy algorithm and the K-Nearest Neighbor algorithm. Artificial neural networks present the advantages of processing time reduction in comparison with classical models, adaptability, and the weighted property of pattern learning. The goal of the developed application is image enhancement in order to restore old movies (noise reduction, focus correction, etc.), to improve digital television images, or to treat images which require adaptive processing (medical images, spatial images, special effects, etc.). Image results show a quantitative improvement over the noisy image as well as the efficiency of this system. Further enhancements are being examined to improve the output of the system.

A wavelet and least square filter based spatial-spectral denoising approach of hyperspectral imagery

NASA Astrophysics Data System (ADS)

Li, Ting; Chen, Xiao-Mei; Chen, Gang; Xue, Bo; Ni, Guo-Qiang

2009-11-01

Noise reduction is a crucial step in hyperspectral imagery pre-processing. Based on sensor characteristics, the noise of hyperspectral imagery represents in both spatial and spectral domain. However, most prevailing denosing techniques process the imagery in only one specific domain, which have not utilized multi-domain nature of hyperspectral imagery. In this paper, a new spatial-spectral noise reduction algorithm is proposed, which is based on wavelet analysis and least squares filtering techniques. First, in the spatial domain, a new stationary wavelet shrinking algorithm with improved threshold function is utilized to adjust the noise level band-by-band. This new algorithm uses BayesShrink for threshold estimation, and amends the traditional soft-threshold function by adding shape tuning parameters. Comparing with soft or hard threshold function, the improved one, which is first-order derivable and has a smooth transitional region between noise and signal, could save more details of image edge and weaken Pseudo-Gibbs. Then, in the spectral domain, cubic Savitzky-Golay filter based on least squares method is used to remove spectral noise and artificial noise that may have been introduced in during the spatial denoising. Appropriately selecting the filter window width according to prior knowledge, this algorithm has effective performance in smoothing the spectral curve. The performance of the new algorithm is experimented on a set of Hyperion imageries acquired in 2007. The result shows that the new spatial-spectral denoising algorithm provides more significant signal-to-noise-ratio improvement than traditional spatial or spectral method, while saves the local spectral absorption features better.

Optimal design of minimum mean-square error noise reduction algorithms using the simulated annealing technique.

PubMed

Bai, Mingsian R; Hsieh, Ping-Ju; Hur, Kur-Nan

2009-02-01

The performance of the minimum mean-square error noise reduction (MMSE-NR) algorithm in conjunction with time-recursive averaging (TRA) for noise estimation is found to be very sensitive to the choice of two recursion parameters. To address this problem in a more systematic manner, this paper proposes an optimization method to efficiently search the optimal parameters of the MMSE-TRA-NR algorithms. The objective function is based on a regression model, whereas the optimization process is carried out with the simulated annealing algorithm that is well suited for problems with many local optima. Another NR algorithm proposed in the paper employs linear prediction coding as a preprocessor for extracting the correlated portion of human speech. Objective and subjective tests were undertaken to compare the optimized MMSE-TRA-NR algorithm with several conventional NR algorithms. The results of subjective tests were processed by using analysis of variance to justify the statistic significance. A post hoc test, Tukey's Honestly Significant Difference, was conducted to further assess the pairwise difference between the NR algorithms.

Objective performance assessment of five computed tomography iterative reconstruction algorithms.

PubMed

Omotayo, Azeez; Elbakri, Idris

2016-11-22

Iterative algorithms are gaining clinical acceptance in CT. We performed objective phantom-based image quality evaluation of five commercial iterative reconstruction algorithms available on four different multi-detector CT (MDCT) scanners at different dose levels as well as the conventional filtered back-projection (FBP) reconstruction. Using the Catphan500 phantom, we evaluated image noise, contrast-to-noise ratio (CNR), modulation transfer function (MTF) and noise-power spectrum (NPS). The algorithms were evaluated over a CTDIvol range of 0.75-18.7 mGy on four major MDCT scanners: GE DiscoveryCT750HD (algorithms: ASIR™ and VEO™); Siemens Somatom Definition AS+ (algorithm: SAFIRE™); Toshiba Aquilion64 (algorithm: AIDR3D™); and Philips Ingenuity iCT256 (algorithm: iDose4™). Images were reconstructed using FBP and the respective iterative algorithms on the four scanners. Use of iterative algorithms decreased image noise and increased CNR, relative to FBP. In the dose range of 1.3-1.5 mGy, noise reduction using iterative algorithms was in the range of 11%-51% on GE DiscoveryCT750HD, 10%-52% on Siemens Somatom Definition AS+, 49%-62% on Toshiba Aquilion64, and 13%-44% on Philips Ingenuity iCT256. The corresponding CNR increase was in the range 11%-105% on GE, 11%-106% on Siemens, 85%-145% on Toshiba and 13%-77% on Philips respectively. Most algorithms did not affect the MTF, except for VEO™ which produced an increase in the limiting resolution of up to 30%. A shift in the peak of the NPS curve towards lower frequencies and a decrease in NPS amplitude were obtained with all iterative algorithms. VEO™ required long reconstruction times, while all other algorithms produced reconstructions in real time. Compared to FBP, iterative algorithms reduced image noise and increased CNR. The iterative algorithms available on different scanners achieved different levels of noise reduction and CNR increase while spatial resolution improvements were obtained only with VEO™. This study is useful in that it provides performance assessment of the iterative algorithms available from several mainstream CT manufacturers.

Objective Measures of Listening Effort: Effects of Background Noise and Noise Reduction

ERIC Educational Resources Information Center

Sarampalis, Anastasios; Kalluri, Sridhar; Edwards, Brent; Hafter, Ervin

2009-01-01

Purpose: This work is aimed at addressing a seeming contradiction related to the use of noise-reduction (NR) algorithms in hearing aids. The problem is that although some listeners claim a subjective improvement from NR, it has not been shown to improve speech intelligibility, often even making it worse. Method: To address this, the hypothesis…

Algorithmic-Reducibility = Renormalization-Group Fixed-Points; ``Noise''-Induced Phase-Transitions (NITs) to Accelerate Algorithmics (``NIT-Picking'') Replacing CRUTCHES!!!: Gauss Modular/Clock-Arithmetic Congruences = Signal X Noise PRODUCTS..

NASA Astrophysics Data System (ADS)

Siegel, J.; Siegel, Edward Carl-Ludwig

2011-03-01

Cook-Levin computational-"complexity"(C-C) algorithmic-equivalence reduction-theorem reducibility equivalence to renormalization-(semi)-group phase-transitions critical-phenomena statistical-physics universality-classes fixed-points, is exploited with Gauss modular/clock-arithmetic/model congruences = signal X noise PRODUCT reinterpretation. Siegel-Baez FUZZYICS=CATEGORYICS(SON of ``TRIZ''): Category-Semantics(C-S) tabular list-format truth-table matrix analytics predicts and implements "noise"-induced phase-transitions (NITs) to accelerate versus to decelerate Harel [Algorithmics(1987)]-Sipser[Intro. Theory Computation(1997) algorithmic C-C: "NIT-picking" to optimize optimization-problems optimally(OOPO). Versus iso-"noise" power-spectrum quantitative-only amplitude/magnitude-only variation stochastic-resonance, this "NIT-picking" is "noise" power-spectrum QUALitative-type variation via quantitative critical-exponents variation. Computer-"science" algorithmic C-C models: Turing-machine, finite-state-models/automata, are identified as early-days once-workable but NOW ONLY LIMITING CRUTCHES IMPEDING latter-days new-insights!!!

Poisson-Gaussian Noise Reduction Using the Hidden Markov Model in Contourlet Domain for Fluorescence Microscopy Images

PubMed Central

Yang, Sejung; Lee, Byung-Uk

2015-01-01

In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach. PMID:26352138

Adaptive noise correction of dual-energy computed tomography images.

PubMed

Maia, Rafael Simon; Jacob, Christian; Hara, Amy K; Silva, Alvin C; Pavlicek, William; Mitchell, J Ross

2016-04-01

Noise reduction in material density images is a necessary preprocessing step for the correct interpretation of dual-energy computed tomography (DECT) images. In this paper we describe a new method based on a local adaptive processing to reduce noise in DECT images An adaptive neighborhood Wiener (ANW) filter was implemented and customized to use local characteristics of material density images. The ANW filter employs a three-level wavelet approach, combined with the application of an anisotropic diffusion filter. Material density images and virtual monochromatic images are noise corrected with two resulting noise maps. The algorithm was applied and quantitatively evaluated in a set of 36 images. From that set of images, three are shown here, and nine more are shown in the online supplementary material. Processed images had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than the raw material density images. The average improvements in SNR and CNR for the material density images were 56.5 and 54.75%, respectively. We developed a new DECT noise reduction algorithm. We demonstrate throughout a series of quantitative analyses that the algorithm improves the quality of material density images and virtual monochromatic images.

Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Choi, Sunghoon; Kim, Hee-Joung

2018-03-01

When processing medical images, image denoising is an important pre-processing step. Various image denoising algorithms have been developed in the past few decades. Recently, image denoising using the deep learning method has shown excellent performance compared to conventional image denoising algorithms. In this study, we introduce an image denoising technique based on a convolutional denoising autoencoder (CDAE) and evaluate clinical applications by comparing existing image denoising algorithms. We train the proposed CDAE model using 3000 chest radiograms training data. To evaluate the performance of the developed CDAE model, we compare it with conventional denoising algorithms including median filter, total variation (TV) minimization, and non-local mean (NLM) algorithms. Furthermore, to verify the clinical effectiveness of the developed denoising model with CDAE, we investigate the performance of the developed denoising algorithm on chest radiograms acquired from real patients. The results demonstrate that the proposed denoising algorithm developed using CDAE achieves a superior noise-reduction effect in chest radiograms compared to TV minimization and NLM algorithms, which are state-of-the-art algorithms for image noise reduction. For example, the peak signal-to-noise ratio and structure similarity index measure of CDAE were at least 10% higher compared to conventional denoising algorithms. In conclusion, the image denoising algorithm developed using CDAE effectively eliminated noise without loss of information on anatomical structures in chest radiograms. It is expected that the proposed denoising algorithm developed using CDAE will be effective for medical images with microscopic anatomical structures, such as terminal bronchioles.

Efficacy and Clinical Utility of a High-Attenuation Object Artifact Reduction Algorithm in Flat-Detector Image Reconstruction Compared With Standard Image Reconstruction.

PubMed

Naehle, Claas P; Hechelhammer, Lukas; Richter, Heiko; Ryffel, Fabian; Wildermuth, Simon; Weber, Johannes

To evaluate the effectiveness and clinical utility of a metal artifact reduction (MAR) image reconstruction algorithm for the reduction of high-attenuation object (HAO)-related image artifacts. Images were quantitatively evaluated for image noise (noiseSD and noiserange) and qualitatively for artifact severity, gray-white-matter delineation, and diagnostic confidence with conventional reconstruction and after applying a MAR algorithm. Metal artifact reduction reduces noiseSD and noiserange (median [interquartile range]) at the level of HAO in 1-cm distance compared with conventional reconstruction (noiseSD: 60.0 [71.4] vs 12.8 [16.1] and noiserange: 262.0 [236.8] vs 72.0 [28.3]; P < 0.0001). Artifact severity (reader 1 [mean ± SD]: 1.1 ± 0.6 vs 2.4 ± 0.5, reader 2: 0.8 ± 0.6 vs 2.0 ± 0.4) at level of HAO and diagnostic confidence (reader 1: 1.6 ± 0.7 vs 2.6 ± 0.5, reader 2: 1.0 ± 0.6 vs 2.3 ± 0.7) significantly improved with MAR (P < 0.0001). Metal artifact reduction did not affect gray-white-matter delineation. Metal artifact reduction effectively reduces image artifacts caused by HAO and significantly improves diagnostic confidence without worsening gray-white-matter delineation.

A multi-band spectral subtraction-based algorithm for real-time noise cancellation applied to gunshot acoustics

NASA Astrophysics Data System (ADS)

Ramos, António L. L.; Holm, Sverre; Gudvangen, Sigmund; Otterlei, Ragnvald

2013-06-01

Acoustical sniper positioning is based on the detection and direction-of-arrival estimation of the shockwave and the muzzle blast acoustical signals. In real-life situations, the detection and direction-of-arrival estimation processes is usually performed under the influence of background noise sources, e.g., vehicles noise, and might result in non-negligible inaccuracies than can affect the system performance and reliability negatively, specially when detecting the muzzle sound under long range distance and absorbing terrains. This paper introduces a multi-band spectral subtraction based algorithm for real-time noise reduction, applied to gunshot acoustical signals. The ballistic shockwave and the muzzle blast signals exhibit distinct frequency contents that are affected differently by additive noise. In most real situations, the noise component is colored and a multi-band spectral subtraction approach for noise reduction contributes to reducing the presence of artifacts in denoised signals. The proposed algorithm is tested using a dataset generated by combining signals from real gunshots and real vehicle noise. The noise component was generated using a steel tracked military tank running on asphalt and includes, therefore, the sound from the vehicle engine, which varies slightly in frequency over time according to the engine's rpm, and the sound from the steel tracks as the vehicle moves.

Robust frequency diversity based algorithm for clutter noise reduction of ultrasonic signals using multiple sub-spectrum phase coherence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gongzhang, R.; Xiao, B.; Lardner, T.

2014-02-18

This paper presents a robust frequency diversity based algorithm for clutter reduction in ultrasonic A-scan waveforms. The performance of conventional spectral-temporal techniques like Split Spectrum Processing (SSP) is highly dependent on the parameter selection, especially when the signal to noise ratio (SNR) is low. Although spatial beamforming offers noise reduction with less sensitivity to parameter variation, phased array techniques are not always available. The proposed algorithm first selects an ascending series of frequency bands. A signal is reconstructed for each selected band in which a defect is present when all frequency components are in uniform sign. Combining all reconstructed signalsmore » through averaging gives a probability profile of potential defect position. To facilitate data collection and validate the proposed algorithm, Full Matrix Capture is applied on the austenitic steel and high nickel alloy (HNA) samples with 5MHz transducer arrays. When processing A-scan signals with unrefined parameters, the proposed algorithm enhances SNR by 20dB for both samples and consequently, defects are more visible in B-scan images created from the large amount of A-scan traces. Importantly, the proposed algorithm is considered robust, while SSP is shown to fail on the austenitic steel data and achieves less SNR enhancement on the HNA data.« less

The Effects of Digital Noise Reduction on the Acceptance of Background Noise

PubMed Central

Mueller, H. Gustav; Weber, Jennifer; Hornsby, Benjamin W. Y.

2006-01-01

Modern hearing aids commonly employ digital noise reduction (DNR) algorithms. The potential benefit of these algorithms is to provide improved speech understanding in noise or, at the least, to provide relaxed listening or increased ease of listening. In this study, 22 adults were fitted with 16-channel wide-dynamic-range compression hearing aids containing DNR processing. The DNR includes both modulation-based and Wiener-filter-type algorithms working simultaneously. Both speech intelligibility and acceptable noise level (ANL) were assessed using the Hearing in Noise Test (HINT) with DNR on and DNR off. The ANL was also assessed without hearing aids. The results showed a significant mean improvement for the ANL (4.2 dB) for the DNR-on condition when compared to DNR-off condition. Moreover, there was a significant correlation between the magnitude of ANL improvement (relative to DNR on) and the DNR-off ANL. There was no significant mean improvement for the HINT for the DNR-on condition, and on an individual basis, the HINT score did not significantly correlate with either aided ANL (DNR on or DNR off). These findings suggest that at least within the constraints of the DNR algorithms and test conditions employed in this study, DNR can significantly improve the clinically measured ANL, which may result in improved ease of listening for speech-in-noise situations. PMID:16959732

Noise reduction algorithm with the soft thresholding based on the Shannon entropy and bone-conduction speech cross- correlation bands.

PubMed

Na, Sung Dae; Wei, Qun; Seong, Ki Woong; Cho, Jin Ho; Kim, Myoung Nam

2018-01-01

The conventional methods of speech enhancement, noise reduction, and voice activity detection are based on the suppression of noise or non-speech components of the target air-conduction signals. However, air-conduced speech is hard to differentiate from babble or white noise signals. To overcome this problem, the proposed algorithm uses the bone-conduction speech signals and soft thresholding based on the Shannon entropy principle and cross-correlation of air- and bone-conduction signals. A new algorithm for speech detection and noise reduction is proposed, which makes use of the Shannon entropy principle and cross-correlation with the bone-conduction speech signals to threshold the wavelet packet coefficients of the noisy speech. The proposed method can be get efficient result by objective quality measure that are PESQ, RMSE, Correlation, SNR. Each threshold is generated by the entropy and cross-correlation approaches in the decomposed bands using the wavelet packet decomposition. As a result, the noise is reduced by the proposed method using the MATLAB simulation. To verify the method feasibility, we compared the air- and bone-conduction speech signals and their spectra by the proposed method. As a result, high performance of the proposed method is confirmed, which makes it quite instrumental to future applications in communication devices, noisy environment, construction, and military operations.

Dual-microphone and binaural noise reduction techniques for improved speech intelligibility by hearing aid users

NASA Astrophysics Data System (ADS)

Yousefian Jazi, Nima

Spatial filtering and directional discrimination has been shown to be an effective pre-processing approach for noise reduction in microphone array systems. In dual-microphone hearing aids, fixed and adaptive beamforming techniques are the most common solutions for enhancing the desired speech and rejecting unwanted signals captured by the microphones. In fact, beamformers are widely utilized in systems where spatial properties of target source (usually in front of the listener) is assumed to be known. In this dissertation, some dual-microphone coherence-based speech enhancement techniques applicable to hearing aids are proposed. All proposed algorithms operate in the frequency domain and (like traditional beamforming techniques) are purely based on the spatial properties of the desired speech source and does not require any knowledge of noise statistics for calculating the noise reduction filter. This benefit gives our algorithms the ability to address adverse noise conditions, such as situations where interfering talker(s) speaks simultaneously with the target speaker. In such cases, the (adaptive) beamformers lose their effectiveness in suppressing interference, since the noise channel (reference) cannot be built and updated accordingly. This difference is the main advantage of the proposed techniques in the dissertation over traditional adaptive beamformers. Furthermore, since the suggested algorithms are independent of noise estimation, they offer significant improvement in scenarios that the power level of interfering sources are much more than that of target speech. The dissertation also shows the premise behind the proposed algorithms can be extended and employed to binaural hearing aids. The main purpose of the investigated techniques is to enhance the intelligibility level of speech, measured through subjective listening tests with normal hearing and cochlear implant listeners. However, the improvement in quality of the output speech achieved by the algorithms are also presented to show that the proposed methods can be potential candidates for future use in commercial hearing aids and cochlear implant devices.

Head-to-head comparison of adaptive statistical and model-based iterative reconstruction algorithms for submillisievert coronary CT angiography.

PubMed

Benz, Dominik C; Fuchs, Tobias A; Gräni, Christoph; Studer Bruengger, Annina A; Clerc, Olivier F; Mikulicic, Fran; Messerli, Michael; Stehli, Julia; Possner, Mathias; Pazhenkottil, Aju P; Gaemperli, Oliver; Kaufmann, Philipp A; Buechel, Ronny R

2018-02-01

Iterative reconstruction (IR) algorithms allow for a significant reduction in radiation dose of coronary computed tomography angiography (CCTA). We performed a head-to-head comparison of adaptive statistical IR (ASiR) and model-based IR (MBIR) algorithms to assess their impact on quantitative image parameters and diagnostic accuracy for submillisievert CCTA. CCTA datasets of 91 patients were reconstructed using filtered back projection (FBP), increasing contributions of ASiR (20, 40, 60, 80, and 100%), and MBIR. Signal and noise were measured in the aortic root to calculate signal-to-noise ratio (SNR). In a subgroup of 36 patients, diagnostic accuracy of ASiR 40%, ASiR 100%, and MBIR for diagnosis of coronary artery disease (CAD) was compared with invasive coronary angiography. Median radiation dose was 0.21 mSv for CCTA. While increasing levels of ASiR gradually reduced image noise compared with FBP (up to - 48%, P < 0.001), MBIR provided largest noise reduction (-79% compared with FBP) outperforming ASiR (-59% compared with ASiR 100%; P < 0.001). Increased noise and lower SNR with ASiR 40% and ASiR 100% resulted in substantially lower diagnostic accuracy to detect CAD as diagnosed by invasive coronary angiography compared with MBIR: sensitivity and specificity were 100 and 37%, 100 and 57%, and 100 and 74% for ASiR 40%, ASiR 100%, and MBIR, respectively. MBIR offers substantial noise reduction with increased SNR, paving the way for implementation of submillisievert CCTA protocols in clinical routine. In contrast, inferior noise reduction by ASiR negatively affects diagnostic accuracy of submillisievert CCTA for CAD detection. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com.

Comparison of single-microphone noise reduction schemes: can hearing impaired listeners tell the difference?

PubMed

Huber, Rainer; Bisitz, Thomas; Gerkmann, Timo; Kiessling, Jürgen; Meister, Hartmut; Kollmeier, Birger

2018-06-01

The perceived qualities of nine different single-microphone noise reduction (SMNR) algorithms were to be evaluated and compared in subjective listening tests with normal hearing and hearing impaired (HI) listeners. Speech samples added with traffic noise or with party noise were processed by the SMNR algorithms. Subjects rated the amount of speech distortions, intrusiveness of background noise, listening effort and overall quality, using a simplified MUSHRA (ITU-R, 2003 ) assessment method. 18 normal hearing and 18 moderately HI subjects participated in the study. Significant differences between the rating behaviours of the two subject groups were observed: While normal hearing subjects clearly differentiated between different SMNR algorithms, HI subjects rated all processed signals very similarly. Moreover, HI subjects rated speech distortions of the unprocessed, noisier signals as being more severe than the distortions of the processed signals, in contrast to normal hearing subjects. It seems harder for HI listeners to distinguish between additive noise and speech distortions or/and they might have a different understanding of the term "speech distortion" than normal hearing listeners have. The findings confirm that the evaluation of SMNR schemes for hearing aids should always involve HI listeners.

SU-E-I-04: Improving CT Quality for Radiation Therapy of Patients with High Body Mass Index Using Iterative Reconstruction Algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Noid, G; Tai, A; Li, X

2015-06-15

Purpose: Iterative reconstruction (IR) algorithms are developed to improve CT image quality (IQ) by reducing noise without diminishing spatial resolution or contrast. The CT IQ for patients with a high Body Mass Index (BMI) can suffer from increased noise due to photon starvation. The purpose of this study is to investigate and to quantify the IQ enhancement for high BMI patients through the application of IR algorithms. Methods: CT raw data collected for 6 radiotherapy (RT) patients with BMI, greater than or equal to 30 were retrospectively analyzed. All CT data were acquired using a CT scanner (Somaton Definition ASmore » Open, Siemens) installed in a linac room (CT-on-rails) using standard imaging protocols. The CT data were reconstructed using the Sinogram Affirmed Iterative Reconstruction (SAFIRE) and Filtered Back Projection (FBP) methods. IQ metrics of the obtained CTs were compared and correlated with patient depth and BMI. The patient depth was defined as the largest distance from anterior to posterior along the bilateral symmetry axis. Results: IR techniques are demonstrated to preserve contrast and reduce noise in comparison to traditional FBP. Driven by the reduction in noise, the contrast to noise ratio is roughly doubled by adopting the highest SAFIRE strength. A significant correlation was observed between patient depth and IR noise reduction through Pearson’s correlation test (R = 0.9429/P = 0.0167). The mean patient depth was 30.4 cm and the average relative noise reduction for the strongest iterative reconstruction was 55%. Conclusion: The IR techniques produce a measureable enhancement to CT IQ by reducing the noise. Dramatic noise reduction is evident for the high BMI patients. The improved CT IQ enables more accurate delineation of tumors and organs at risk and more accuarte dose calculations for RT planning and delivery guidance. Supported by Siemens.« less

Adaptive spatial filtering improves speech reception in noise while preserving binaural cues.

PubMed

Bissmeyer, Susan R S; Goldsworthy, Raymond L

2017-09-01

Hearing loss greatly reduces an individual's ability to comprehend speech in the presence of background noise. Over the past decades, numerous signal-processing algorithms have been developed to improve speech reception in these situations for cochlear implant and hearing aid users. One challenge is to reduce background noise while not introducing interaural distortion that would degrade binaural hearing. The present study evaluates a noise reduction algorithm, referred to as binaural Fennec, that was designed to improve speech reception in background noise while preserving binaural cues. Speech reception thresholds were measured for normal-hearing listeners in a simulated environment with target speech generated in front of the listener and background noise originating 90° to the right of the listener. Lateralization thresholds were also measured in the presence of background noise. These measures were conducted in anechoic and reverberant environments. Results indicate that the algorithm improved speech reception thresholds, even in highly reverberant environments. Results indicate that the algorithm also improved lateralization thresholds for the anechoic environment while not affecting lateralization thresholds for the reverberant environments. These results provide clear evidence that this algorithm can improve speech reception in background noise while preserving binaural cues used to lateralize sound.

Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique.

PubMed

Choi, Hyun Ho; Lee, Ju Hwan; Kim, Sung Min; Park, Sung Yun

2015-01-01

Here, the speckle noise in ultrasonic images is removed using an image fusion-based denoising method. To optimize the denoising performance, each discrete wavelet transform (DWT) and filtering technique was analyzed and compared. In addition, the performances were compared in order to derive the optimal input conditions. To evaluate the speckle noise removal performance, an image fusion algorithm was applied to the ultrasound images, and comparatively analyzed with the original image without the algorithm. As a result, applying DWT and filtering techniques caused information loss and noise characteristics, and did not represent the most significant noise reduction performance. Conversely, an image fusion method applying SRAD-original conditions preserved the key information in the original image, and the speckle noise was removed. Based on such characteristics, the input conditions of SRAD-original had the best denoising performance with the ultrasound images. From this study, the best denoising technique proposed based on the results was confirmed to have a high potential for clinical application.

Speckle Noise Reduction in Optical Coherence Tomography Using Two-dimensional Curvelet-based Dictionary Learning.

PubMed

Esmaeili, Mahdad; Dehnavi, Alireza Mehri; Rabbani, Hossein; Hajizadeh, Fedra

2017-01-01

The process of interpretation of high-speed optical coherence tomography (OCT) images is restricted due to the large speckle noise. To address this problem, this paper proposes a new method using two-dimensional (2D) curvelet-based K-SVD algorithm for speckle noise reduction and contrast enhancement of intra-retinal layers of 2D spectral-domain OCT images. For this purpose, we take curvelet transform of the noisy image. In the next step, noisy sub-bands of different scales and rotations are separately thresholded with an adaptive data-driven thresholding method, then, each thresholded sub-band is denoised based on K-SVD dictionary learning with a variable size initial dictionary dependent on the size of curvelet coefficients' matrix in each sub-band. We also modify each coefficient matrix to enhance intra-retinal layers, with noise suppression at the same time. We demonstrate the ability of the proposed algorithm in speckle noise reduction of 100 publically available OCT B-scans with and without non-neovascular age-related macular degeneration (AMD), and improvement of contrast-to-noise ratio from 1.27 to 5.12 and mean-to-standard deviation ratio from 3.20 to 14.41 are obtained.

Comparison study of noise reduction algorithms in dual energy chest digital tomosynthesis

NASA Astrophysics Data System (ADS)

Lee, D.; Kim, Y.-S.; Choi, S.; Lee, H.; Choi, S.; Kim, H.-J.

2018-04-01

Dual energy chest digital tomosynthesis (CDT) is a recently developed medical technique that takes advantage of both tomosynthesis and dual energy X-ray images. However, quantum noise, which occurs in dual energy X-ray images, strongly interferes with diagnosis in various clinical situations. Therefore, noise reduction is necessary in dual energy CDT. In this study, noise-compensating algorithms, including a simple smoothing of high-energy images (SSH) and anti-correlated noise reduction (ACNR), were evaluated in a CDT system. We used a newly developed prototype CDT system and anthropomorphic chest phantom for experimental studies. The resulting images demonstrated that dual energy CDT can selectively image anatomical structures, such as bone and soft tissue. Among the resulting images, those acquired with ACNR showed the best image quality. Both coefficient of variation and contrast to noise ratio (CNR) were the highest in ACNR among the three different dual energy techniques, and the CNR of bone was significantly improved compared to the reconstructed images acquired at a single energy. This study demonstrated the clinical value of dual energy CDT and quantitatively showed that ACNR is the most suitable among the three developed dual energy techniques, including standard log subtraction, SSH, and ACNR.

An evaluation of noise reduction algorithms for particle-based fluid simulations in multi-scale applications

NASA Astrophysics Data System (ADS)

Zimoń, M. J.; Prosser, R.; Emerson, D. R.; Borg, M. K.; Bray, D. J.; Grinberg, L.; Reese, J. M.

2016-11-01

Filtering of particle-based simulation data can lead to reduced computational costs and enable more efficient information transfer in multi-scale modelling. This paper compares the effectiveness of various signal processing methods to reduce numerical noise and capture the structures of nano-flow systems. In addition, a novel combination of these algorithms is introduced, showing the potential of hybrid strategies to improve further the de-noising performance for time-dependent measurements. The methods were tested on velocity and density fields, obtained from simulations performed with molecular dynamics and dissipative particle dynamics. Comparisons between the algorithms are given in terms of performance, quality of the results and sensitivity to the choice of input parameters. The results provide useful insights on strategies for the analysis of particle-based data and the reduction of computational costs in obtaining ensemble solutions.

Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

PubMed

Xia, Youshen; Wang, Jun

2015-07-01

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Active noise control in a duct to cancel broadband noise

NASA Astrophysics Data System (ADS)

Chen, Kuan-Chun; Chang, Cheng-Yuan; Kuo, Sen M.

2017-09-01

The paper presents cancelling duct noises by using the active noise control (ANC) techniques. We use the single channel feed forward algorithm with feedback neutralization to realize ANC. Several kinds of ducts noises including tonal noises, sweep tonal signals, and white noise had investigated. Experimental results show that the proposed ANC system can cancel these noises in a PVC duct very well. The noise reduction of white noise can be up to 20 dB.

3-D CSEM data inversion algorithm based on simultaneously active multiple transmitters concept

NASA Astrophysics Data System (ADS)

Dehiya, Rahul; Singh, Arun; Gupta, Pravin Kumar; Israil, Mohammad

2017-05-01

We present an algorithm for efficient 3-D inversion of marine controlled-source electromagnetic data. The efficiency is achieved by exploiting the redundancy in data. The data redundancy is reduced by compressing the data through stacking of the response of transmitters which are in close proximity. This stacking is equivalent to synthesizing the data as if the multiple transmitters are simultaneously active. The redundancy in data, arising due to close transmitter spacing, has been studied through singular value analysis of the Jacobian formed in 1-D inversion. This study reveals that the transmitter spacing of 100 m, typically used in marine data acquisition, does result in redundancy in the data. In the proposed algorithm, the data are compressed through stacking which leads to both computational advantage and reduction in noise. The performance of the algorithm for noisy data is demonstrated through the studies on two types of noise, viz., uncorrelated additive noise and correlated non-additive noise. It is observed that in case of uncorrelated additive noise, up to a moderately high (10 percent) noise level the algorithm addresses the noise as effectively as the traditional full data inversion. However, when the noise level in the data is high (20 percent), the algorithm outperforms the traditional full data inversion in terms of data misfit. Similar results are obtained in case of correlated non-additive noise and the algorithm performs better if the level of noise is high. The inversion results of a real field data set are also presented to demonstrate the robustness of the algorithm. The significant computational advantage in all cases presented makes this algorithm a better choice.

System identification and model reduction using modulating function techniques

NASA Technical Reports Server (NTRS)

Shen, Yan

1993-01-01

Weighted least squares (WLS) and adaptive weighted least squares (AWLS) algorithms are initiated for continuous-time system identification using Fourier type modulating function techniques. Two stochastic signal models are examined using the mean square properties of the stochastic calculus: an equation error signal model with white noise residuals, and a more realistic white measurement noise signal model. The covariance matrices in each model are shown to be banded and sparse, and a joint likelihood cost function is developed which links the real and imaginary parts of the modulated quantities. The superior performance of above algorithms is demonstrated by comparing them with the LS/MFT and popular predicting error method (PEM) through 200 Monte Carlo simulations. A model reduction problem is formulated with the AWLS/MFT algorithm, and comparisons are made via six examples with a variety of model reduction techniques, including the well-known balanced realization method. Here the AWLS/MFT algorithm manifests higher accuracy in almost all cases, and exhibits its unique flexibility and versatility. Armed with this model reduction, the AWLS/MFT algorithm is extended into MIMO transfer function system identification problems. The impact due to the discrepancy in bandwidths and gains among subsystem is explored through five examples. Finally, as a comprehensive application, the stability derivatives of the longitudinal and lateral dynamics of an F-18 aircraft are identified using physical flight data provided by NASA. A pole-constrained SIMO and MIMO AWLS/MFT algorithm is devised and analyzed. Monte Carlo simulations illustrate its high-noise rejecting properties. Utilizing the flight data, comparisons among different MFT algorithms are tabulated and the AWLS is found to be strongly favored in almost all facets.

A simulation study of harmonics regeneration in noise reduction for electric and acoustic stimulation.

PubMed

Hu, Yi

2010-05-01

Recent research results show that combined electric and acoustic stimulation (EAS) significantly improves speech recognition in noise, and it is generally established that access to the improved F0 representation of target speech, along with the glimpse cues, provide the EAS benefits. Under noisy listening conditions, noise signals degrade these important cues by introducing undesired temporal-frequency components and corrupting harmonics structure. In this study, the potential of combining noise reduction and harmonics regeneration techniques was investigated to further improve speech intelligibility in noise by providing improved beneficial cues for EAS. Three hypotheses were tested: (1) noise reduction methods can improve speech intelligibility in noise for EAS; (2) harmonics regeneration after noise reduction can further improve speech intelligibility in noise for EAS; and (3) harmonics sideband constraints in frequency domain (or equivalently, amplitude modulation in temporal domain), even deterministic ones, can provide additional benefits. Test results demonstrate that combining noise reduction and harmonics regeneration can significantly improve speech recognition in noise for EAS, and it is also beneficial to preserve the harmonics sidebands under adverse listening conditions. This finding warrants further work into the development of algorithms that regenerate harmonics and the related sidebands for EAS processing under noisy conditions.

Combined iterative reconstruction and image-domain decomposition for dual energy CT using total-variation regularization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dong, Xue; Niu, Tianye; Zhu, Lei, E-mail: leizhu@gatech.edu

2014-05-15

Purpose: Dual-energy CT (DECT) is being increasingly used for its capability of material decomposition and energy-selective imaging. A generic problem of DECT, however, is that the decomposition process is unstable in the sense that the relative magnitude of decomposed signals is reduced due to signal cancellation while the image noise is accumulating from the two CT images of independent scans. Direct image decomposition, therefore, leads to severe degradation of signal-to-noise ratio on the resultant images. Existing noise suppression techniques are typically implemented in DECT with the procedures of reconstruction and decomposition performed independently, which do not explore the statistical propertiesmore » of decomposed images during the reconstruction for noise reduction. In this work, the authors propose an iterative approach that combines the reconstruction and the signal decomposition procedures to minimize the DECT image noise without noticeable loss of resolution. Methods: The proposed algorithm is formulated as an optimization problem, which balances the data fidelity and total variation of decomposed images in one framework, and the decomposition step is carried out iteratively together with reconstruction. The noise in the CT images from the proposed algorithm becomes well correlated even though the noise of the raw projections is independent on the two CT scans. Due to this feature, the proposed algorithm avoids noise accumulation during the decomposition process. The authors evaluate the method performance on noise suppression and spatial resolution using phantom studies and compare the algorithm with conventional denoising approaches as well as combined iterative reconstruction methods with different forms of regularization. Results: On the Catphan©600 phantom, the proposed method outperforms the existing denoising methods on preserving spatial resolution at the same level of noise suppression, i.e., a reduction of noise standard deviation by one order of magnitude. This improvement is mainly attributed to the high noise correlation in the CT images reconstructed by the proposed algorithm. Iterative reconstruction using different regularization, including quadratic orq-generalized Gaussian Markov random field regularization, achieves similar noise suppression from high noise correlation. However, the proposed TV regularization obtains a better edge preserving performance. Studies of electron density measurement also show that our method reduces the average estimation error from 9.5% to 7.1%. On the anthropomorphic head phantom, the proposed method suppresses the noise standard deviation of the decomposed images by a factor of ∼14 without blurring the fine structures in the sinus area. Conclusions: The authors propose a practical method for DECT imaging reconstruction, which combines the image reconstruction and material decomposition into one optimization framework. Compared to the existing approaches, our method achieves a superior performance on DECT imaging with respect to decomposition accuracy, noise reduction, and spatial resolution.« less

Hard decoding algorithm for optimizing thresholds under general Markovian noise

NASA Astrophysics Data System (ADS)

Chamberland, Christopher; Wallman, Joel; Beale, Stefanie; Laflamme, Raymond

2017-04-01

Quantum error correction is instrumental in protecting quantum systems from noise in quantum computing and communication settings. Pauli channels can be efficiently simulated and threshold values for Pauli error rates under a variety of error-correcting codes have been obtained. However, realistic quantum systems can undergo noise processes that differ significantly from Pauli noise. In this paper, we present an efficient hard decoding algorithm for optimizing thresholds and lowering failure rates of an error-correcting code under general completely positive and trace-preserving (i.e., Markovian) noise. We use our hard decoding algorithm to study the performance of several error-correcting codes under various non-Pauli noise models by computing threshold values and failure rates for these codes. We compare the performance of our hard decoding algorithm to decoders optimized for depolarizing noise and show improvements in thresholds and reductions in failure rates by several orders of magnitude. Our hard decoding algorithm can also be adapted to take advantage of a code's non-Pauli transversal gates to further suppress noise. For example, we show that using the transversal gates of the 5-qubit code allows arbitrary rotations around certain axes to be perfectly corrected. Furthermore, we show that Pauli twirling can increase or decrease the threshold depending upon the code properties. Lastly, we show that even if the physical noise model differs slightly from the hypothesized noise model used to determine an optimized decoder, failure rates can still be reduced by applying our hard decoding algorithm.

Speech Enhancement of Mobile Devices Based on the Integration of a Dual Microphone Array and a Background Noise Elimination Algorithm.

PubMed

Chen, Yung-Yue

2018-05-08

Mobile devices are often used in our daily lives for the purposes of speech and communication. The speech quality of mobile devices is always degraded due to the environmental noises surrounding mobile device users. Regretfully, an effective background noise reduction solution cannot easily be developed for this speech enhancement problem. Due to these depicted reasons, a methodology is systematically proposed to eliminate the effects of background noises for the speech communication of mobile devices. This methodology integrates a dual microphone array with a background noise elimination algorithm. The proposed background noise elimination algorithm includes a whitening process, a speech modelling method and an H ₂ estimator. Due to the adoption of the dual microphone array, a low-cost design can be obtained for the speech enhancement of mobile devices. Practical tests have proven that this proposed method is immune to random background noises, and noiseless speech can be obtained after executing this denoise process.

Digital Noise Reduction: An Overview

PubMed Central

Bentler, Ruth; Chiou, Li-Kuei

2006-01-01

Digital noise reduction schemes are being used in most hearing aids currently marketed. Unlike the earlier analog schemes, these manufacturer-specific algorithms are developed to acoustically analyze the incoming signal and alter the gain/output characteristics according to their predetermined rules. Although most are modulation-based schemes (ie, differentiating speech from noise based on temporal characteristics), spectral subtraction techniques are being applied as well. The purpose of this article is to overview these schemes in terms of their differences and similarities. PMID:16959731

Speech enhancement based on modified phase-opponency detectors

NASA Astrophysics Data System (ADS)

Deshmukh, Om D.; Espy-Wilson, Carol Y.

2005-09-01

A speech enhancement algorithm based on a neural model was presented by Deshmukh et al., [149th meeting of the Acoustical Society America, 2005]. The algorithm consists of a bank of Modified Phase Opponency (MPO) filter pairs tuned to different center frequencies. This algorithm is able to enhance salient spectral features in speech signals even at low signal-to-noise ratios. However, the algorithm introduces musical noise and sometimes misses a spectral peak that is close in frequency to a stronger spectral peak. Refinement in the design of the MPO filters was recently made that takes advantage of the falling spectrum of the speech signal in sonorant regions. The modified set of filters leads to better separation of the noise and speech signals, and more accurate enhancement of spectral peaks. The improvements also lead to a significant reduction in musical noise. Continuity algorithms based on the properties of speech signals are used to further reduce the musical noise effect. The efficiency of the proposed method in enhancing the speech signal when the level of the background noise is fluctuating will be demonstrated. The performance of the improved speech enhancement method will be compared with various spectral subtraction-based methods. [Work supported by NSF BCS0236707.

Computationally efficient video restoration for Nyquist sampled imaging sensors combining an affine-motion-based temporal Kalman filter and adaptive Wiener filter.

PubMed

Rucci, Michael; Hardie, Russell C; Barnard, Kenneth J

2014-05-01

In this paper, we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive Wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multidelay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatiotemporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.

Effect of a Noise-Optimized Second-Generation Monoenergetic Algorithm on Image Noise and Conspicuity of Hypervascular Liver Tumors: An In Vitro and In Vivo Study.

PubMed

Marin, Daniele; Ramirez-Giraldo, Juan Carlos; Gupta, Sonia; Fu, Wanyi; Stinnett, Sandra S; Mileto, Achille; Bellini, Davide; Patel, Bhavik; Samei, Ehsan; Nelson, Rendon C

2016-06-01

The purpose of this study is to investigate whether the reduction in noise using a second-generation monoenergetic algorithm can improve the conspicuity of hypervascular liver tumors on dual-energy CT (DECT) images of the liver. An anthropomorphic liver phantom in three body sizes and iodine-containing inserts simulating hypervascular lesions was imaged with DECT and single-energy CT at various energy levels (80-140 kV). In addition, a retrospective clinical study was performed in 31 patients with 66 hypervascular liver tumors who underwent DECT during the late hepatic arterial phase. Datasets at energy levels ranging from 40 to 80 keV were reconstructed using first- and second-generation monoenergetic algorithms. Noise, tumor-to-liver contrast-to-noise ratio (CNR), and CNR with a noise constraint (CNRNC) set with a maximum noise increase of 50% were calculated and compared among the different reconstructed datasets. The maximum CNR for the second-generation monoenergetic algorithm, which was attained at 40 keV in both phantom and clinical datasets, was statistically significantly higher than the maximum CNR for the first-generation monoenergetic algorithm (p < 0.001) or single-energy CT acquisitions across a wide range of kilovoltage values. With the second-generation monoenergetic algorithm, the optimal CNRNC occurred at 55 keV, corresponding to lower energy levels compared with first-generation algorithm (predominantly at 70 keV). Patient body size did not substantially affect the selection of the optimal energy level to attain maximal CNR and CNRNC using the second-generation monoenergetic algorithm. A noise-optimized second-generation monoenergetic algorithm significantly improves the conspicuity of hypervascular liver tumors.

Metal artefact reduction with cone beam CT: an in vitro study

PubMed Central

Bechara, BB; Moore, WS; McMahan, CA; Noujeim, M

2012-01-01

Background Metal in a patient's mouth has been shown to cause artefacts that can interfere with the diagnostic quality of cone beam CT. Recently, a manufacturer has made an algorithm and software available which reduces metal streak artefact (Picasso Master 3D® machine; Vatech, Hwaseong, Republic of Korea). Objectives The purpose of this investigation was to determine whether or not the metal artefact reduction algorithm was effective and enhanced the contrast-to-noise ratio. Methods A phantom was constructed incorporating three metallic beads and three epoxy resin-based bone substitutes to simulate bone next to metal. The phantom was placed in the centre of the field of view and at the periphery. 10 data sets were acquired at 50–90 kVp. The images obtained were analysed using a public domain software ImageJ (NIH Image, Bethesda, MD). Profile lines were used to evaluate grey level changes and area histograms were used to evaluate contrast. The contrast-to-noise ratio was calculated. Results The metal artefact reduction option reduced grey value variation and increased the contrast-to-noise ratio. The grey value varied least when the phantom was in the middle of the volume and the metal artefact reduction was activated. The image quality improved as the peak kilovoltage increased. Conclusion Better images of a phantom were obtained when the metal artefact reduction algorithm was used. PMID:22241878

Environment-specific noise suppression for improved speech intelligibility by cochlear implant users.

PubMed

Hu, Yi; Loizou, Philipos C

2010-06-01

Attempts to develop noise-suppression algorithms that can significantly improve speech intelligibility in noise by cochlear implant (CI) users have met with limited success. This is partly because algorithms were sought that would work equally well in all listening situations. Accomplishing this has been quite challenging given the variability in the temporal/spectral characteristics of real-world maskers. A different approach is taken in the present study focused on the development of environment-specific noise suppression algorithms. The proposed algorithm selects a subset of the envelope amplitudes for stimulation based on the signal-to-noise ratio (SNR) of each channel. Binary classifiers, trained using data collected from a particular noisy environment, are first used to classify the mixture envelopes of each channel as either target-dominated (SNR>or=0 dB) or masker-dominated (SNR<0 dB). Only target-dominated channels are subsequently selected for stimulation. Results with CI listeners indicated substantial improvements (by nearly 44 percentage points at 5 dB SNR) in intelligibility with the proposed algorithm when tested with sentences embedded in three real-world maskers. The present study demonstrated that the environment-specific approach to noise reduction has the potential to restore speech intelligibility in noise to a level near to that attained in quiet.

The Influence of Noise Reduction on Speech Intelligibility, Response Times to Speech, and Perceived Listening Effort in Normal-Hearing Listeners.

PubMed

van den Tillaart-Haverkate, Maj; de Ronde-Brons, Inge; Dreschler, Wouter A; Houben, Rolph

2017-01-01

Single-microphone noise reduction leads to subjective benefit, but not to objective improvements in speech intelligibility. We investigated whether response times (RTs) provide an objective measure of the benefit of noise reduction and whether the effect of noise reduction is reflected in rated listening effort. Twelve normal-hearing participants listened to digit triplets that were either unprocessed or processed with one of two noise-reduction algorithms: an ideal binary mask (IBM) and a more realistic minimum mean square error estimator (MMSE). For each of these three processing conditions, we measured (a) speech intelligibility, (b) RTs on two different tasks (identification of the last digit and arithmetic summation of the first and last digit), and (c) subjective listening effort ratings. All measurements were performed at four signal-to-noise ratios (SNRs): -5, 0, +5, and +∞ dB. Speech intelligibility was high (>97% correct) for all conditions. A significant decrease in response time, relative to the unprocessed condition, was found for both IBM and MMSE for the arithmetic but not the identification task. Listening effort ratings were significantly lower for IBM than for MMSE and unprocessed speech in noise. We conclude that RT for an arithmetic task can provide an objective measure of the benefit of noise reduction. For young normal-hearing listeners, both ideal and realistic noise reduction can reduce RTs at SNRs where speech intelligibility is close to 100%. Ideal noise reduction can also reduce perceived listening effort.

Method for simulating dose reduction in digital mammography using the Anscombe transformation.

PubMed

Borges, Lucas R; Oliveira, Helder C R de; Nunes, Polyana F; Bakic, Predrag R; Maidment, Andrew D A; Vieira, Marcelo A C

2016-06-01

This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose. The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtained by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose. The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%. A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions.

Effects of noise and working memory capacity on memory processing of speech for hearing-aid users.

PubMed

Ng, Elaine Hoi Ning; Rudner, Mary; Lunner, Thomas; Pedersen, Michael Syskind; Rönnberg, Jerker

2013-07-01

It has been shown that noise reduction algorithms can reduce the negative effects of noise on memory processing in persons with normal hearing. The objective of the present study was to investigate whether a similar effect can be obtained for persons with hearing impairment and whether such an effect is dependent on individual differences in working memory capacity. A sentence-final word identification and recall (SWIR) test was conducted in two noise backgrounds with and without noise reduction as well as in quiet. Working memory capacity was measured using a reading span (RS) test. Twenty-six experienced hearing-aid users with moderate to moderately severe sensorineural hearing loss. Noise impaired recall performance. Competing speech disrupted memory performance more than speech-shaped noise. For late list items the disruptive effect of the competing speech background was virtually cancelled out by noise reduction for persons with high working memory capacity. Noise reduction can reduce the adverse effect of noise on memory for speech for persons with good working memory capacity. We argue that the mechanism behind this is faster word identification that enhances encoding into working memory.

Ensemble empirical mode decomposition based fluorescence spectral noise reduction for low concentration PAHs

NASA Astrophysics Data System (ADS)

Wang, Shu-tao; Yang, Xue-ying; Kong, De-ming; Wang, Yu-tian

2017-11-01

A new noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed to improve the detection effect for fluorescence spectra. Polycyclic aromatic hydrocarbons (PAHs) pollutants, as a kind of important current environmental pollution source, are highly oncogenic. Using the fluorescence spectroscopy method, the PAHs pollutants can be detected. However, instrument will produce noise in the experiment. Weak fluorescent signals can be affected by noise, so we propose a way to denoise and improve the detection effect. Firstly, we use fluorescence spectrometer to detect PAHs to obtain fluorescence spectra. Subsequently, noises are reduced by EEMD algorithm. Finally, the experiment results show the proposed method is feasible.

TH-A-18C-03: Noise Correlation in CBCT Projection Data and Its Application for Noise Reduction in Low-Dose CBCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

ZHANG, H; Huang, J; Ma, J

2014-06-15

Purpose: To study the noise correlation properties of cone-beam CT (CBCT) projection data and to incorporate the noise correlation information to a statistics-based projection restoration algorithm for noise reduction in low-dose CBCT. Methods: In this study, we systematically investigated the noise correlation properties among detector bins of CBCT projection data by analyzing repeated projection measurements. The measurements were performed on a TrueBeam on-board CBCT imaging system with a 4030CB flat panel detector. An anthropomorphic male pelvis phantom was used to acquire 500 repeated projection data at six different dose levels from 0.1 mAs to 1.6 mAs per projection at threemore » fixed angles. To minimize the influence of the lag effect, lag correction was performed on the consecutively acquired projection data. The noise correlation coefficient between detector bin pairs was calculated from the corrected projection data. The noise correlation among CBCT projection data was then incorporated into the covariance matrix of the penalized weighted least-squares (PWLS) criterion for noise reduction of low-dose CBCT. Results: The analyses of the repeated measurements show that noise correlation coefficients are non-zero between the nearest neighboring bins of CBCT projection data. The average noise correlation coefficients for the first- and second- order neighbors are about 0.20 and 0.06, respectively. The noise correlation coefficients are independent of the dose level. Reconstruction of the pelvis phantom shows that the PWLS criterion with consideration of noise correlation (PWLS-Cor) results in a lower noise level as compared to the PWLS criterion without considering the noise correlation (PWLS-Dia) at the matched resolution. Conclusion: Noise is correlated among nearest neighboring detector bins of CBCT projection data. An accurate noise model of CBCT projection data can improve the performance of the statistics-based projection restoration algorithm for low-dose CBCT.« less

Active structural acoustic control of helicopter interior multifrequency noise using input-output-based hybrid control

NASA Astrophysics Data System (ADS)

Ma, Xunjun; Lu, Yang; Wang, Fengjiao

2017-09-01

This paper presents the recent advances in reduction of multifrequency noise inside helicopter cabin using an active structural acoustic control system, which is based on active gearbox struts technical approach. To attenuate the multifrequency gearbox vibrations and resulting noise, a new scheme of discrete model predictive sliding mode control has been proposed based on controlled auto-regressive moving average model. Its implementation only needs input/output data, hence a broader frequency range of controlled system is modelled and the burden on the state observer design is released. Furthermore, a new iteration form of the algorithm is designed, improving the developing efficiency and run speed. To verify the algorithm's effectiveness and self-adaptability, experiments of real-time active control are performed on a newly developed helicopter model system. The helicopter model can generate gear meshing vibration/noise similar to a real helicopter with specially designed gearbox and active struts. The algorithm's control abilities are sufficiently checked by single-input single-output and multiple-input multiple-output experiments via different feedback strategies progressively: (1) control gear meshing noise through attenuating vibrations at the key points on the transmission path, (2) directly control the gear meshing noise in the cabin using the actuators. Results confirm that the active control system is practical for cancelling multifrequency helicopter interior noise, which also weakens the frequency-modulation of the tones. For many cases, the attenuations of the measured noise exceed the level of 15 dB, with maximum reduction reaching 31 dB. Also, the control process is demonstrated to be smoother and faster.

Sensor-Based Vibration Signal Feature Extraction Using an Improved Composite Dictionary Matching Pursuit Algorithm

PubMed Central

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-01-01

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective. PMID:25207870

Sensor-based vibration signal feature extraction using an improved composite dictionary matching pursuit algorithm.

PubMed

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-09-09

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective.

Use of the Kalman Filter for Aortic Pressure Waveform Noise Reduction

PubMed Central

Lu, Hsiang-Wei; Wu, Chung-Che; Aliyazicioglu, Zekeriya; Kang, James S.

2017-01-01

Clinical applications that require extraction and interpretation of physiological signals or waveforms are susceptible to corruption by noise or artifacts. Real-time hemodynamic monitoring systems are important for clinicians to assess the hemodynamic stability of surgical or intensive care patients by interpreting hemodynamic parameters generated by an analysis of aortic blood pressure (ABP) waveform measurements. Since hemodynamic parameter estimation algorithms often detect events and features from measured ABP waveforms to generate hemodynamic parameters, noise and artifacts integrated into ABP waveforms can severely distort the interpretation of hemodynamic parameters by hemodynamic algorithms. In this article, we propose the use of the Kalman filter and the 4-element Windkessel model with static parameters, arterial compliance C, peripheral resistance R, aortic impedance r, and the inertia of blood L, to represent aortic circulation for generating accurate estimations of ABP waveforms through noise and artifact reduction. Results show the Kalman filter could very effectively eliminate noise and generate a good estimation from the noisy ABP waveform based on the past state history. The power spectrum of the measured ABP waveform and the synthesized ABP waveform shows two similar harmonic frequencies. PMID:28611850

A complex noise reduction method for improving visualization of SD-OCT skin biomedical images

NASA Astrophysics Data System (ADS)

Myakinin, Oleg O.; Zakharov, Valery P.; Bratchenko, Ivan A.; Kornilin, Dmitry V.; Khramov, Alexander G.

2014-05-01

In this paper we consider the original method of solving noise reduction problem for visualization's quality improvement of SD-OCT skin and tumors biomedical images. The principal advantages of OCT are high resolution and possibility of in vivo analysis. We propose a two-stage algorithm: 1) process of raw one-dimensional A-scans of SD-OCT and 2) remove a noise from the resulting B(C)-scans. The general mathematical methods of SD-OCT are unstable: if the noise of the CCD is 1.6% of the dynamic range then result distortions are already 25-40% of the dynamic range. We use at the first stage a resampling of A-scans and simple linear filters to reduce the amount of data and remove the noise of the CCD camera. The efficiency, improving productivity and conservation of the axial resolution when using this approach are showed. At the second stage we use an effective algorithms based on Hilbert-Huang Transform for more accurately noise peaks removal. The effectiveness of the proposed approach for visualization of malignant and benign skin tumors (melanoma, BCC etc.) and a significant improvement of SNR level for different methods of noise reduction are showed. Also in this study we consider a modification of this method depending of a specific hardware and software features of used OCT setup. The basic version does not require any hardware modifications of existing equipment. The effectiveness of proposed method for 3D visualization of tissues can simplify medical diagnosis in oncology.

An Approach for Practical Use of Common-Mode Noise Reduction Technique for In-Vehicle Electronic Equipment

NASA Astrophysics Data System (ADS)

Uno, Takanori; Ichikawa, Kouji; Mabuchi, Yuichi; Nakamura, Atsushi; Okazaki, Yuji; Asai, Hideki

In this paper, we studied the use of common-mode noise reduction technique for in-vehicle electronic equipment in an actual instrument design. We have improved the circuit model of the common-mode noise that flows to the wire harness to add the effect of a bypass capacitor located near the LSI. We analyzed the improved circuit model using a circuit simulator and verified the effectiveness of the noise reduction condition derived from the circuit model. It was also confirmed that offsetting the impedance mismatch in the PCB section requires to make a circuit constant larger than that necessary for doing the impedance mismatch in the LSI section. An evaluation circuit board comprising an automotive microcomputer was prototyped to experiment on the common-mode noise reduction effect of the board. The experimental results showed the noise reduction effect of the board. The experimental results also revealed that the degree of impedance mismatch in the LSI section can be estimated by using a PCB having a known impedance. We further inquired into the optimization of impedance parameters, which is difficult for actual products at present. To satisfy the noise reduction condition composed of numerous parameters, we proposed a design method using an optimization algorithm and an electromagnetic field simulator, and confirmed its effectiveness.

Temporal flicker reduction and denoising in video using sparse directional transforms

NASA Astrophysics Data System (ADS)

Kanumuri, Sandeep; Guleryuz, Onur G.; Civanlar, M. Reha; Fujibayashi, Akira; Boon, Choong S.

2008-08-01

The bulk of the video content available today over the Internet and over mobile networks suffers from many imperfections caused during acquisition and transmission. In the case of user-generated content, which is typically produced with inexpensive equipment, these imperfections manifest in various ways through noise, temporal flicker and blurring, just to name a few. Imperfections caused by compression noise and temporal flicker are present in both studio-produced and user-generated video content transmitted at low bit-rates. In this paper, we introduce an algorithm designed to reduce temporal flicker and noise in video sequences. The algorithm takes advantage of the sparse nature of video signals in an appropriate transform domain that is chosen adaptively based on local signal statistics. When the signal corresponds to a sparse representation in this transform domain, flicker and noise, which are spread over the entire domain, can be reduced easily by enforcing sparsity. Our results show that the proposed algorithm reduces flicker and noise significantly and enables better presentation of compressed videos.

A pratical deconvolution algorithm in multi-fiber spectra extraction

NASA Astrophysics Data System (ADS)

Zhang, Haotong; Li, Guangwei; Bai, Zhongrui

2015-08-01

Deconvolution algorithm is a very promising method in multi-fiber spectroscopy data reduction, the method can extract spectra to the photo noise level as well as improve the spectral resolution, but as mentioned in Bolton & Schlegel (2010), it is limited by its huge computation requirement and thus can not be implemented directly in actual data reduction. We develop a practical algorithm to solve the computation problem. The new algorithm can deconvolve a 2D fiber spectral image of any size with actual PSFs, which may vary with positions. We further consider the influence of noise, which is thought to be an intrinsic ill-posed problem in deconvolution algorithms. We modify our method with a Tikhonov regularization item to depress the method induced noise. A series of simulations based on LAMOST data are carried out to test our method under more real situations with poisson noise and extreme cross talk, i.e., the fiber-to-fiber distance is comparable to the FWHM of the fiber profile. Compared with the results of traditional extraction methods, i.e., the Aperture Extraction Method and the Profile Fitting Method, our method shows both higher S/N and spectral resolution. The computaion time for a noise added image with 250 fibers and 4k pixels in wavelength direction, is about 2 hours when the fiber cross talk is not in the extreme case and 3.5 hours for the extreme fiber cross talk. We finally apply our method to real LAMOST data. We find that the 1D spectrum extracted by our method has both higher SNR and resolution than the traditional methods, but there are still some suspicious weak features possibly caused by the noise sensitivity of the method around the strong emission lines. How to further attenuate the noise influence will be the topic of our future work. As we have demonstrated, multi-fiber spectra extracted by our method will have higher resolution and signal to noise ratio thus will provide more accurate information (such as higher radial velocity and metallicity measurement accuracy in stellar physics) to astronomers than traditional methods.

Evaluation of speech reception threshold in noise in young Cochlear™ Nucleus® system 6 implant recipients using two different digital remote microphone technologies and a speech enhancement sound processing algorithm.

PubMed

Razza, Sergio; Zaccone, Monica; Meli, Aannalisa; Cristofari, Eliana

2017-12-01

Children affected by hearing loss can experience difficulties in challenging and noisy environments even when deafness is corrected by Cochlear implant (CI) devices. These patients have a selective attention deficit in multiple listening conditions. At present, the most effective ways to improve the performance of speech recognition in noise consists of providing CI processors with noise reduction algorithms and of providing patients with bilateral CIs. The aim of this study was to compare speech performances in noise, across increasing noise levels, in CI recipients using two kinds of wireless remote-microphone radio systems that use digital radio frequency transmission: the Roger Inspiro accessory and the Cochlear Wireless Mini Microphone accessory. Eleven Nucleus Cochlear CP910 CI young user subjects were studied. The signal/noise ratio, at a speech reception threshold (SRT) value of 50%, was measured in different conditions for each patient: with CI only, with the Roger or with the MiniMic accessory. The effect of the application of the SNR-noise reduction algorithm in each of these conditions was also assessed. The tests were performed with the subject positioned in front of the main speaker, at a distance of 2.5 m. Another two speakers were positioned at 3.50 m. The main speaker at 65 dB issued disyllabic words. Babble noise signal was delivered through the other speakers, with variable intensity. The use of both wireless remote microphones improved the SRT results. Both systems improved gain of speech performances. The gain was higher with the Mini Mic system (SRT = -4.76) than the Roger system (SRT = -3.01). The addition of the NR algorithm did not statistically further improve the results. There is significant improvement in speech recognition results with both wireless digital remote microphone accessories, in particular with the Mini Mic system when used with the CP910 processor. The use of a remote microphone accessory surpasses the benefit of application of NR algorithm. Copyright © 2017. Published by Elsevier B.V.

Analysis of de-noising methods to improve the precision of the ILSF BPM electronic readout system

NASA Astrophysics Data System (ADS)

Shafiee, M.; Feghhi, S. A. H.; Rahighi, J.

2016-12-01

In order to have optimum operation and precise control system at particle accelerators, it is required to measure the beam position with the precision of sub-μm. We developed a BPM electronic readout system at Iranian Light Source Facility and it has been experimentally tested at ALBA accelerator facility. The results show the precision of 0.54 μm in beam position measurements. To improve the precision of this beam position monitoring system to sub-μm level, we have studied different de-noising methods such as principal component analysis, wavelet transforms, filtering by FIR, and direct averaging method. An evaluation of the noise reduction was given to testify the ability of these methods. The results show that the noise reduction based on Daubechies wavelet transform is better than other algorithms, and the method is suitable for signal noise reduction in beam position monitoring system.

Reduction of the Radiating Sound of a Submerged Finite Cylindrical Shell Structure by Active Vibration Control

PubMed Central

Kim, Heung Soo; Sohn, Jung Woo; Jeon, Juncheol; Choi, Seung-Bok

2013-01-01

In this work, active vibration control of an underwater cylindrical shell structure was investigated, to suppress structural vibration and structure-borne noise in water. Finite element modeling of the submerged cylindrical shell structure was developed, and experimentally evaluated. Modal reduction was conducted to obtain the reduced system equation for the active feedback control algorithm. Three Macro Fiber Composites (MFCs) were used as actuators and sensors. One MFC was used as an exciter. The optimum control algorithm was designed based on the reduced system equations. The active control performance was then evaluated using the lab scale underwater cylindrical shell structure. Structural vibration and structure-borne noise of the underwater cylindrical shell structure were reduced significantly by activating the optimal controller associated with the MFC actuators. The results provide that active vibration control of the underwater structure is a useful means to reduce structure-borne noise in water. PMID:23389344

Reduction of the radiating sound of a submerged finite cylindrical shell structure by active vibration control.

PubMed

Kim, Heung Soo; Sohn, Jung Woo; Jeon, Juncheol; Choi, Seung-Bok

2013-02-06

In this work, active vibration control of an underwater cylindrical shell structure was investigated, to suppress structural vibration and structure-borne noise in water. Finite element modeling of the submerged cylindrical shell structure was developed, and experimentally evaluated. Modal reduction was conducted to obtain the reduced system equation for the active feedback control algorithm. Three Macro Fiber Composites (MFCs) were used as actuators and sensors. One MFC was used as an exciter. The optimum control algorithm was designed based on the reduced system equations. The active control performance was then evaluated using the lab scale underwater cylindrical shell structure. Structural vibration and structure-borne noise of the underwater cylindrical shell structure were reduced significantly by activating the optimal controller associated with the MFC actuators. The results provide that active vibration control of the underwater structure is a useful means to reduce structure-borne noise in water.

Adaptive statistical iterative reconstruction and bismuth shielding for evaluation of dose reduction to the eye and image quality during head CT

NASA Astrophysics Data System (ADS)

Kim, Myeong Seong; Choi, Jiwon; Kim, Sun Young; Kweon, Dae Cheol

2014-03-01

There is a concern regarding the adverse effects of increasing radiation doses due to repeated computed tomography (CT) scans, especially in radiosensitive organs and portions thereof, such as the lenses of the eyes. Bismuth shielding with an adaptive statistical iterative reconstruction (ASIR) algorithm was recently introduced in our clinic as a method to reduce the absorbed radiation dose. This technique was applied to the lens of the eye during CT scans. The purpose of this study was to evaluate the reduction in the absorbed radiation dose and to determine the noise level when using bismuth shielding and the ASIR algorithm with the GE DC 750 HD 64-channel CT scanner for CT of the head of a humanoid phantom. With the use of bismuth shielding, the noise level was higher in the beam-hardening artifact areas than in the revealed artifact areas. However, with the use of ASIR, the noise level was lower than that with the use of bismuth alone; it was also lower in the artifact areas. The reduction in the radiation dose with the use of bismuth was greatest at the surface of the phantom to a limited depth. In conclusion, it is possible to reduce the radiation level and slightly decrease the bismuth-induced noise level by using a combination of ASIR as an algorithm process and bismuth as an in-plane hardware-type shielding method.

Analysis on Influence Factors of Adaptive Filter Acting on ANC

NASA Astrophysics Data System (ADS)

Zhang, Xiuqun; Zou, Liang; Ni, Guangkui; Wang, Xiaojun; Han, Tao; Zhao, Quanfu

The noise problem has become more and more serious in recent years. The adaptive filter theory which is applied in ANC [1] (active noise control) has also attracted more and more attention. In this article, the basic principle and algorithm of adaptive theory are both researched. And then the influence factor that affects its covergence rate and noise reduction is also simulated.

Head-mounted active noise control system with virtual sensing technique

NASA Astrophysics Data System (ADS)

Miyazaki, Nobuhiro; Kajikawa, Yoshinobu

2015-03-01

In this paper, we apply a virtual sensing technique to a head-mounted active noise control (ANC) system we have already proposed. The proposed ANC system can reduce narrowband noise while improving the noise reduction ability at the desired locations. A head-mounted ANC system based on an adaptive feedback structure can reduce noise with periodicity or narrowband components. However, since quiet zones are formed only at the locations of error microphones, an adequate noise reduction cannot be achieved at the locations where error microphones cannot be placed such as near the eardrums. A solution to this problem is to apply a virtual sensing technique. A virtual sensing ANC system can achieve higher noise reduction at the desired locations by measuring the system models from physical sensors to virtual sensors, which will be used in the online operation of the virtual sensing ANC algorithm. Hence, we attempt to achieve the maximum noise reduction near the eardrums by applying the virtual sensing technique to the head-mounted ANC system. However, it is impossible to place the microphone near the eardrums. Therefore, the system models from physical sensors to virtual sensors are estimated using the Head And Torso Simulator (HATS) instead of human ears. Some simulation, experimental, and subjective assessment results demonstrate that the head-mounted ANC system with virtual sensing is superior to that without virtual sensing in terms of the noise reduction ability at the desired locations.

Noise correlation in CBCT projection data and its application for noise reduction in low-dose CBCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Hua; Ouyang, Luo; Wang, Jing, E-mail: jhma@smu.edu.cn, E-mail: jing.wang@utsouthwestern.edu

2014-03-15

Purpose: To study the noise correlation properties of cone-beam CT (CBCT) projection data and to incorporate the noise correlation information to a statistics-based projection restoration algorithm for noise reduction in low-dose CBCT. Methods: In this study, the authors systematically investigated the noise correlation properties among detector bins of CBCT projection data by analyzing repeated projection measurements. The measurements were performed on a TrueBeam onboard CBCT imaging system with a 4030CB flat panel detector. An anthropomorphic male pelvis phantom was used to acquire 500 repeated projection data at six different dose levels from 0.1 to 1.6 mAs per projection at threemore » fixed angles. To minimize the influence of the lag effect, lag correction was performed on the consecutively acquired projection data. The noise correlation coefficient between detector bin pairs was calculated from the corrected projection data. The noise correlation among CBCT projection data was then incorporated into the covariance matrix of the penalized weighted least-squares (PWLS) criterion for noise reduction of low-dose CBCT. Results: The analyses of the repeated measurements show that noise correlation coefficients are nonzero between the nearest neighboring bins of CBCT projection data. The average noise correlation coefficients for the first- and second-order neighbors are 0.20 and 0.06, respectively. The noise correlation coefficients are independent of the dose level. Reconstruction of the pelvis phantom shows that the PWLS criterion with consideration of noise correlation (PWLS-Cor) results in a lower noise level as compared to the PWLS criterion without considering the noise correlation (PWLS-Dia) at the matched resolution. At the 2.0 mm resolution level in the axial-plane noise resolution tradeoff analysis, the noise level of the PWLS-Cor reconstruction is 6.3% lower than that of the PWLS-Dia reconstruction. Conclusions: Noise is correlated among nearest neighboring detector bins of CBCT projection data. An accurate noise model of CBCT projection data can improve the performance of the statistics-based projection restoration algorithm for low-dose CBCT.« less

Quantitative Image Quality and Histogram-Based Evaluations of an Iterative Reconstruction Algorithm at Low-to-Ultralow Radiation Dose Levels: A Phantom Study in Chest CT

PubMed Central

Lee, Ki Baek

2018-01-01

Objective To describe the quantitative image quality and histogram-based evaluation of an iterative reconstruction (IR) algorithm in chest computed tomography (CT) scans at low-to-ultralow CT radiation dose levels. Materials and Methods In an adult anthropomorphic phantom, chest CT scans were performed with 128-section dual-source CT at 70, 80, 100, 120, and 140 kVp, and the reference (3.4 mGy in volume CT Dose Index [CTDIvol]), 30%-, 60%-, and 90%-reduced radiation dose levels (2.4, 1.4, and 0.3 mGy). The CT images were reconstructed by using filtered back projection (FBP) algorithms and IR algorithm with strengths 1, 3, and 5. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were statistically compared between different dose levels, tube voltages, and reconstruction algorithms. Moreover, histograms of subtraction images before and after standardization in x- and y-axes were visually compared. Results Compared with FBP images, IR images with strengths 1, 3, and 5 demonstrated image noise reduction up to 49.1%, SNR increase up to 100.7%, and CNR increase up to 67.3%. Noteworthy image quality degradations on IR images including a 184.9% increase in image noise, 63.0% decrease in SNR, and 51.3% decrease in CNR, and were shown between 60% and 90% reduced levels of radiation dose (p < 0.0001). Subtraction histograms between FBP and IR images showed progressively increased dispersion with increased IR strength and increased dose reduction. After standardization, the histograms appeared deviated and ragged between FBP images and IR images with strength 3 or 5, but almost normally-distributed between FBP images and IR images with strength 1. Conclusion The IR algorithm may be used to save radiation doses without substantial image quality degradation in chest CT scanning of the adult anthropomorphic phantom, down to approximately 1.4 mGy in CTDIvol (60% reduced dose). PMID:29354008

Single and Multiple Microphone Noise Reduction Strategies in Cochlear Implants

PubMed Central

Azimi, Behnam; Hu, Yi; Friedland, David R.

2012-01-01

To restore hearing sensation, cochlear implants deliver electrical pulses to the auditory nerve by relying on sophisticated signal processing algorithms that convert acoustic inputs to electrical stimuli. Although individuals fitted with cochlear implants perform well in quiet, in the presence of background noise, the speech intelligibility of cochlear implant listeners is more susceptible to background noise than that of normal hearing listeners. Traditionally, to increase performance in noise, single-microphone noise reduction strategies have been used. More recently, a number of approaches have suggested that speech intelligibility in noise can be improved further by making use of two or more microphones, instead. Processing strategies based on multiple microphones can better exploit the spatial diversity of speech and noise because such strategies rely mostly on spatial information about the relative position of competing sound sources. In this article, we identify and elucidate the most significant theoretical aspects that underpin single- and multi-microphone noise reduction strategies for cochlear implants. More analytically, we focus on strategies of both types that have been shown to be promising for use in current-generation implant devices. We present data from past and more recent studies, and furthermore we outline the direction that future research in the area of noise reduction for cochlear implants could follow. PMID:22923425

Method for simulating dose reduction in digital mammography using the Anscombe transformation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Borges, Lucas R., E-mail: lucas.rodrigues.borges@usp.br; Oliveira, Helder C. R. de; Nunes, Polyana F.

2016-06-15

Purpose: This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose. Methods: The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtainedmore » by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose. Results: The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%. Conclusions: A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions.« less

Method for simulating dose reduction in digital mammography using the Anscombe transformation

PubMed Central

Borges, Lucas R.; de Oliveira, Helder C. R.; Nunes, Polyana F.; Bakic, Predrag R.; Maidment, Andrew D. A.; Vieira, Marcelo A. C.

2016-01-01

Purpose: This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose. Methods: The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtained by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose. Results: The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%. Conclusions: A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions. PMID:27277017

Advanced Beamformers for Cochlear Implant Users: Acute Measurement of Speech Perception in Challenging Listening Conditions

PubMed Central

Buechner, Andreas; Dyballa, Karl-Heinz; Hehrmann, Phillipp; Fredelake, Stefan; Lenarz, Thomas

2014-01-01

Objective To investigate the performance of monaural and binaural beamforming technology with an additional noise reduction algorithm, in cochlear implant recipients. Method This experimental study was conducted as a single subject repeated measures design within a large German cochlear implant centre. Twelve experienced users of an Advanced Bionics HiRes90K or CII implant with a Harmony speech processor were enrolled. The cochlear implant processor of each subject was connected to one of two bilaterally placed state-of-the-art hearing aids (Phonak Ambra) providing three alternative directional processing options: an omnidirectional setting, an adaptive monaural beamformer, and a binaural beamformer. A further noise reduction algorithm (ClearVoice) was applied to the signal on the cochlear implant processor itself. The speech signal was presented from 0° and speech shaped noise presented from loudspeakers placed at ±70°, ±135° and 180°. The Oldenburg sentence test was used to determine the signal-to-noise ratio at which subjects scored 50% correct. Results Both the adaptive and binaural beamformer were significantly better than the omnidirectional condition (5.3 dB±1.2 dB and 7.1 dB±1.6 dB (p<0.001) respectively). The best score was achieved with the binaural beamformer in combination with the ClearVoice noise reduction algorithm, with a significant improvement in SRT of 7.9 dB±2.4 dB (p<0.001) over the omnidirectional alone condition. Conclusions The study showed that the binaural beamformer implemented in the Phonak Ambra hearing aid could be used in conjunction with a Harmony speech processor to produce substantial average improvements in SRT of 7.1 dB. The monaural, adaptive beamformer provided an averaged SRT improvement of 5.3 dB. PMID:24755864

Active feedforward noise control and signal tracking of headsets: Electroacoustic analysis and system implementation.

PubMed

Bai, Mingsian R; Pan, Weichi; Chen, Hungyu

2018-03-01

Active noise control (ANC) of headsets is revisited in this paper. An in-depth electroacoustic analysis of the combined loudspeaker-cavity headset system is conducted on the basis of electro-mechano-acoustical analogous circuits. Model matching of the primary path and the secondary path leads to a feedforward control architecture. The ideal controller sheds some light on the key parameters that affect the noise reduction performance. Filtered-X least-mean-squares algorithm is employed to implement the feedforward controller on a digital signal processor. Since the relative delay of the primary path and the secondary path is crucial to the noise reduction performance, multirate signal processing with polyphase implementation is utilized to minimize the effective analog-digital conversion delay in the secondary path. Ad hoc decimation and interpolation filters are designed in order not to introduce excessive phase delays at the cutoff. Real-time experiments are undertaken to validate the implemented ANC system. Listening tests are also conducted to compare the fixed controller and the adaptive controller in terms of noise reduction and signal tracking performance for three noise types. The results have demonstrated that the fixed feedforward controller achieved satisfactory noise reduction performance and signal tracking quality.

An experimental investigation of the interior noise control effects of propeller synchrophasing

NASA Technical Reports Server (NTRS)

Jones, J. D.; Fuller, C. R.

1986-01-01

A simplified cylindrical model of an aircraft fuselage is used to investigate the mechanisms of interior noise suppression using synchrophasing techniques. This investigation allows isolation of important parameters to define the characteristics of synchrophasing. The optimum synchrophase angle for maximum noise reduction is found for several interior microphone positions with pure tone source excitation. Noise reductions of up to 30 dB are shown for some microphone positions, however, overall reductions are less. A computer algorithm is developed to decompose the cylinder vibration into modal components over a wide range of synchrophase angles. The circumferential modal response of the shell vibration is shown to govern the transmission of sound into the cylinder rather than localized transmission. As well as investigating synchrophasing, the interior sound field due to sources typical of propellers has been measured and discussed.

Automatic welding detection by an intelligent tool pipe inspection

NASA Astrophysics Data System (ADS)

Arizmendi, C. J.; Garcia, W. L.; Quintero, M. A.

2015-07-01

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called “smart pig” in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

Algorithms and analyses for stochastic optimization for turbofan noise reduction using parallel reduced-order modeling

NASA Astrophysics Data System (ADS)

Yang, Huanhuan; Gunzburger, Max

2017-06-01

Simulation-based optimization of acoustic liner design in a turbofan engine nacelle for noise reduction purposes can dramatically reduce the cost and time needed for experimental designs. Because uncertainties are inevitable in the design process, a stochastic optimization algorithm is posed based on the conditional value-at-risk measure so that an ideal acoustic liner impedance is determined that is robust in the presence of uncertainties. A parallel reduced-order modeling framework is developed that dramatically improves the computational efficiency of the stochastic optimization solver for a realistic nacelle geometry. The reduced stochastic optimization solver takes less than 500 seconds to execute. In addition, well-posedness and finite element error analyses of the state system and optimization problem are provided.

Speckle reduction in optical coherence tomography by adaptive total variation method

NASA Astrophysics Data System (ADS)

Wu, Tong; Shi, Yaoyao; Liu, Youwen; He, Chongjun

2015-12-01

An adaptive total variation method based on the combination of speckle statistics and total variation restoration is proposed and developed for reducing speckle noise in optical coherence tomography (OCT) images. The statistical distribution of the speckle noise in OCT image is investigated and measured. With the measured parameters such as the mean value and variance of the speckle noise, the OCT image is restored by the adaptive total variation restoration method. The adaptive total variation restoration algorithm was applied to the OCT images of a volunteer's hand skin, which showed effective speckle noise reduction and image quality improvement. For image quality comparison, the commonly used median filtering method was also applied to the same images to reduce the speckle noise. The measured results demonstrate the superior performance of the adaptive total variation restoration method in terms of image signal-to-noise ratio, equivalent number of looks, contrast-to-noise ratio, and mean square error.

Post-processing method to reduce noise while preserving high time resolution in aethalometer real-time black carbon data

EPA Science Inventory

Real-time aerosol black carbon (BC) data, presented at time resolutions on the order of seconds to minutes, is desirable in field and source characterization studies measuring rapidly varying concentrations of BC. The Optimized Noise-reduction Averaging (ONA) algorithm has been d...

An Eigensystem Realization Algorithm (ERA) for modal parameter identification and model reduction

NASA Technical Reports Server (NTRS)

Juang, J. N.; Pappa, R. S.

1985-01-01

A method, called the Eigensystem Realization Algorithm (ERA), is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system modes and noise modes. For illustration of the algorithm, examples are shown using simulation data and experimental data for a rectangular grid structure.

Bilateral filtering using the full noise covariance matrix applied to x-ray phase-contrast computed tomography.

PubMed

Allner, S; Koehler, T; Fehringer, A; Birnbacher, L; Willner, M; Pfeiffer, F; Noël, P B

2016-05-21

The purpose of this work is to develop an image-based de-noising algorithm that exploits complementary information and noise statistics from multi-modal images, as they emerge in x-ray tomography techniques, for instance grating-based phase-contrast CT and spectral CT. Among the noise reduction methods, image-based de-noising is one popular approach and the so-called bilateral filter is a well known algorithm for edge-preserving filtering. We developed a generalization of the bilateral filter for the case where the imaging system provides two or more perfectly aligned images. The proposed generalization is statistically motivated and takes the full second order noise statistics of these images into account. In particular, it includes a noise correlation between the images and spatial noise correlation within the same image. The novel generalized three-dimensional bilateral filter is applied to the attenuation and phase images created with filtered backprojection reconstructions from grating-based phase-contrast tomography. In comparison to established bilateral filters, we obtain improved noise reduction and at the same time a better preservation of edges in the images on the examples of a simulated soft-tissue phantom, a human cerebellum and a human artery sample. The applied full noise covariance is determined via cross-correlation of the image noise. The filter results yield an improved feature recovery based on enhanced noise suppression and edge preservation as shown here on the example of attenuation and phase images captured with grating-based phase-contrast computed tomography. This is supported by quantitative image analysis. Without being bound to phase-contrast imaging, this generalized filter is applicable to any kind of noise-afflicted image data with or without noise correlation. Therefore, it can be utilized in various imaging applications and fields.

Evaluation of an iterative model-based CT reconstruction algorithm by intra-patient comparison of standard and ultra-low-dose examinations.

PubMed

Noël, Peter B; Engels, Stephan; Köhler, Thomas; Muenzel, Daniela; Franz, Daniela; Rasper, Michael; Rummeny, Ernst J; Dobritz, Martin; Fingerle, Alexander A

2018-01-01

Background The explosive growth of computer tomography (CT) has led to a growing public health concern about patient and population radiation dose. A recently introduced technique for dose reduction, which can be combined with tube-current modulation, over-beam reduction, and organ-specific dose reduction, is iterative reconstruction (IR). Purpose To evaluate the quality, at different radiation dose levels, of three reconstruction algorithms for diagnostics of patients with proven liver metastases under tumor follow-up. Material and Methods A total of 40 thorax-abdomen-pelvis CT examinations acquired from 20 patients in a tumor follow-up were included. All patients were imaged using the standard-dose and a specific low-dose CT protocol. Reconstructed slices were generated by using three different reconstruction algorithms: a classical filtered back projection (FBP); a first-generation iterative noise-reduction algorithm (iDose4); and a next generation model-based IR algorithm (IMR). Results The overall detection of liver lesions tended to be higher with the IMR algorithm than with FBP or iDose4. The IMR dataset at standard dose yielded the highest overall detectability, while the low-dose FBP dataset showed the lowest detectability. For the low-dose protocols, a significantly improved detectability of the liver lesion can be reported compared to FBP or iDose 4 ( P = 0.01). The radiation dose decreased by an approximate factor of 5 between the standard-dose and the low-dose protocol. Conclusion The latest generation of IR algorithms significantly improved the diagnostic image quality and provided virtually noise-free images for ultra-low-dose CT imaging.

Comparisons of spectral characteristics of wind noise between omnidirectional and directional microphones.

PubMed

Chung, King

2012-06-01

Wind noise reduction is a topic of ongoing research and development for hearing aids and cochlear implants. The purposes of this study were to examine spectral characteristics of wind noise generated by directional (DIR) and omnidirectional (OMNI) microphones on different styles of hearing aids and to derive wind noise reduction strategies. Three digital hearing aids (BTE, ITE, and ITC) were fitted to Knowles Electronic Manikin for Acoustic Research. They were programmed to have linear amplification and matching frequency responses between the DIR and OMNI modes. Flow noise recordings were made from 0° to 360° azimuths at flow velocities of 4.5, 9.0, and 13.5 m/s in a quiet wind tunnel. Noise levels were analyzed in one-third octave bands from 100 to 8000 Hz. Comparison of wind noise revealed that DIR generally produced higher noise levels than OMNI for all hearing aids, but it could result in lower levels than OMNI at some frequencies and head angles. Wind noise reduction algorithms can be designed to detect noise levels of DIR and OMNI outputs in each frequency channel, remove the constraint to switch to OMNI in low-frequency channel(s) only, and adopt the microphone mode with lower noise levels to take advantage of the microphone differences.

Noise reduction in digital holography based on a filtering algorithm

NASA Astrophysics Data System (ADS)

Zhang, Wenhui; Cao, Liangcai; Zhang, Hua; Jin, Guofan; Brady, David

2018-02-01

Holography is a tool to record the object wavefront by interference. Complex amplitude of the object wave is coded into a two dimensional hologram. Unfortunately, the conjugate wave and background wave would also appear at the object plane during reconstruction, as noise, which blurs the reconstructed object. From the perspective of wave, we propose a filtering algorithm to get a noise-reduced reconstruction. Due to the fact that the hologram is a kind of amplitude grating, three waves would appear when reconstruction, which are object wave, conjugate wave and background wave. The background is easy to eliminate by frequency domain filtering. The object wave and conjugate wave are signals to be dealt with. These two waves, as a whole, propagate in the space. However, when detected at the original object plane, the object wave would diffract into a sparse pattern while the conjugate wave would diffract into a diffused pattern forming the noise. Hence, the noise can be reduced based on these difference with a filtering algorithm. Both amplitude and phase distributions are truthfully retrieved in our simulation and experimental demonstration.

Evaluating and minimizing noise impact due to aircraft flyover

NASA Technical Reports Server (NTRS)

Jacobson, I. D.

1980-01-01

The results of a study on the evaluation and reduction of noise impact to a community due to aircraft landing and takeoff operations are presented. The case of multiple aircrafts flying on several trajectories, for either approach/landings or takeoffs was examined. An extremely realistic model of the flight path was developed. The annoyance criterion used was the noise impact index (NII). The algorithm was applied to Patrick Henry International Airport.

Flight Test of ASAC Aircraft Interior Noise Control System

NASA Technical Reports Server (NTRS)

Palumbo, Dan; Cabell, Ran; Cline, John; Sullivan, Brenda

1999-01-01

A flight test is described in which an active structural/acoustic control system reduces turboprop induced interior noise on a Raytheon Aircraft Company 1900D airliner. Control inputs to 21 inertial force actuators were computed adaptively using a transform domain version of the multichannel filtered-X LMS algorithm to minimize the mean square response of 32 microphones. A combinatorial search algorithm was employed to optimize placement of the force actuators on the aircraft frame. Both single frequency and multi-frequency results are presented. Reductions of up to 15 dB were obtained at the blade passage frequency (BPF) during single frequency control tests. Simultaneous reductions of the BPF and next 2 harmonics of 10 dB, 2.5 dB and 3.0 dB, were obtained in a multi-frequency test.

Dereverberation and denoising based on generalized spectral subtraction by multi-channel LMS algorithm using a small-scale microphone array

NASA Astrophysics Data System (ADS)

Wang, Longbiao; Odani, Kyohei; Kai, Atsuhiko

2012-12-01

A blind dereverberation method based on power spectral subtraction (SS) using a multi-channel least mean squares algorithm was previously proposed to suppress the reverberant speech without additive noise. The results of isolated word speech recognition experiments showed that this method achieved significant improvements over conventional cepstral mean normalization (CMN) in a reverberant environment. In this paper, we propose a blind dereverberation method based on generalized spectral subtraction (GSS), which has been shown to be effective for noise reduction, instead of power SS. Furthermore, we extend the missing feature theory (MFT), which was initially proposed to enhance the robustness of additive noise, to dereverberation. A one-stage dereverberation and denoising method based on GSS is presented to simultaneously suppress both the additive noise and nonstationary multiplicative noise (reverberation). The proposed dereverberation method based on GSS with MFT is evaluated on a large vocabulary continuous speech recognition task. When the additive noise was absent, the dereverberation method based on GSS with MFT using only 2 microphones achieves a relative word error reduction rate of 11.4 and 32.6% compared to the dereverberation method based on power SS and the conventional CMN, respectively. For the reverberant and noisy speech, the dereverberation and denoising method based on GSS achieves a relative word error reduction rate of 12.8% compared to the conventional CMN with GSS-based additive noise reduction method. We also analyze the effective factors of the compensation parameter estimation for the dereverberation method based on SS, such as the number of channels (the number of microphones), the length of reverberation to be suppressed, and the length of the utterance used for parameter estimation. The experimental results showed that the SS-based method is robust in a variety of reverberant environments for both isolated and continuous speech recognition and under various parameter estimation conditions.

A Noise Reduction Method for Dual-Mass Micro-Electromechanical Gyroscopes Based on Sample Entropy Empirical Mode Decomposition and Time-Frequency Peak Filtering

PubMed Central

Shen, Chong; Li, Jie; Zhang, Xiaoming; Shi, Yunbo; Tang, Jun; Cao, Huiliang; Liu, Jun

2016-01-01

The different noise components in a dual-mass micro-electromechanical system (MEMS) gyroscope structure is analyzed in this paper, including mechanical-thermal noise (MTN), electronic-thermal noise (ETN), flicker noise (FN) and Coriolis signal in-phase noise (IPN). The structure equivalent electronic model is established, and an improved white Gaussian noise reduction method for dual-mass MEMS gyroscopes is proposed which is based on sample entropy empirical mode decomposition (SEEMD) and time-frequency peak filtering (TFPF). There is a contradiction in TFPS, i.e., selecting a short window length may lead to good preservation of signal amplitude but bad random noise reduction, whereas selecting a long window length may lead to serious attenuation of the signal amplitude but effective random noise reduction. In order to achieve a good tradeoff between valid signal amplitude preservation and random noise reduction, SEEMD is adopted to improve TFPF. Firstly, the original signal is decomposed into intrinsic mode functions (IMFs) by EMD, and the SE of each IMF is calculated in order to classify the numerous IMFs into three different components; then short window TFPF is employed for low frequency component of IMFs, and long window TFPF is employed for high frequency component of IMFs, and the noise component of IMFs is wiped off directly; at last the final signal is obtained after reconstruction. Rotation experimental and temperature experimental are carried out to verify the proposed SEEMD-TFPF algorithm, the verification and comparison results show that the de-noising performance of SEEMD-TFPF is better than that achievable with the traditional wavelet, Kalman filter and fixed window length TFPF methods. PMID:27258276

A Noise Reduction Method for Dual-Mass Micro-Electromechanical Gyroscopes Based on Sample Entropy Empirical Mode Decomposition and Time-Frequency Peak Filtering.

PubMed

Shen, Chong; Li, Jie; Zhang, Xiaoming; Shi, Yunbo; Tang, Jun; Cao, Huiliang; Liu, Jun

2016-05-31

The different noise components in a dual-mass micro-electromechanical system (MEMS) gyroscope structure is analyzed in this paper, including mechanical-thermal noise (MTN), electronic-thermal noise (ETN), flicker noise (FN) and Coriolis signal in-phase noise (IPN). The structure equivalent electronic model is established, and an improved white Gaussian noise reduction method for dual-mass MEMS gyroscopes is proposed which is based on sample entropy empirical mode decomposition (SEEMD) and time-frequency peak filtering (TFPF). There is a contradiction in TFPS, i.e., selecting a short window length may lead to good preservation of signal amplitude but bad random noise reduction, whereas selecting a long window length may lead to serious attenuation of the signal amplitude but effective random noise reduction. In order to achieve a good tradeoff between valid signal amplitude preservation and random noise reduction, SEEMD is adopted to improve TFPF. Firstly, the original signal is decomposed into intrinsic mode functions (IMFs) by EMD, and the SE of each IMF is calculated in order to classify the numerous IMFs into three different components; then short window TFPF is employed for low frequency component of IMFs, and long window TFPF is employed for high frequency component of IMFs, and the noise component of IMFs is wiped off directly; at last the final signal is obtained after reconstruction. Rotation experimental and temperature experimental are carried out to verify the proposed SEEMD-TFPF algorithm, the verification and comparison results show that the de-noising performance of SEEMD-TFPF is better than that achievable with the traditional wavelet, Kalman filter and fixed window length TFPF methods.

Characterization of adaptive statistical iterative reconstruction algorithm for dose reduction in CT: A pediatric oncology perspective

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brady, S. L.; Yee, B. S.; Kaufman, R. A.

Purpose: This study demonstrates a means of implementing an adaptive statistical iterative reconstruction (ASiR Trade-Mark-Sign ) technique for dose reduction in computed tomography (CT) while maintaining similar noise levels in the reconstructed image. The effects of image quality and noise texture were assessed at all implementation levels of ASiR Trade-Mark-Sign . Empirically derived dose reduction limits were established for ASiR Trade-Mark-Sign for imaging of the trunk for a pediatric oncology population ranging from 1 yr old through adolescence/adulthood. Methods: Image quality was assessed using metrics established by the American College of Radiology (ACR) CT accreditation program. Each image quality metricmore » was tested using the ACR CT phantom with 0%-100% ASiR Trade-Mark-Sign blended with filtered back projection (FBP) reconstructed images. Additionally, the noise power spectrum (NPS) was calculated for three common reconstruction filters of the trunk. The empirically derived limitations on ASiR Trade-Mark-Sign implementation for dose reduction were assessed using (1, 5, 10) yr old and adolescent/adult anthropomorphic phantoms. To assess dose reduction limits, the phantoms were scanned in increments of increased noise index (decrementing mA using automatic tube current modulation) balanced with ASiR Trade-Mark-Sign reconstruction to maintain noise equivalence of the 0% ASiR Trade-Mark-Sign image. Results: The ASiR Trade-Mark-Sign algorithm did not produce any unfavorable effects on image quality as assessed by ACR criteria. Conversely, low-contrast resolution was found to improve due to the reduction of noise in the reconstructed images. NPS calculations demonstrated that images with lower frequency noise had lower noise variance and coarser graininess at progressively higher percentages of ASiR Trade-Mark-Sign reconstruction; and in spite of the similar magnitudes of noise, the image reconstructed with 50% or more ASiR Trade-Mark-Sign presented a more smoothed appearance than the pre-ASiR Trade-Mark-Sign 100% FBP image. Finally, relative to non-ASiR Trade-Mark-Sign images with 100% of standard dose across the pediatric phantom age spectrum, similar noise levels were obtained in the images at a dose reduction of 48% with 40% ASIR Trade-Mark-Sign and a dose reduction of 82% with 100% ASIR Trade-Mark-Sign . Conclusions: The authors' work was conducted to identify the dose reduction limits of ASiR Trade-Mark-Sign for a pediatric oncology population using automatic tube current modulation. Improvements in noise levels from ASiR Trade-Mark-Sign reconstruction were adapted to provide lower radiation exposure (i.e., lower mA) instead of improved image quality. We have demonstrated for the image quality standards required at our institution, a maximum dose reduction of 82% can be achieved using 100% ASiR Trade-Mark-Sign ; however, to negate changes in the appearance of reconstructed images using ASiR Trade-Mark-Sign with a medium to low frequency noise preserving reconstruction filter (i.e., standard), 40% ASiR Trade-Mark-Sign was implemented in our clinic for 42%-48% dose reduction at all pediatric ages without a visually perceptible change in image quality or image noise.« less

Two-microphone spatial filtering provides speech reception benefits for cochlear implant users in difficult acoustic environments

PubMed Central

Goldsworthy, Raymond L.; Delhorne, Lorraine A.; Desloge, Joseph G.; Braida, Louis D.

2014-01-01

This article introduces and provides an assessment of a spatial-filtering algorithm based on two closely-spaced (∼1 cm) microphones in a behind-the-ear shell. The evaluated spatial-filtering algorithm used fast (∼10 ms) temporal-spectral analysis to determine the location of incoming sounds and to enhance sounds arriving from straight ahead of the listener. Speech reception thresholds (SRTs) were measured for eight cochlear implant (CI) users using consonant and vowel materials under three processing conditions: An omni-directional response, a dipole-directional response, and the spatial-filtering algorithm. The background noise condition used three simultaneous time-reversed speech signals as interferers located at 90°, 180°, and 270°. Results indicated that the spatial-filtering algorithm can provide speech reception benefits of 5.8 to 10.7 dB SRT compared to an omni-directional response in a reverberant room with multiple noise sources. Given the observed SRT benefits, coupled with an efficient design, the proposed algorithm is promising as a CI noise-reduction solution. PMID:25096120

An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles

NASA Astrophysics Data System (ADS)

Liu, Yahui; Fan, Xiaoqian; Lv, Chen; Wu, Jian; Li, Liang; Ding, Dawei

2018-02-01

Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in this paper. The algorithm continuously updates the measurement noise variance and processes noise variance of the system by collecting the estimated and measured values, and this method can suppress white noise. Because a measured value closer to the current time would more accurately reflect the characteristics of the noise, an attenuation factor is introduced to increase the weight of the current value, in order to deal with the noise variance caused by environment disturbance. To validate the effectiveness of the proposed algorithm, a series of road tests are carried out in urban environment. The GPS and IMU data of the experiments were collected and processed by dSPACE and MATLAB/Simulink. Based on the test results, the accuracy of the proposed algorithm is 20% higher than that of a traditional Adaptive Kalman Filter. It also shows that the precision of the integrated navigation can be improved due to the reduction of the influence of environment noise.

Edge enhancement algorithm for low-dose X-ray fluoroscopic imaging.

PubMed

Lee, Min Seok; Park, Chul Hee; Kang, Moon Gi

2017-12-01

Low-dose X-ray fluoroscopy has continually evolved to reduce radiation risk to patients during clinical diagnosis and surgery. However, the reduction in dose exposure causes quality degradation of the acquired images. In general, an X-ray device has a time-average pre-processor to remove the generated quantum noise. However, this pre-processor causes blurring and artifacts within the moving edge regions, and noise remains in the image. During high-pass filtering (HPF) to enhance edge detail, this noise in the image is amplified. In this study, a 2D edge enhancement algorithm comprising region adaptive HPF with the transient improvement (TI) method, as well as artifacts and noise reduction (ANR), was developed for degraded X-ray fluoroscopic images. The proposed method was applied in a static scene pre-processed by a low-dose X-ray fluoroscopy device. First, the sharpness of the X-ray image was improved using region adaptive HPF with the TI method, which facilitates sharpening of edge details without overshoot problems. Then, an ANR filter that uses an edge directional kernel was developed to remove the artifacts and noise that can occur during sharpening, while preserving edge details. The quantitative and qualitative results obtained by applying the developed method to low-dose X-ray fluoroscopic images and visually and numerically comparing the final images with images improved using conventional edge enhancement techniques indicate that the proposed method outperforms existing edge enhancement methods in terms of objective criteria and subjective visual perception of the actual X-ray fluoroscopic image. The developed edge enhancement algorithm performed well when applied to actual low-dose X-ray fluoroscopic images, not only by improving the sharpness, but also by removing artifacts and noise, including overshoot. Copyright © 2017 Elsevier B.V. All rights reserved.

Speckle-reduction algorithm for ultrasound images in complex wavelet domain using genetic algorithm-based mixture model.

PubMed

Uddin, Muhammad Shahin; Tahtali, Murat; Lambert, Andrew J; Pickering, Mark R; Marchese, Margaret; Stuart, Iain

2016-05-20

Compared with other medical-imaging modalities, ultrasound (US) imaging is a valuable way to examine the body's internal organs, and two-dimensional (2D) imaging is currently the most common technique used in clinical diagnoses. Conventional 2D US imaging systems are highly flexible cost-effective imaging tools that permit operators to observe and record images of a large variety of thin anatomical sections in real time. Recently, 3D US imaging has also been gaining popularity due to its considerable advantages over 2D US imaging. It reduces dependency on the operator and provides better qualitative and quantitative information for an effective diagnosis. Furthermore, it provides a 3D view, which allows the observation of volume information. The major shortcoming of any type of US imaging is the presence of speckle noise. Hence, speckle reduction is vital in providing a better clinical diagnosis. The key objective of any speckle-reduction algorithm is to attain a speckle-free image while preserving the important anatomical features. In this paper we introduce a nonlinear multi-scale complex wavelet-diffusion based algorithm for speckle reduction and sharp-edge preservation of 2D and 3D US images. In the proposed method we use a Rayleigh and Maxwell-mixture model for 2D and 3D US images, respectively, where a genetic algorithm is used in combination with an expectation maximization method to estimate mixture parameters. Experimental results using both 2D and 3D synthetic, physical phantom, and clinical data demonstrate that our proposed algorithm significantly reduces speckle noise while preserving sharp edges without discernible distortions. The proposed approach performs better than the state-of-the-art approaches in both qualitative and quantitative measures.

A retention-time-shift-tolerant background subtraction and noise reduction algorithm (BgS-NoRA) for extraction of drug metabolites in liquid chromatography/mass spectrometry data from biological matrices.

PubMed

Zhu, Peijuan; Ding, Wei; Tong, Wei; Ghosal, Anima; Alton, Kevin; Chowdhury, Swapan

2009-06-01

A retention-time-shift-tolerant background subtraction and noise reduction algorithm (BgS-NoRA) is implemented using the statistical programming language R to remove non-drug-related ion signals from accurate mass liquid chromatography/mass spectrometry (LC/MS) data. The background-subtraction part of the algorithm is similar to a previously published procedure (Zhang H and Yang Y. J. Mass Spectrom. 2008, 43: 1181-1190). The noise reduction algorithm (NoRA) is an add-on feature to help further clean up the residual matrix ion noises after background subtraction. It functions by removing ion signals that are not consistent across many adjacent scans. The effectiveness of BgS-NoRA was examined in biological matrices by spiking blank plasma extract, bile and urine with diclofenac and ibuprofen that have been pre-metabolized by microsomal incubation. Efficient removal of background ions permitted the detection of drug-related ions in in vivo samples (plasma, bile, urine and feces) obtained from rats orally dosed with (14)C-loratadine with minimal interference. Results from these experiments demonstrate that BgS-NoRA is more effective in removing analyte-unrelated ions than background subtraction alone. NoRA is shown to be particularly effective in the early retention region for urine samples and middle retention region for bile samples, where the matrix ion signals still dominate the total ion chromatograms (TICs) after background subtraction. In most cases, the TICs after BgS-NoRA are in excellent qualitative correlation to the radiochromatograms. BgS-NoRA will be a very useful tool in metabolite detection and identification work, especially in first-in-human (FIH) studies and multiple dose toxicology studies where non-radio-labeled drugs are administered. Data from these types of studies are critical to meet the latest FDA guidance on Metabolite in Safety Testing (MIST). Copyright (c) 2009 John Wiley & Sons, Ltd.

Active noise control for infant incubators.

PubMed

Yu, Xun; Gujjula, Shruthi; Kuo, Sen M

2009-01-01

This paper presents an active noise control system for infant incubators. Experimental results show that global noise reduction can be achieved for infant incubator ANC systems. An audio-integration algorithm is presented to introduce a healthy audio (intrauterine) sound with the ANC system to mask the residual noise and soothe the infant. Carbon nanotube based transparent thin film speaker is also introduced in this paper as the actuator for the ANC system to generate the destructive secondary sound, which can significantly save the congested incubator space and without blocking the view of doctors and nurses.

Optimization of actuator arrays for aircraft interior noise control

NASA Technical Reports Server (NTRS)

Cabell, R. H.; Lester, H. C.; Mathur, G. P.; Tran, B. N.

1993-01-01

A numerical procedure for grouping actuators in order to reduce the number of degrees of freedom in an active noise control system is evaluated using experimental data. Piezoceramic actuators for reducing aircraft interior noise are arranged into groups using a nonlinear optimization routine and clustering algorithm. An actuator group is created when two or more actuators are driven with the same control input. This procedure is suitable for active control applications where actuators are already mounted on a structure. The feasibility of this technique is demonstrated using measured data from the aft cabin of a Douglas DC-9 fuselage. The measured data include transfer functions between 34 piezoceramic actuators and 29 interior microphones and microphone responses due to the primary noise produced by external speakers. Control inputs for the grouped actuators were calculated so that a cost function, defined as a quadratic pressure term and a penalty term, was a minimum. The measured transfer functions and microphone responses are checked by comparing calculated noise reductions with measured noise reductions for four frequencies. The grouping procedure is then used to determine actuator groups that improve overall interior noise reductions by 5.3 to 15 dB, compared to the baseline experimental configuration.

Nonlocal Total-Variation-Based Speckle Filtering for Ultrasound Images.

PubMed

Wen, Tiexiang; Gu, Jia; Li, Ling; Qin, Wenjian; Wang, Lei; Xie, Yaoqin

2016-07-01

Ultrasound is one of the most important medical imaging modalities for its real-time and portable imaging advantages. However, the contrast resolution and important details are degraded by the speckle in ultrasound images. Many speckle filtering methods have been developed, but they are suffered from several limitations, difficult to reach a balance between speckle reduction and edge preservation. In this paper, an adaptation of the nonlocal total variation (NLTV) filter is proposed for speckle reduction in ultrasound images. The speckle is modeled via a signal-dependent noise distribution for the log-compressed ultrasound images. Instead of the Euclidian distance, the statistical Pearson distance is introduced in this study for the similarity calculation between image patches via the Bayesian framework. And the Split-Bregman fast algorithm is used to solve the adapted NLTV despeckling functional. Experimental results on synthetic and clinical ultrasound images and comparisons with some classical and recent algorithms are used to demonstrate its improvements in both speckle noise reduction and tissue boundary preservation for ultrasound images. © The Author(s) 2015.

Neurodynamic evaluation of hearing aid features using EEG correlates of listening effort.

PubMed

Bernarding, Corinna; Strauss, Daniel J; Hannemann, Ronny; Seidler, Harald; Corona-Strauss, Farah I

2017-06-01

In this study, we propose a novel estimate of listening effort using electroencephalographic data. This method is a translation of our past findings, gained from the evoked electroencephalographic activity, to the oscillatory EEG activity. To test this technique, electroencephalographic data from experienced hearing aid users with moderate hearing loss were recorded, wearing hearing aids. The investigated hearing aid settings were: a directional microphone combined with a noise reduction algorithm in a medium and a strong setting, the noise reduction setting turned off, and a setting using omnidirectional microphones without any noise reduction. The results suggest that the electroencephalographic estimate of listening effort seems to be a useful tool to map the exerted effort of the participants. In addition, the results indicate that a directional processing mode can reduce the listening effort in multitalker listening situations.

A generalized time-frequency subtraction method for robust speech enhancement based on wavelet filter banks modeling of human auditory system.

PubMed

Shao, Yu; Chang, Chip-Hong

2007-08-01

We present a new speech enhancement scheme for a single-microphone system to meet the demand for quality noise reduction algorithms capable of operating at a very low signal-to-noise ratio. A psychoacoustic model is incorporated into the generalized perceptual wavelet denoising method to reduce the residual noise and improve the intelligibility of speech. The proposed method is a generalized time-frequency subtraction algorithm, which advantageously exploits the wavelet multirate signal representation to preserve the critical transient information. Simultaneous masking and temporal masking of the human auditory system are modeled by the perceptual wavelet packet transform via the frequency and temporal localization of speech components. The wavelet coefficients are used to calculate the Bark spreading energy and temporal spreading energy, from which a time-frequency masking threshold is deduced to adaptively adjust the subtraction parameters of the proposed method. An unvoiced speech enhancement algorithm is also integrated into the system to improve the intelligibility of speech. Through rigorous objective and subjective evaluations, it is shown that the proposed speech enhancement system is capable of reducing noise with little speech degradation in adverse noise environments and the overall performance is superior to several competitive methods.

A new automated assessment method for contrast-detail images by applying support vector machine and its robustness to nonlinear image processing.

PubMed

Takei, Takaaki; Ikeda, Mitsuru; Imai, Kuniharu; Yamauchi-Kawaura, Chiyo; Kato, Katsuhiko; Isoda, Haruo

2013-09-01

The automated contrast-detail (C-D) analysis methods developed so-far cannot be expected to work well on images processed with nonlinear methods, such as noise reduction methods. Therefore, we have devised a new automated C-D analysis method by applying support vector machine (SVM), and tested for its robustness to nonlinear image processing. We acquired the CDRAD (a commercially available C-D test object) images at a tube voltage of 120 kV and a milliampere-second product (mAs) of 0.5-5.0. A partial diffusion equation based technique was used as noise reduction method. Three radiologists and three university students participated in the observer performance study. The training data for our SVM method was the classification data scored by the one radiologist for the CDRAD images acquired at 1.6 and 3.2 mAs and their noise-reduced images. We also compared the performance of our SVM method with the CDRAD Analyser algorithm. The mean C-D diagrams (that is a plot of the mean of the smallest visible hole diameter vs. hole depth) obtained from our devised SVM method agreed well with the ones averaged across the six human observers for both original and noise-reduced CDRAD images, whereas the mean C-D diagrams from the CDRAD Analyser algorithm disagreed with the ones from the human observers for both original and noise-reduced CDRAD images. In conclusion, our proposed SVM method for C-D analysis will work well for the images processed with the non-linear noise reduction method as well as for the original radiographic images.

Active Structural Acoustic Control of Interior Noise on a Raytheon 1900D

NASA Technical Reports Server (NTRS)

Palumbo, Dan; Cabell, Ran; Sullivan, Brenda; Cline, John

2000-01-01

An active structural acoustic control system has been demonstrated on a Raytheon Aircraft Company 1900D turboprop airliner. Both single frequency and multi-frequency control of the blade passage frequency and its harmonics was accomplished. The control algorithm was a variant of the popular filtered-x LMS implemented in the principal component domain. The control system consisted of 21 inertial actuators and 32 microphones. The actuators were mounted to the aircraft's ring frames. The microphones were distributed uniformly throughout the interior at head height, both seated and standing. Actuator locations were selected using a combinatorial search optimization algorithm. The control system achieved a 14 dB noise reduction of the blade passage frequency during single frequency tests. Multi-frequency control of the first 1st, 2nd and 3rd harmonics resulted in 10.2 dB, 3.3 dB and 1.6 dB noise reductions respectively. These results fall short of the predictions which were produced by the optimization algorithm (13.5 dB, 8.6 dB and 6.3 dB). The optimization was based on actuator transfer functions taken on the ground and it is postulated that cabin pressurization at flight altitude was a factor in this discrepancy.

Gaussian diffusion sinogram inpainting for X-ray CT metal artifact reduction.

PubMed

Peng, Chengtao; Qiu, Bensheng; Li, Ming; Guan, Yihui; Zhang, Cheng; Wu, Zhongyi; Zheng, Jian

2017-01-05

Metal objects implanted in the bodies of patients usually generate severe streaking artifacts in reconstructed images of X-ray computed tomography, which degrade the image quality and affect the diagnosis of disease. Therefore, it is essential to reduce these artifacts to meet the clinical demands. In this work, we propose a Gaussian diffusion sinogram inpainting metal artifact reduction algorithm based on prior images to reduce these artifacts for fan-beam computed tomography reconstruction. In this algorithm, prior information that originated from a tissue-classified prior image is used for the inpainting of metal-corrupted projections, and it is incorporated into a Gaussian diffusion function. The prior knowledge is particularly designed to locate the diffusion position and improve the sparsity of the subtraction sinogram, which is obtained by subtracting the prior sinogram of the metal regions from the original sinogram. The sinogram inpainting algorithm is implemented through an approach of diffusing prior energy and is then solved by gradient descent. The performance of the proposed metal artifact reduction algorithm is compared with two conventional metal artifact reduction algorithms, namely the interpolation metal artifact reduction algorithm and normalized metal artifact reduction algorithm. The experimental datasets used included both simulated and clinical datasets. By evaluating the results subjectively, the proposed metal artifact reduction algorithm causes fewer secondary artifacts than the two conventional metal artifact reduction algorithms, which lead to severe secondary artifacts resulting from impertinent interpolation and normalization. Additionally, the objective evaluation shows the proposed approach has the smallest normalized mean absolute deviation and the highest signal-to-noise ratio, indicating that the proposed method has produced the image with the best quality. No matter for the simulated datasets or the clinical datasets, the proposed algorithm has reduced the metal artifacts apparently.

Iterative raw measurements restoration method with penalized weighted least squares approach for low-dose CT

NASA Astrophysics Data System (ADS)

Takahashi, Hisashi; Goto, Taiga; Hirokawa, Koichi; Miyazaki, Osamu

2014-03-01

Statistical iterative reconstruction and post-log data restoration algorithms for CT noise reduction have been widely studied and these techniques have enabled us to reduce irradiation doses while maintaining image qualities. In low dose scanning, electronic noise becomes obvious and it results in some non-positive signals in raw measurements. The nonpositive signal should be converted to positive signal so that it can be log-transformed. Since conventional conversion methods do not consider local variance on the sinogram, they have difficulty of controlling the strength of the filtering. Thus, in this work, we propose a method to convert the non-positive signal to the positive signal by mainly controlling the local variance. The method is implemented in two separate steps. First, an iterative restoration algorithm based on penalized weighted least squares is used to mitigate the effect of electronic noise. The algorithm preserves the local mean and reduces the local variance induced by the electronic noise. Second, smoothed raw measurements by the iterative algorithm are converted to the positive signal according to a function which replaces the non-positive signal with its local mean. In phantom studies, we confirm that the proposed method properly preserves the local mean and reduce the variance induced by the electronic noise. Our technique results in dramatically reduced shading artifacts and can also successfully cooperate with the post-log data filter to reduce streak artifacts.

Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsantis, Stavros; Spiliopoulos, Stavros; Karnabatidis, Dimitrios

Purpose: Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. Methods: The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. Results: A totalmore » of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists’ qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. Conclusions: A new wavelet-based EFCM clustering model was introduced toward noise reduction and detail preservation. The proposed method improves the overall US image quality, which in turn could affect the decision-making on whether additional imaging and/or intervention is needed.« less

Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction.

PubMed

Tsantis, Stavros; Spiliopoulos, Stavros; Skouroliakou, Aikaterini; Karnabatidis, Dimitrios; Hazle, John D; Kagadis, George C

2014-07-01

Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. A total of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists' qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. A new wavelet-based EFCM clustering model was introduced toward noise reduction and detail preservation. The proposed method improves the overall US image quality, which in turn could affect the decision-making on whether additional imaging and/or intervention is needed.

Maximizing Iodine Contrast-to-Noise Ratios in Abdominal CT Imaging through Use of Energy Domain Noise Reduction and Virtual Monoenergetic Dual-Energy CT.

PubMed

Leng, Shuai; Yu, Lifeng; Fletcher, Joel G; McCollough, Cynthia H

2015-08-01

To determine the iodine contrast-to-noise ratio (CNR) for abdominal computed tomography (CT) when using energy domain noise reduction and virtual monoenergetic dual-energy (DE) CT images and to compare the CNR to that attained with single-energy CT at 80, 100, 120, and 140 kV. This HIPAA-compliant study was approved by the institutional review board with waiver of informed consent. A syringe filled with diluted iodine contrast material was placed into 30-, 35-, and 45-cm-wide water phantoms and scanned with a dual-source CT scanner in both DE and single-energy modes with matched scanner output. Virtual monoenergetic images were generated, with energies ranging from 40 to 110 keV in 10-keV steps. A previously developed energy domain noise reduction algorithm was applied to reduce image noise by exploiting information redundancies in the energy domain. Image noise and iodine CNR were calculated. To show the potential clinical benefit of this technique, it was retrospectively applied to a clinical DE CT study of the liver in a 59-year-old male patient by using conventional and iterative reconstruction techniques. Image noise and CNR were compared for virtual monoenergetic images with and without energy domain noise reduction at each virtual monoenergetic energy (in kiloelectron volts) and phantom size by using a paired t test. CNR of virtual monoenergetic images was also compared with that of single-energy images acquired with 80, 100, 120, and 140 kV. Noise reduction of up to 59% (28.7 of 65.7) was achieved for DE virtual monoenergetic images by using an energy domain noise reduction technique. For the commercial virtual monoenergetic images, the maximum iodine CNR was achieved at 70 keV and was 18.6, 16.6, and 10.8 for the 30-, 35-, and 45-cm phantoms. After energy domain noise reduction, maximum iodine CNR was achieved at 40 keV and increased to 30.6, 25.4, and 16.5. These CNRs represented improvement of up to 64% (12.0 of 18.6) with the energy domain noise reduction technique. For single-energy CT at the optimal tube potential, iodine CNR was 29.1 (80 kV), 21.2 (80 kV), and 11.5 (100 kV). For patient images, 39% (24 of 61) noise reduction and 67% (0.74 of 1.10) CNR improvement were observed with the energy domain noise reduction technique when compared with standard filtered back-projection images. Iodine CNR for adult abdominal CT may be maximized with energy domain noise reduction and virtual monoenergetic DE CT. (©) RSNA, 2015.

Quantitative evaluation of ASiR image quality: an adaptive statistical iterative reconstruction technique

NASA Astrophysics Data System (ADS)

Van de Casteele, Elke; Parizel, Paul; Sijbers, Jan

2012-03-01

Adaptive statistical iterative reconstruction (ASiR) is a new reconstruction algorithm used in the field of medical X-ray imaging. This new reconstruction method combines the idealized system representation, as we know it from the standard Filtered Back Projection (FBP) algorithm, and the strength of iterative reconstruction by including a noise model in the reconstruction scheme. It studies how noise propagates through the reconstruction steps, feeds this model back into the loop and iteratively reduces noise in the reconstructed image without affecting spatial resolution. In this paper the effect of ASiR on the contrast to noise ratio is studied using the low contrast module of the Catphan phantom. The experiments were done on a GE LightSpeed VCT system at different voltages and currents. The results show reduced noise and increased contrast for the ASiR reconstructions compared to the standard FBP method. For the same contrast to noise ratio the images from ASiR can be obtained using 60% less current, leading to a reduction in dose of the same amount.

Artifact reduction of different metallic implants in flat detector C-arm CT.

PubMed

Hung, S-C; Wu, C-C; Lin, C-J; Guo, W-Y; Luo, C-B; Chang, F-C; Chang, C-Y

2014-07-01

Flat detector CT has been increasingly used as a follow-up examination after endovascular intervention. Metal artifact reduction has been successfully demonstrated in coil mass cases, but only in a small series. We attempted to objectively and subjectively evaluate the feasibility of metal artifact reduction with various metallic objects and coil lengths. We retrospectively reprocessed the flat detector CT data of 28 patients (15 men, 13 women; mean age, 55.6 years) after they underwent endovascular treatment (20 coiling ± stent placement, 6 liquid embolizers) or shunt drainage (n = 2) between January 2009 and November 2011 by using a metal artifact reduction correction algorithm. We measured CT value ranges and noise by using region-of-interest methods, and 2 experienced neuroradiologists rated the degrees of improved imaging quality and artifact reduction by comparing uncorrected and corrected images. After we applied the metal artifact reduction algorithm, the CT value ranges and the noise were substantially reduced (1815.3 ± 793.7 versus 231.7 ± 95.9 and 319.9 ± 136.6 versus 45.9 ± 14.0; both P < .001) regardless of the types of metallic objects and various sizes of coil masses. The rater study achieved an overall improvement of imaging quality and artifact reduction (85.7% and 78.6% of cases by 2 raters, respectively), with the greatest improvement in the coiling group, moderate improvement in the liquid embolizers, and the smallest improvement in ventricular shunting (overall agreement, 0.857). The metal artifact reduction algorithm substantially reduced artifacts and improved the objective image quality in every studied case. It also allowed improved diagnostic confidence in most cases. © 2014 by American Journal of Neuroradiology.

A new axial smoothing method based on elastic mapping

NASA Astrophysics Data System (ADS)

Yang, J.; Huang, S. C.; Lin, K. P.; Czernin, J.; Wolfenden, P.; Dahlbom, M.; Hoh, C. K.; Phelps, M. E.

1996-12-01

New positron emission tomography (PET) scanners have higher axial and in-plane spatial resolutions but at the expense of reduced per plane sensitivity, which prevents the higher resolution from being fully realized. Normally, Gaussian-weighted interplane axial smoothing is used to reduce noise. In this study, the authors developed a new algorithm that first elastically maps adjacent planes, and then the mapped images are smoothed axially to reduce the image noise level. Compared to those obtained by the conventional axial-directional smoothing method, the images by the new method have improved signal-to-noise ratio. To quantify the signal-to-noise improvement, both simulated and real cardiac PET images were studied. Various Hanning reconstruction filters with cutoff frequency=0.5, 0.7, 1.0/spl times/Nyquist frequency and Ramp filter were tested on simulated images. Effective in-plane resolution was measured by the effective global Gaussian resolution (EGGR) and noise reduction was evaluated by the cross-correlation coefficient. Results showed that the new method was robust to various noise levels and indicated larger noise reduction or better image feature preservation (i.e., smaller EGGR) than by the conventional method.

On the importance of preserving the harmonics and neighboring partials prior to vocoder processing: implications for cochlear implants.

PubMed

Hu, Yi; Loizou, Philipos C

2010-01-01

Pre-processing based noise-reduction algorithms used for cochlear implants (CIs) can sometimes introduce distortions which are carried through the vocoder stages of CI processing. While the background noise may be notably suppressed, the harmonic structure and/or spectral envelope of the signal may be distorted. The present study investigates the potential of preserving the signal's harmonic structure in voiced segments (e.g., vowels) as a means of alleviating the negative effects of pre-processing. The hypothesis tested is that preserving the harmonic structure of the signal is crucial for subsequent vocoder processing. The implications of preserving either the main harmonic components occurring at multiples of F0 or the main harmonics along with adjacent partials are investigated. This is done by first pre-processing noisy speech with a conventional noise-reduction algorithm, regenerating the harmonics, and vocoder processing the stimuli with eight channels of stimulation in steady speech-shaped noise. Results indicated that preserving the main low-frequency harmonics (spanning 1 or 3 kHz) alone was not beneficial. Preserving, however, the harmonic structure of the stimulus, i.e., the main harmonics along with the adjacent partials, was found to be critically important and provided substantial improvements (41 percentage points) in intelligibility.

An active structural acoustic control approach for the reduction of the structure-borne road noise

NASA Astrophysics Data System (ADS)

Douville, Hugo; Berry, Alain; Masson, Patrice

2002-11-01

The reduction of the structure-borne road noise generated inside the cabin of an automobile is investigated using an Active Structural Acoustic Control (ASAC) approach. First, a laboratory test bench consisting of a wheel/suspension/lower suspension A-arm assembly has been developed in order to identify the vibroacoustic transfer paths (up to 250 Hz) for realistic road noise excitation of the wheel. Frequency Response Function (FRF) measurements between the excitation/control actuators and each suspension/chassis linkage are used to characterize the different transfer paths that transmit energy through the chassis of the car. Second, a FE/BE model (Finite/Boundary Elements) was developed to simulate the acoustic field of an automobile cab interior. This model is used to predict the acoustic field inside the cabin as a response to the measured forces applied on the suspension/chassis linkages. Finally, an experimental implementation of ASAC is presented. The control approach relies on the use of inertial actuators to modify the vibration behavior of the suspension and the automotive chassis such that its noise radiation efficiency is decreased. The implemented algorithm consists of a MIMO (Multiple-Input-Multiple-Output) feedforward configuration with a filtered-X LMS algorithm using an advanced reference signal (width FIR filters) using the Simulink/Dspace environment for control prototyping.

Speech understanding in background noise with the two-microphone adaptive beamformer BEAM in the Nucleus Freedom Cochlear Implant System.

PubMed

Spriet, Ann; Van Deun, Lieselot; Eftaxiadis, Kyriaky; Laneau, Johan; Moonen, Marc; van Dijk, Bas; van Wieringen, Astrid; Wouters, Jan

2007-02-01

This paper evaluates the benefit of the two-microphone adaptive beamformer BEAM in the Nucleus Freedom cochlear implant (CI) system for speech understanding in background noise by CI users. A double-blind evaluation of the two-microphone adaptive beamformer BEAM and a hardware directional microphone was carried out with five adult Nucleus CI users. The test procedure consisted of a pre- and post-test in the lab and a 2-wk trial period at home. In the pre- and post-test, the speech reception threshold (SRT) with sentences and the percentage correct phoneme scores for CVC words were measured in quiet and background noise at different signal-to-noise ratios. Performance was assessed for two different noise configurations (with a single noise source and with three noise sources) and two different noise materials (stationary speech-weighted noise and multitalker babble). During the 2-wk trial period at home, the CI users evaluated the noise reduction performance in different listening conditions by means of the SSQ questionnaire. In addition to the perceptual evaluation, the noise reduction performance of the beamformer was measured physically as a function of the direction of the noise source. Significant improvements of both the SRT in noise (average improvement of 5-16 dB) and the percentage correct phoneme scores (average improvement of 10-41%) were observed with BEAM compared to the standard hardware directional microphone. In addition, the SSQ questionnaire and subjective evaluation in controlled and real-life scenarios suggested a possible preference for the beamformer in noisy environments. The evaluation demonstrates that the adaptive noise reduction algorithm BEAM in the Nucleus Freedom CI-system may significantly increase the speech perception by cochlear implantees in noisy listening conditions. This is the first monolateral (adaptive) noise reduction strategy actually implemented in a mainstream commercial CI.

Spatial independent component analysis of functional MRI time-series: to what extent do results depend on the algorithm used?

PubMed

Esposito, Fabrizio; Formisano, Elia; Seifritz, Erich; Goebel, Rainer; Morrone, Renato; Tedeschi, Gioacchino; Di Salle, Francesco

2002-07-01

Independent component analysis (ICA) has been successfully employed to decompose functional MRI (fMRI) time-series into sets of activation maps and associated time-courses. Several ICA algorithms have been proposed in the neural network literature. Applied to fMRI, these algorithms might lead to different spatial or temporal readouts of brain activation. We compared the two ICA algorithms that have been used so far for spatial ICA (sICA) of fMRI time-series: the Infomax (Bell and Sejnowski [1995]: Neural Comput 7:1004-1034) and the Fixed-Point (Hyvärinen [1999]: Adv Neural Inf Proc Syst 10:273-279) algorithms. We evaluated the Infomax- and Fixed Point-based sICA decompositions of simulated motor, and real motor and visual activation fMRI time-series using an ensemble of measures. Log-likelihood (McKeown et al. [1998]: Hum Brain Mapp 6:160-188) was used as a measure of how significantly the estimated independent sources fit the statistical structure of the data; receiver operating characteristics (ROC) and linear correlation analyses were used to evaluate the algorithms' accuracy of estimating the spatial layout and the temporal dynamics of simulated and real activations; cluster sizing calculations and an estimation of a residual gaussian noise term within the components were used to examine the anatomic structure of ICA components and for the assessment of noise reduction capabilities. Whereas both algorithms produced highly accurate results, the Fixed-Point outperformed the Infomax in terms of spatial and temporal accuracy as long as inferential statistics were employed as benchmarks. Conversely, the Infomax sICA was superior in terms of global estimation of the ICA model and noise reduction capabilities. Because of its adaptive nature, the Infomax approach appears to be better suited to investigate activation phenomena that are not predictable or adequately modelled by inferential techniques. Copyright 2002 Wiley-Liss, Inc.

The Effects of Hearing Aid Directional Microphone and Noise Reduction Processing on Listening Effort in Older Adults with Hearing Loss.

PubMed

Desjardins, Jamie L

2016-01-01

Older listeners with hearing loss may exert more cognitive resources to maintain a level of listening performance similar to that of younger listeners with normal hearing. Unfortunately, this increase in cognitive load, which is often conceptualized as increased listening effort, may come at the cost of cognitive processing resources that might otherwise be available for other tasks. The purpose of this study was to evaluate the independent and combined effects of a hearing aid directional microphone and a noise reduction (NR) algorithm on reducing the listening effort older listeners with hearing loss expend on a speech-in-noise task. Participants were fitted with study worn commercially available behind-the-ear hearing aids. Listening effort on a sentence recognition in noise task was measured using an objective auditory-visual dual-task paradigm. The primary task required participants to repeat sentences presented in quiet and in a four-talker babble. The secondary task was a digital visual pursuit rotor-tracking test, for which participants were instructed to use a computer mouse to track a moving target around an ellipse that was displayed on a computer screen. Each of the two tasks was presented separately and concurrently at a fixed overall speech recognition performance level of 50% correct with and without the directional microphone and/or the NR algorithm activated in the hearing aids. In addition, participants reported how effortful it was to listen to the sentences in quiet and in background noise in the different hearing aid listening conditions. Fifteen older listeners with mild sloping to severe sensorineural hearing loss participated in this study. Listening effort in background noise was significantly reduced with the directional microphones activated in the hearing aids. However, there was no significant change in listening effort with the hearing aid NR algorithm compared to no noise processing. Correlation analysis between objective and self-reported ratings of listening effort showed no significant relation. Directional microphone processing effectively reduced the cognitive load of listening to speech in background noise. This is significant because it is likely that listeners with hearing impairment will frequently encounter noisy speech in their everyday communications. American Academy of Audiology.

X-ray dose reduction in abdominal computed tomography using advanced iterative reconstruction algorithms.

PubMed

Ning, Peigang; Zhu, Shaocheng; Shi, Dapeng; Guo, Ying; Sun, Minghua

2014-01-01

This work aims to explore the effects of adaptive statistical iterative reconstruction (ASiR) and model-based iterative reconstruction (MBIR) algorithms in reducing computed tomography (CT) radiation dosages in abdominal imaging. CT scans on a standard male phantom were performed at different tube currents. Images at the different tube currents were reconstructed with the filtered back-projection (FBP), 50% ASiR and MBIR algorithms and compared. The CT value, image noise and contrast-to-noise ratios (CNRs) of the reconstructed abdominal images were measured. Volumetric CT dose indexes (CTDIvol) were recorded. At different tube currents, 50% ASiR and MBIR significantly reduced image noise and increased the CNR when compared with FBP. The minimal tube current values required by FBP, 50% ASiR, and MBIR to achieve acceptable image quality using this phantom were 200, 140, and 80 mA, respectively. At the identical image quality, 50% ASiR and MBIR reduced the radiation dose by 35.9% and 59.9% respectively when compared with FBP. Advanced iterative reconstruction techniques are able to reduce image noise and increase image CNRs. Compared with FBP, 50% ASiR and MBIR reduced radiation doses by 35.9% and 59.9%, respectively.

Speckle noise reduction technique for Lidar echo signal based on self-adaptive pulse-matching independent component analysis

NASA Astrophysics Data System (ADS)

Xu, Fan; Wang, Jiaxing; Zhu, Daiyin; Tu, Qi

2018-04-01

Speckle noise has always been a particularly tricky problem in improving the ranging capability and accuracy of Lidar system especially in harsh environment. Currently, effective speckle de-noising techniques are extremely scarce and should be further developed. In this study, a speckle noise reduction technique has been proposed based on independent component analysis (ICA). Since normally few changes happen in the shape of laser pulse itself, the authors employed the laser source as a reference pulse and executed the ICA decomposition to find the optimal matching position. In order to achieve the self-adaptability of algorithm, local Mean Square Error (MSE) has been defined as an appropriate criterion for investigating the iteration results. The obtained experimental results demonstrated that the self-adaptive pulse-matching ICA (PM-ICA) method could effectively decrease the speckle noise and recover the useful Lidar echo signal component with high quality. Especially, the proposed method achieves 4 dB more improvement of signal-to-noise ratio (SNR) than a traditional homomorphic wavelet method.

Tradeoff between noise reduction and inartificial visualization in a model-based iterative reconstruction algorithm on coronary computed tomography angiography.

PubMed

Hirata, Kenichiro; Utsunomiya, Daisuke; Kidoh, Masafumi; Funama, Yoshinori; Oda, Seitaro; Yuki, Hideaki; Nagayama, Yasunori; Iyama, Yuji; Nakaura, Takeshi; Sakabe, Daisuke; Tsujita, Kenichi; Yamashita, Yasuyuki

2018-05-01

We aimed to evaluate the image quality performance of coronary CT angiography (CTA) under the different settings of forward-projected model-based iterative reconstruction solutions (FIRST).Thirty patients undergoing coronary CTA were included. Each image was reconstructed using filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR-3D), and 2 model-based iterative reconstructions including FIRST-body and FIRST-cardiac sharp (CS). CT number and noise were measured in the coronary vessels and plaque. Subjective image-quality scores were obtained for noise and structure visibility.In the objective image analysis, FIRST-body produced the significantly highest contrast-to-noise ratio. Regarding subjective image quality, FIRST-CS had the highest score for structure visibility, although the image noise score was inferior to that of FIRST-body.In conclusion, FIRST provides significant improvements in objective and subjective image quality compared with FBP and AIDR-3D. FIRST-body effectively reduces image noise, but the structure visibility with FIRST-CS was superior to FIRST-body.

Iterative Reconstruction Techniques in Abdominopelvic CT: Technical Concepts and Clinical Implementation.

PubMed

Patino, Manuel; Fuentes, Jorge M; Singh, Sarabjeet; Hahn, Peter F; Sahani, Dushyant V

2015-07-01

This article discusses the clinical challenge of low-radiation-dose examinations, the commonly used approaches for dose optimization, and their effect on image quality. We emphasize practical aspects of the different iterative reconstruction techniques, along with their benefits, pitfalls, and clinical implementation. The widespread use of CT has raised concerns about potential radiation risks, motivating diverse strategies to reduce the radiation dose associated with CT. CT manufacturers have developed alternative reconstruction algorithms intended to improve image quality on dose-optimized CT studies, mainly through noise and artifact reduction. Iterative reconstruction techniques take unique approaches to noise reduction and provide distinct strength levels or settings.

Spectral Prior Image Constrained Compressed Sensing (Spectral PICCS) for Photon-Counting Computed Tomography

PubMed Central

Yu, Zhicong; Leng, Shuai; Li, Zhoubo; McCollough, Cynthia H.

2016-01-01

Photon-counting computed tomography (PCCT) is an emerging imaging technique that enables multi-energy imaging with only a single scan acquisition. To enable multi-energy imaging, the detected photons corresponding to the full x-ray spectrum are divided into several subgroups of bin data that correspond to narrower energy windows. Consequently, noise in each energy bin increases compared to the full-spectrum data. This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging. The algorithm is based on the framework of prior image constrained compressed sensing (PICCS) and is called spectral PICCS; it uses the full-spectrum image reconstructed using conventional filtered back-projection as the prior image. The spectral PICCS algorithm is implemented using a constrained optimization scheme with adaptive iterative step sizes such that only two tuning parameters are required in most cases. The algorithm was first evaluated using computer simulations, and then validated by both physical phantoms and in-vivo swine studies using a research PCCT system. Results from both computer-simulation and experimental studies showed substantial image noise reduction in narrow energy bins (43~73%) without sacrificing CT number accuracy or spatial resolution. PMID:27551878

Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography

NASA Astrophysics Data System (ADS)

Yu, Zhicong; Leng, Shuai; Li, Zhoubo; McCollough, Cynthia H.

2016-09-01

Photon-counting computed tomography (PCCT) is an emerging imaging technique that enables multi-energy imaging with only a single scan acquisition. To enable multi-energy imaging, the detected photons corresponding to the full x-ray spectrum are divided into several subgroups of bin data that correspond to narrower energy windows. Consequently, noise in each energy bin increases compared to the full-spectrum data. This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging. The algorithm is based on the framework of prior image constrained compressed sensing (PICCS) and is called spectral PICCS; it uses the full-spectrum image reconstructed using conventional filtered back-projection as the prior image. The spectral PICCS algorithm is implemented using a constrained optimization scheme with adaptive iterative step sizes such that only two tuning parameters are required in most cases. The algorithm was first evaluated using computer simulations, and then validated by both physical phantoms and in vivo swine studies using a research PCCT system. Results from both computer-simulation and experimental studies showed substantial image noise reduction in narrow energy bins (43-73%) without sacrificing CT number accuracy or spatial resolution.

Recursive algorithms for bias and gain nonuniformity correction in infrared videos.

PubMed

Pipa, Daniel R; da Silva, Eduardo A B; Pagliari, Carla L; Diniz, Paulo S R

2012-12-01

Infrared focal-plane array (IRFPA) detectors suffer from fixed-pattern noise (FPN) that degrades image quality, which is also known as spatial nonuniformity. FPN is still a serious problem, despite recent advances in IRFPA technology. This paper proposes new scene-based correction algorithms for continuous compensation of bias and gain nonuniformity in FPA sensors. The proposed schemes use recursive least-square and affine projection techniques that jointly compensate for both the bias and gain of each image pixel, presenting rapid convergence and robustness to noise. The synthetic and real IRFPA videos experimentally show that the proposed solutions are competitive with the state-of-the-art in FPN reduction, by presenting recovered images with higher fidelity.

An Efficient Augmented Lagrangian Method for Statistical X-Ray CT Image Reconstruction.

PubMed

Li, Jiaojiao; Niu, Shanzhou; Huang, Jing; Bian, Zhaoying; Feng, Qianjin; Yu, Gaohang; Liang, Zhengrong; Chen, Wufan; Ma, Jianhua

2015-01-01

Statistical iterative reconstruction (SIR) for X-ray computed tomography (CT) under the penalized weighted least-squares criteria can yield significant gains over conventional analytical reconstruction from the noisy measurement. However, due to the nonlinear expression of the objective function, most exiting algorithms related to the SIR unavoidably suffer from heavy computation load and slow convergence rate, especially when an edge-preserving or sparsity-based penalty or regularization is incorporated. In this work, to address abovementioned issues of the general algorithms related to the SIR, we propose an adaptive nonmonotone alternating direction algorithm in the framework of augmented Lagrangian multiplier method, which is termed as "ALM-ANAD". The algorithm effectively combines an alternating direction technique with an adaptive nonmonotone line search to minimize the augmented Lagrangian function at each iteration. To evaluate the present ALM-ANAD algorithm, both qualitative and quantitative studies were conducted by using digital and physical phantoms. Experimental results show that the present ALM-ANAD algorithm can achieve noticeable gains over the classical nonlinear conjugate gradient algorithm and state-of-the-art split Bregman algorithm in terms of noise reduction, contrast-to-noise ratio, convergence rate, and universal quality index metrics.

Characterization of a CT unit for the detection of low contrast structures

NASA Astrophysics Data System (ADS)

Viry, Anais; Racine, Damien; Ba, Alexandre; Becce, Fabio; Bochud, François O.; Verdun, Francis R.

2017-03-01

Major technological advances in CT enable the acquisition of high quality images while minimizing patient exposure. The goal of this study was to objectively compare two generations of iterative reconstruction (IR) algorithms for the detection of low contrast structures. An abdominal phantom (QRM, Germany), containing 8, 6 and 5mm-diameter spheres (with a nominal contrast of 20HU) was scanned using our standard clinical noise index settings on a GE CT: "Discovery 750 HD". Two additional rings (2.5 and 5 cm) were also added to the phantom. Images were reconstructed using FBP, ASIR-50%, and VEO (full statistical Model Based Iterative Reconstruction, MBIR). The reconstructed slice thickness was 2.5 mm except 0.625 mm for VEO reconstructions. NPS was calculated to highlight the potential noise reduction of each IR algorithm. To assess LCD (low Contrast Detectability), a Channelized Hotelling Observer (CHO) with 10 DDoG channels was used with the area under the curve (AUC) as a figure of merit. Spheres contrast was also measured. ASIR-50% allowed a noise reduction by a factor two when compared to FBP without an improvement of the LCD. VEO allowed an additional noise reduction with a thinner slice thickness compared to ASIR-50% but with a major improvement of the LCD especially for the large-sized phantom and small lesions. Contrast decreased up to 10% with the phantom size increase for FBP and ASIR-50% and remained constant with VEO. VEO is particularly interesting for LCD when dealing with large patients and small lesion sizes and when the detection task is difficult.

Noise reduction improves memory for target language speech in competing native but not foreign language speech.

PubMed

Ng, Elaine Hoi Ning; Rudner, Mary; Lunner, Thomas; Rönnberg, Jerker

2015-01-01

A hearing aid noise reduction (NR) algorithm reduces the adverse effect of competing speech on memory for target speech for individuals with hearing impairment with high working memory capacity. In the present study, we investigated whether the positive effect of NR could be extended to individuals with low working memory capacity, as well as how NR influences recall performance for target native speech when the masker language is non-native. A sentence-final word identification and recall (SWIR) test was administered to 26 experienced hearing aid users. In this test, target spoken native language (Swedish) sentence lists were presented in competing native (Swedish) or foreign (Cantonese) speech with or without binary masking NR algorithm. After each sentence list, free recall of sentence final words was prompted. Working memory capacity was measured using a reading span (RS) test. Recall performance was associated with RS. However, the benefit obtained from NR was not associated with RS. Recall performance was more disrupted by native than foreign speech babble and NR improved recall performance in native but not foreign competing speech. Noise reduction improved memory for speech heard in competing speech for hearing aid users. Memory for native speech was more disrupted by native babble than foreign babble, but the disruptive effect of native speech babble was reduced to that of foreign babble when there was NR.

SU-E-I-82: Improving CT Image Quality for Radiation Therapy Using Iterative Reconstruction Algorithms and Slightly Increasing Imaging Doses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Noid, G; Chen, G; Tai, A

2014-06-01

Purpose: Iterative reconstruction (IR) algorithms are developed to improve CT image quality (IQ) by reducing noise without diminishing spatial resolution or contrast. For CT in radiation therapy (RT), slightly increasing imaging dose to improve IQ may be justified if it can substantially enhance structure delineation. The purpose of this study is to investigate and to quantify the IQ enhancement as a result of increasing imaging doses and using IR algorithms. Methods: CT images were acquired for phantoms, built to evaluate IQ metrics including spatial resolution, contrast and noise, with a variety of imaging protocols using a CT scanner (Definition ASmore » Open, Siemens) installed inside a Linac room. Representative patients were scanned once the protocols were optimized. Both phantom and patient scans were reconstructed using the Sinogram Affirmed Iterative Reconstruction (SAFIRE) and the Filtered Back Projection (FBP) methods. IQ metrics of the obtained CTs were compared. Results: IR techniques are demonstrated to preserve spatial resolution as measured by the point spread function and reduce noise in comparison to traditional FBP. Driven by the reduction in noise, the contrast to noise ratio is doubled by adopting the highest SAFIRE strength. As expected, increasing imaging dose reduces noise for both SAFIRE and FBP reconstructions. The contrast to noise increases from 3 to 5 by increasing the dose by a factor of 4. Similar IQ improvement was observed on the CTs for selected patients with pancreas and prostrate cancers. Conclusion: The IR techniques produce a measurable enhancement to CT IQ by reducing the noise. Increasing imaging dose further reduces noise independent of the IR techniques. The improved CT enables more accurate delineation of tumors and/or organs at risk during RT planning and delivery guidance.« less

Adaptive control and noise suppression by a variable-gain gradient algorithm

NASA Technical Reports Server (NTRS)

Merhav, S. J.; Mehta, R. S.

1987-01-01

An adaptive control system based on normalized LMS filters is investigated. The finite impulse response of the nonparametric controller is adaptively estimated using a given reference model. Specifically, the following issues are addressed: The stability of the closed loop system is analyzed and heuristically established. Next, the adaptation process is studied for piecewise constant plant parameters. It is shown that by introducing a variable-gain in the gradient algorithm, a substantial reduction in the LMS adaptation rate can be achieved. Finally, process noise at the plant output generally causes a biased estimate of the controller. By introducing a noise suppression scheme, this bias can be substantially reduced and the response of the adapted system becomes very close to that of the reference model. Extensive computer simulations validate these and demonstrate assertions that the system can rapidly adapt to random jumps in plant parameters.

Performance Evaluation of Multichannel Adaptive Algorithms for Local Active Noise Control

NASA Astrophysics Data System (ADS)

DE DIEGO, M.; GONZALEZ, A.

2001-07-01

This paper deals with the development of a multichannel active noise control (ANC) system inside an enclosed space. The purpose is to design a real practical system which works well in local ANC applications. Moreover, the algorithm implemented in the adaptive controller should be robust, of low computational complexity and it should manage to generate a uniform useful-size zone of quite in order to allow the head motion of a person seated on a seat inside a car. Experiments were carried out under semi-anechoic and listening room conditions to verify the successful implementation of the multichannel system. The developed prototype consists of an array of up to four microphones used as error sensors mounted on the headrest of a seat place inside the enclosure. One loudspeaker was used as single primary source and two secondary sources were placed facing the seat. The aim of this multichannel system is to reduce the sound pressure levels in an area around the error sensors, following a local control strategy. When using this technique, the cancellation points are not only the error sensor positions but an area around them, which is measured by using a monitoring microphone. Different multichannel adaptive algorithms for ANC have been analyzed and their performance verified. Multiple error algorithms are used in order to cancel out different types of primary noise (engine noise and random noise) with several configurations (up to four channels system). As an alternative to the multiple error LMS algorithm (multichannel version of the filtered-X LMS algorithm, MELMS), the least maximum mean squares (LMMS) and the scanning error-LMS algorithm have been developed in this work in order to reduce computational complexity and achieve a more uniform residual field. The ANC algorithms were programmed on a digital signal processing board equipped with a TMS320C40 floating point DSP processor. Measurements concerning real-time experiments on local noise reduction in two environments and at frequencies below 230 Hz are presented. Better noise levels attenuation is obtained in the semianechoic chamber due to the simplicity of the acoustic field. The size of the zone of quiet makes the system useful at relatively low frequencies and it is large enough to cover a listener's head movements. The spatial extent of the zones of quiet is generally observed to increase as the error sensors are moved away from the secondary source, they are put closer together or its number increases. In summary, different algorithms' performance and the viability of the multichannel system for local active noise control in real listening conditions are evaluated and some guidelines for designing such systems are then proposed.

Jet Noise Shielding Provided by a Hybrid Wing Body Aircraft

NASA Technical Reports Server (NTRS)

Doty, Michael J.; Brooks, Thomas F.; Burley, Casey L.; Bahr, Christopher J.; Pope, Dennis S.

2014-01-01

One approach toward achieving NASA's aggressive N+2 noise goal of 42 EPNdB cumulative margin below Stage 4 is through the use of novel vehicle configurations like the Hybrid Wing Body (HWB). Jet noise measurements from an HWB acoustic test in NASA Langley's 14- by 22-Foot Subsonic Tunnel are described. Two dual-stream, heated Compact Jet Engine Simulator (CJES) units are mounted underneath the inverted HWB model on a traversable support to permit measurement of varying levels of shielding provided by the fuselage. Both an axisymmetric and low noise chevron nozzle set are investigated in the context of shielding. The unshielded chevron nozzle set shows 1 to 2 dB of source noise reduction (relative to the unshielded axisymmetric nozzle set) with some penalties at higher frequencies. Shielding of the axisymmetric nozzles shows up to 6.5 dB of reduction at high frequency. The combination of shielding and low noise chevrons shows benefits beyond the expected additive benefits of the two, up to 10 dB, due to the effective migration of the jet source peak noise location upstream for increased shielding effectiveness. Jet noise source maps from phased array results processed with the Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) algorithm reinforce these observations.

Speckle noise reduction of 1-look SAR imagery

NASA Technical Reports Server (NTRS)

Nathan, Krishna S.; Curlander, John C.

1987-01-01

Speckle noise is inherent to synthetic aperture radar (SAR) imagery. Since the degradation of the image due to this noise results in uncertainties in the interpretation of the scene and in a loss of apparent resolution, it is desirable to filter the image to reduce this noise. In this paper, an adaptive algorithm based on the calculation of the local statistics around a pixel is applied to 1-look SAR imagery. The filter adapts to the nonstationarity of the image statistics since the size of the blocks is very small compared to that of the image. The performance of the filter is measured in terms of the equivalent number of looks (ENL) of the filtered image and the resulting resolution degradation. The results are compared to those obtained from different techniques applied to similar data. The local adaptive filter (LAF) significantly increases the ENL of the final image. The associated loss of resolution is also lower than that for other commonly used speckle reduction techniques.

Development of a high-performance noise-reduction filter for tomographic reconstruction

NASA Astrophysics Data System (ADS)

Kao, Chien-Min; Pan, Xiaochuan

2001-07-01

We propose a new noise-reduction method for tomographic reconstruction. The method incorporates a priori information on the source image for allowing the derivation of the energy spectrum of its ideal sinogram. In combination with the energy spectrum of the Poisson noise in the measured sinogram, we are able to derive a Wiener-like filter for effective suppression of the sinogram noise. The filtered backprojection (FBP) algorithm, with a ramp filter, is then applied to the filtered sinogram to produce tomographic images. The resulting filter has a closed-form expression in the frequency space and contains a single user-adjustable regularization parameter. The proposed method is hence simple to implement and easy to use. In contrast to the ad hoc apodizing windows, such as Hanning and Butterworth filters, that are commonly used in the conventional FBP reconstruction, the proposed filter is theoretically more rigorous as it is derived by basing upon an optimization criterion, subject to a known class of source image intensity distributions.

Two Procedures to Flag Radio Frequency Interference in the UV Plane

NASA Astrophysics Data System (ADS)

Sekhar, Srikrishna; Athreya, Ramana

2018-07-01

We present two algorithms to identify and flag radio frequency interference (RFI) in radio interferometric imaging data. The first algorithm utilizes the redundancy of visibilities inside a UV cell in the visibility plane to identify corrupted data, while varying the detection threshold in accordance with the observed reduction in noise with radial UV distance. In the second algorithm, we propose a scheme to detect faint RFI in the visibility time-channel (TC) plane of baselines. The efficacy of identifying RFI in the residual visibilities is reduced by the presence of ripples due to inaccurate subtraction of the strongest sources. This can be due to several reasons including primary beam asymmetries and other direction-dependent calibration errors. We eliminated these ripples by clipping the corresponding peaks in the associated Fourier plane. RFI was detected in the ripple-free TC plane but was flagged in the original visibilities. Application of these two algorithms to five different 150 MHz data sets from the GMRT resulted in a reduction in image noise of 20%–50% throughout the field along with a reduction in systematics and a corresponding increase in the number of detected sources. However, in comparing the mean flux densities before and after flagging RFI, we find a differential change with the fainter sources (25σ < S < 100 mJy) showing a change of ‑6% to +1% relative to the stronger sources (S > 100 mJy). We are unable to explain this effect, but it could be related to the CLEAN bias known for interferometers.

MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction?

PubMed

Deledalle, Charles-Alban; Denis, Loic; Tabti, Sonia; Tupin, Florence

2017-09-01

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) imaging. Since most current and planned SAR imaging satellites operate in polarimetric, interferometric, or tomographic modes, SAR images are multi-channel and speckle reduction techniques must jointly process all channels to recover polarimetric and interferometric information. The distinctive nature of SAR signal (complex-valued, corrupted by multiplicative fluctuations) calls for the development of specialized methods for speckle reduction. Image denoising is a very active topic in image processing with a wide variety of approaches and many denoising algorithms available, almost always designed for additive Gaussian noise suppression. This paper proposes a general scheme, called MuLoG (MUlti-channel LOgarithm with Gaussian denoising), to include such Gaussian denoisers within a multi-channel SAR speckle reduction technique. A new family of speckle reduction algorithms can thus be obtained, benefiting from the ongoing progress in Gaussian denoising, and offering several speckle reduction results often displaying method-specific artifacts that can be dismissed by comparison between results.

Quantum Error Correction with Biased Noise

NASA Astrophysics Data System (ADS)

Brooks, Peter

Quantum computing offers powerful new techniques for speeding up the calculation of many classically intractable problems. Quantum algorithms can allow for the efficient simulation of physical systems, with applications to basic research, chemical modeling, and drug discovery; other algorithms have important implications for cryptography and internet security. At the same time, building a quantum computer is a daunting task, requiring the coherent manipulation of systems with many quantum degrees of freedom while preventing environmental noise from interacting too strongly with the system. Fortunately, we know that, under reasonable assumptions, we can use the techniques of quantum error correction and fault tolerance to achieve an arbitrary reduction in the noise level. In this thesis, we look at how additional information about the structure of noise, or "noise bias," can improve or alter the performance of techniques in quantum error correction and fault tolerance. In Chapter 2, we explore the possibility of designing certain quantum gates to be extremely robust with respect to errors in their operation. This naturally leads to structured noise where certain gates can be implemented in a protected manner, allowing the user to focus their protection on the noisier unprotected operations. In Chapter 3, we examine how to tailor error-correcting codes and fault-tolerant quantum circuits in the presence of dephasing biased noise, where dephasing errors are far more common than bit-flip errors. By using an appropriately asymmetric code, we demonstrate the ability to improve the amount of error reduction and decrease the physical resources required for error correction. In Chapter 4, we analyze a variety of protocols for distilling magic states, which enable universal quantum computation, in the presence of faulty Clifford operations. Here again there is a hierarchy of noise levels, with a fixed error rate for faulty gates, and a second rate for errors in the distilled states which decreases as the states are distilled to better quality. The interplay of of these different rates sets limits on the achievable distillation and how quickly states converge to that limit.

Objective measures of listening effort: effects of background noise and noise reduction.

PubMed

Sarampalis, Anastasios; Kalluri, Sridhar; Edwards, Brent; Hafter, Ervin

2009-10-01

This work is aimed at addressing a seeming contradiction related to the use of noise-reduction (NR) algorithms in hearing aids. The problem is that although some listeners claim a subjective improvement from NR, it has not been shown to improve speech intelligibility, often even making it worse. To address this, the hypothesis tested here is that the positive effects of NR might be to reduce cognitive effort directed toward speech reception, making it available for other tasks. Normal-hearing individuals participated in 2 dual-task experiments, in which 1 task was to report sentences or words in noise set to various signal-to-noise ratios. Secondary tasks involved either holding words in short-term memory or responding in a complex visual reaction-time task. At low values of signal-to-noise ratio, although NR had no positive effect on speech reception thresholds, it led to better performance on the word-memory task and quicker responses in visual reaction times. Results from both dual tasks support the hypothesis that NR reduces listening effort and frees up cognitive resources for other tasks. Future hearing aid research should incorporate objective measurements of cognitive benefits.

Seismic noise attenuation using an online subspace tracking algorithm

NASA Astrophysics Data System (ADS)

Zhou, Yatong; Li, Shuhua; Zhang, Dong; Chen, Yangkang

2018-02-01

We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVD-based singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.

Noise-enhanced clustering and competitive learning algorithms.

PubMed

Osoba, Osonde; Kosko, Bart

2013-01-01

Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up convergence in stochastic unsupervised competitive learning, supervised competitive learning, and differential competitive learning. Copyright © 2012 Elsevier Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lemaire, H.; Barat, E.; Carrel, F.

In this work, we tested Maximum likelihood expectation-maximization (MLEM) algorithms optimized for gamma imaging applications on two recent coded mask gamma cameras. We respectively took advantage of the characteristics of the GAMPIX and Caliste HD-based gamma cameras: noise reduction thanks to mask/anti-mask procedure but limited energy resolution for GAMPIX, high energy resolution for Caliste HD. One of our short-term perspectives is the test of MAPEM algorithms integrating specific prior values for the data to reconstruct adapted to the gamma imaging topic. (authors)

Acoustic and perceptual effects of magnifying interaural difference cues in a simulated "binaural" hearing aid.

PubMed

de Taillez, Tobias; Grimm, Giso; Kollmeier, Birger; Neher, Tobias

2018-06-01

To investigate the influence of an algorithm designed to enhance or magnify interaural difference cues on speech signals in noisy, spatially complex conditions using both technical and perceptual measurements. To also investigate the combination of interaural magnification (IM), monaural microphone directionality (DIR), and binaural coherence-based noise reduction (BC). Speech-in-noise stimuli were generated using virtual acoustics. A computational model of binaural hearing was used to analyse the spatial effects of IM. Predicted speech quality changes and signal-to-noise-ratio (SNR) improvements were also considered. Additionally, a listening test was carried out to assess speech intelligibility and quality. Listeners aged 65-79 years with and without sensorineural hearing loss (N = 10 each). IM increased the horizontal separation of concurrent directional sound sources without introducing any major artefacts. In situations with diffuse noise, however, the interaural difference cues were distorted. Preprocessing the binaural input signals with DIR reduced distortion. IM influenced neither speech intelligibility nor speech quality. The IM algorithm tested here failed to improve speech perception in noise, probably because of the dispersion and inconsistent magnification of interaural difference cues in complex environments.

The correlation study of parallel feature extractor and noise reduction approaches

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dewi, Deshinta Arrova; Sundararajan, Elankovan; Prabuwono, Anton Satria

2015-05-15

This paper presents literature reviews that show variety of techniques to develop parallel feature extractor and finding its correlation with noise reduction approaches for low light intensity images. Low light intensity images are normally displayed as darker images and low contrast. Without proper handling techniques, those images regularly become evidences of misperception of objects and textures, the incapability to section them. The visual illusions regularly clues to disorientation, user fatigue, poor detection and classification performance of humans and computer algorithms. Noise reduction approaches (NR) therefore is an essential step for other image processing steps such as edge detection, image segmentation,more » image compression, etc. Parallel Feature Extractor (PFE) meant to capture visual contents of images involves partitioning images into segments, detecting image overlaps if any, and controlling distributed and redistributed segments to extract the features. Working on low light intensity images make the PFE face challenges and closely depend on the quality of its pre-processing steps. Some papers have suggested many well established NR as well as PFE strategies however only few resources have suggested or mentioned the correlation between them. This paper reviews best approaches of the NR and the PFE with detailed explanation on the suggested correlation. This finding may suggest relevant strategies of the PFE development. With the help of knowledge based reasoning, computational approaches and algorithms, we present the correlation study between the NR and the PFE that can be useful for the development and enhancement of other existing PFE.« less

Boundary layer noise subtraction in hydrodynamic tunnel using robust principal component analysis.

PubMed

Amailland, Sylvain; Thomas, Jean-Hugh; Pézerat, Charles; Boucheron, Romuald

2018-04-01

The acoustic study of propellers in a hydrodynamic tunnel is of paramount importance during the design process, but can involve significant difficulties due to the boundary layer noise (BLN). Indeed, advanced denoising methods are needed to recover the acoustic signal in case of poor signal-to-noise ratio. The technique proposed in this paper is based on the decomposition of the wall-pressure cross-spectral matrix (CSM) by taking advantage of both the low-rank property of the acoustic CSM and the sparse property of the BLN CSM. Thus, the algorithm belongs to the class of robust principal component analysis (RPCA), which derives from the widely used principal component analysis. If the BLN is spatially decorrelated, the proposed RPCA algorithm can blindly recover the acoustical signals even for negative signal-to-noise ratio. Unfortunately, in a realistic case, acoustic signals recorded in a hydrodynamic tunnel show that the noise may be partially correlated. A prewhitening strategy is then considered in order to take into account the spatially coherent background noise. Numerical simulations and experimental results show an improvement in terms of BLN reduction in the large hydrodynamic tunnel. The effectiveness of the denoising method is also investigated in the context of acoustic source localization.

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain.

PubMed

Rabbani, Hossein; Sonka, Milan; Abramoff, Michael D

2013-01-01

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.

Simulation for noise cancellation using LMS adaptive filter

NASA Astrophysics Data System (ADS)

Lee, Jia-Haw; Ooi, Lu-Ean; Ko, Ying-Hao; Teoh, Choe-Yung

2017-06-01

In this paper, the fundamental algorithm of noise cancellation, Least Mean Square (LMS) algorithm is studied and enhanced with adaptive filter. The simulation of the noise cancellation using LMS adaptive filter algorithm is developed. The noise corrupted speech signal and the engine noise signal are used as inputs for LMS adaptive filter algorithm. The filtered signal is compared to the original noise-free speech signal in order to highlight the level of attenuation of the noise signal. The result shows that the noise signal is successfully canceled by the developed adaptive filter. The difference of the noise-free speech signal and filtered signal are calculated and the outcome implies that the filtered signal is approaching the noise-free speech signal upon the adaptive filtering. The frequency range of the successfully canceled noise by the LMS adaptive filter algorithm is determined by performing Fast Fourier Transform (FFT) on the signals. The LMS adaptive filter algorithm shows significant noise cancellation at lower frequency range.

Noise reduction for low-dose helical CT by 3D penalized weighted least-squares sinogram smoothing

NASA Astrophysics Data System (ADS)

Wang, Jing; Li, Tianfang; Lu, Hongbing; Liang, Zhengrong

2006-03-01

Helical computed tomography (HCT) has several advantages over conventional step-and-shoot CT for imaging a relatively large object, especially for dynamic studies. However, HCT may increase X-ray exposure significantly to the patient. This work aims to reduce the radiation by lowering the X-ray tube current (mA) and filtering the low-mA (or dose) sinogram noise. Based on the noise properties of HCT sinogram, a three-dimensional (3D) penalized weighted least-squares (PWLS) objective function was constructed and an optimal sinogram was estimated by minimizing the objective function. To consider the difference of signal correlation among different direction of the HCT sinogram, an anisotropic Markov random filed (MRF) Gibbs function was designed as the penalty. The minimization of the objection function was performed by iterative Gauss-Seidel updating strategy. The effectiveness of the 3D-PWLS sinogram smoothing for low-dose HCT was demonstrated by a 3D Shepp-Logan head phantom study. Comparison studies with our previously developed KL domain PWLS sinogram smoothing algorithm indicate that the KL+2D-PWLS algorithm shows better performance on in-plane noise-resolution trade-off while the 3D-PLWS shows better performance on z-axis noise-resolution trade-off. Receiver operating characteristic (ROC) studies by using channelized Hotelling observer (CHO) shows that 3D-PWLS and KL+2DPWLS algorithms have similar performance on detectability in low-contrast environment.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

NASA Astrophysics Data System (ADS)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Models of formation and some algorithms of hyperspectral image processing

NASA Astrophysics Data System (ADS)

Achmetov, R. N.; Stratilatov, N. R.; Yudakov, A. A.; Vezenov, V. I.; Eremeev, V. V.

2014-12-01

Algorithms and information technologies for processing Earth hyperspectral imagery are presented. Several new approaches are discussed. Peculiar properties of processing the hyperspectral imagery, such as multifold signal-to-noise reduction, atmospheric distortions, access to spectral characteristics of every image point, and high dimensionality of data, were studied. Different measures of similarity between individual hyperspectral image points and the effect of additive uncorrelated noise on these measures were analyzed. It was shown that these measures are substantially affected by noise, and a new measure free of this disadvantage was proposed. The problem of detecting the observed scene object boundaries, based on comparing the spectral characteristics of image points, is considered. It was shown that contours are processed much better when spectral characteristics are used instead of energy brightness. A statistical approach to the correction of atmospheric distortions, which makes it possible to solve the stated problem based on analysis of a distorted image in contrast to analytical multiparametric models, was proposed. Several algorithms used to integrate spectral zonal images with data from other survey systems, which make it possible to image observed scene objects with a higher quality, are considered. Quality characteristics of hyperspectral data processing were proposed and studied.

A Background Noise Reduction Technique Using Adaptive Noise Cancellation for Microphone Arrays

NASA Technical Reports Server (NTRS)

Spalt, Taylor B.; Fuller, Christopher R.; Brooks, Thomas F.; Humphreys, William M., Jr.; Brooks, Thomas F.

2011-01-01

Background noise in wind tunnel environments poses a challenge to acoustic measurements due to possible low or negative Signal to Noise Ratios (SNRs) present in the testing environment. This paper overviews the application of time domain Adaptive Noise Cancellation (ANC) to microphone array signals with an intended application of background noise reduction in wind tunnels. An experiment was conducted to simulate background noise from a wind tunnel circuit measured by an out-of-flow microphone array in the tunnel test section. A reference microphone was used to acquire a background noise signal which interfered with the desired primary noise source signal at the array. The technique s efficacy was investigated using frequency spectra from the array microphones, array beamforming of the point source region, and subsequent deconvolution using the Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) algorithm. Comparisons were made with the conventional techniques for improving SNR of spectral and Cross-Spectral Matrix subtraction. The method was seen to recover the primary signal level in SNRs as low as -29 dB and outperform the conventional methods. A second processing approach using the center array microphone as the noise reference was investigated for more general applicability of the ANC technique. It outperformed the conventional methods at the -29 dB SNR but yielded less accurate results when coherence over the array dropped. This approach could possibly improve conventional testing methodology but must be investigated further under more realistic testing conditions.

Accelerated numerical processing of electronically recorded holograms with reduced speckle noise.

PubMed

Trujillo, Carlos; Garcia-Sucerquia, Jorge

2013-09-01

The numerical reconstruction of digitally recorded holograms suffers from speckle noise. An accelerated method that uses general-purpose computing in graphics processing units to reduce that noise is shown. The proposed methodology utilizes parallelized algorithms to record, reconstruct, and superimpose multiple uncorrelated holograms of a static scene. For the best tradeoff between reduction of the speckle noise and processing time, the method records, reconstructs, and superimposes six holograms of 1024 × 1024 pixels in 68 ms; for this case, the methodology reduces the speckle noise by 58% compared with that exhibited by a single hologram. The fully parallelized method running on a commodity graphics processing unit is one order of magnitude faster than the same technique implemented on a regular CPU using its multithreading capabilities. Experimental results are shown to validate the proposal.

Removal of impulse noise clusters from color images with local order statistics

NASA Astrophysics Data System (ADS)

Ruchay, Alexey; Kober, Vitaly

2017-09-01

This paper proposes a novel algorithm for restoring images corrupted with clusters of impulse noise. The noise clusters often occur when the probability of impulse noise is very high. The proposed noise removal algorithm consists of detection of bulky impulse noise in three color channels with local order statistics followed by removal of the detected clusters by means of vector median filtering. With the help of computer simulation we show that the proposed algorithm is able to effectively remove clustered impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics with that of common successful algorithms.

Dimension Reduction With Extreme Learning Machine.

PubMed

Kasun, Liyanaarachchi Lekamalage Chamara; Yang, Yan; Huang, Guang-Bin; Zhang, Zhengyou

2016-08-01

Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error.

Cascade and parallel combination (CPC) of adaptive filters for estimating heart rate during intensive physical exercise from photoplethysmographic signal

PubMed Central

Islam, Mohammad Tariqul; Tanvir Ahmed, Sk.; Zabir, Ishmam; Shahnaz, Celia

2018-01-01

Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable devices because of simplicity of construction and low cost of the sensor. The task becomes very difficult due to the presence of various motion artefacts. In this study, an algorithm based on cascade and parallel combination (CPC) of adaptive filters is proposed in order to reduce the effect of motion artefacts. First, preliminary noise reduction is performed by averaging two channel PPG signals. Next in order to reduce the effect of motion artefacts, a cascaded filter structure consisting of three cascaded adaptive filter blocks is developed where three-channel accelerometer signals are used as references to motion artefacts. To further reduce the affect of noise, a scheme based on convex combination of two such cascaded adaptive noise cancelers is introduced, where two widely used adaptive filters namely recursive least squares and least mean squares filters are employed. Heart rates are estimated from the noise reduced PPG signal in spectral domain. Finally, an efficient heart rate tracking algorithm is designed based on the nature of the heart rate variability. The performance of the proposed CPC method is tested on a widely used public database. It is found that the proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach. PMID:29515812

Ultrasound speckle reduction based on fractional order differentiation.

PubMed

Shao, Dangguo; Zhou, Ting; Liu, Fan; Yi, Sanli; Xiang, Yan; Ma, Lei; Xiong, Xin; He, Jianfeng

2017-07-01

Ultrasound images show a granular pattern of noise known as speckle that diminishes their quality and results in difficulties in diagnosis. To preserve edges and features, this paper proposes a fractional differentiation-based image operator to reduce speckle in ultrasound. An image de-noising model based on fractional partial differential equations with balance relation between k (gradient modulus threshold that controls the conduction) and v (the order of fractional differentiation) was constructed by the effective combination of fractional calculus theory and a partial differential equation, and the numerical algorithm of it was achieved using a fractional differential mask operator. The proposed algorithm has better speckle reduction and structure preservation than the three existing methods [P-M model, the speckle reducing anisotropic diffusion (SRAD) technique, and the detail preserving anisotropic diffusion (DPAD) technique]. And it is significantly faster than bilateral filtering (BF) in producing virtually the same experimental results. Ultrasound phantom testing and in vivo imaging show that the proposed method can improve the quality of an ultrasound image in terms of tissue SNR, CNR, and FOM values.

Priori mask guided image reconstruction (p-MGIR) for ultra-low dose cone-beam computed tomography

NASA Astrophysics Data System (ADS)

Park, Justin C.; Zhang, Hao; Chen, Yunmei; Fan, Qiyong; Kahler, Darren L.; Liu, Chihray; Lu, Bo

2015-11-01

Recently, the compressed sensing (CS) based iterative reconstruction method has received attention because of its ability to reconstruct cone beam computed tomography (CBCT) images with good quality using sparsely sampled or noisy projections, thus enabling dose reduction. However, some challenges remain. In particular, there is always a tradeoff between image resolution and noise/streak artifact reduction based on the amount of regularization weighting that is applied uniformly across the CBCT volume. The purpose of this study is to develop a novel low-dose CBCT reconstruction algorithm framework called priori mask guided image reconstruction (p-MGIR) that allows reconstruction of high-quality low-dose CBCT images while preserving the image resolution. In p-MGIR, the unknown CBCT volume was mathematically modeled as a combination of two regions: (1) where anatomical structures are complex, and (2) where intensities are relatively uniform. The priori mask, which is the key concept of the p-MGIR algorithm, was defined as the matrix that distinguishes between the two separate CBCT regions where the resolution needs to be preserved and where streak or noise needs to be suppressed. We then alternately updated each part of image by solving two sub-minimization problems iteratively, where one minimization was focused on preserving the edge information of the first part while the other concentrated on the removal of noise/artifacts from the latter part. To evaluate the performance of the p-MGIR algorithm, a numerical head-and-neck phantom, a Catphan 600 physical phantom, and a clinical head-and-neck cancer case were used for analysis. The results were compared with the standard Feldkamp-Davis-Kress as well as conventional CS-based algorithms. Examination of the p-MGIR algorithm showed that high-quality low-dose CBCT images can be reconstructed without compromising the image resolution. For both phantom and the patient cases, the p-MGIR is able to achieve a clinically-reasonable image with 60 projections. Therefore, a clinically-viable, high-resolution head-and-neck CBCT image can be obtained while cutting the dose by 83%. Moreover, the image quality obtained using p-MGIR is better than the quality obtained using other algorithms. In this work, we propose a novel low-dose CBCT reconstruction algorithm called p-MGIR. It can be potentially used as a CBCT reconstruction algorithm with low dose scan requests

Reduction of artifacts caused by orthopedic hardware in the spine in spectral detector CT examinations using virtual monoenergetic image reconstructions and metal-artifact-reduction algorithms.

PubMed

Große Hokamp, Nils; Neuhaus, V; Abdullayev, N; Laukamp, K; Lennartz, S; Mpotsaris, A; Borggrefe, J

2018-02-01

Aim of this study was to assess the artifact reduction in patients with orthopedic hardware in the spine as provided by (1) metal-artifact-reduction algorithms (O-MAR) and (2) virtual monoenergetic images (MonoE) as provided by spectral detector CT (SDCT) compared to conventional iterative reconstruction (CI). In all, 28 consecutive patients with orthopedic hardware in the spine who underwent SDCT-examinations were included. CI, O-MAR and MonoE (40-200 keV) images were reconstructed. Attenuation (HU) and noise (SD) were measured in order to calculate signal-to-noise ratio (SNR) of paravertebral muscle and spinal canal. Subjective image quality was assessed by two radiologists in terms of image quality and extent of artifact reduction. O-MAR and high-keV MonoE showed significant decrease of hypodense artifacts in terms of higher attenuation as compared to CI (CI vs O-MAR, 200 keV MonoE: -396.5HU vs. -115.2HU, -48.1HU; both p ≤ 0.001). Further, artifacts as depicted by noise were reduced in O-MAR and high-keV MonoE as compared to CI in (1) paravertebral muscle and (2) spinal canal-CI vs. O-MAR/200 keV: (1) 34.7 ± 19.0 HU vs. 26.4 ± 14.4 HU, p ≤ 0.05/27.4 ± 16.1, n.s.; (2) 103.4 ± 61.3 HU vs. 72.6 ± 62.6 HU/60.9 ± 40.1 HU, both p ≤ 0.001. Subjectively both O-MAR and high-keV images yielded an artifact reduction in up to 24/28 patients. Both, O-MAR and high-keV MonoE reconstructions as provided by SDCT lead to objective and subjective artifact reduction, thus the combination of O-MAR and MonoE seems promising for further reduction.

Impact of a New Adaptive Statistical Iterative Reconstruction (ASIR)-V Algorithm on Image Quality in Coronary Computed Tomography Angiography.

PubMed

Pontone, Gianluca; Muscogiuri, Giuseppe; Andreini, Daniele; Guaricci, Andrea I; Guglielmo, Marco; Baggiano, Andrea; Fazzari, Fabio; Mushtaq, Saima; Conte, Edoardo; Annoni, Andrea; Formenti, Alberto; Mancini, Elisabetta; Verdecchia, Massimo; Campari, Alessandro; Martini, Chiara; Gatti, Marco; Fusini, Laura; Bonfanti, Lorenzo; Consiglio, Elisa; Rabbat, Mark G; Bartorelli, Antonio L; Pepi, Mauro

2018-03-27

A new postprocessing algorithm named adaptive statistical iterative reconstruction (ASIR)-V has been recently introduced. The aim of this article was to analyze the impact of ASIR-V algorithm on signal, noise, and image quality of coronary computed tomography angiography. Fifty consecutive patients underwent clinically indicated coronary computed tomography angiography (Revolution CT; GE Healthcare, Milwaukee, WI). Images were reconstructed using filtered back projection and ASIR-V 0%, and a combination of filtered back projection and ASIR-V 20%-80% and ASIR-V 100%. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for left main coronary artery (LM), left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA) and were compared between the different postprocessing algorithms used. Similarly a four-point Likert image quality score of coronary segments was graded for each dataset and compared. A cutoff value of P < .05 was considered statistically significant. Compared to ASIR-V 0%, ASIR-V 100% demonstrated a significant reduction of image noise in all coronaries (P < .01). Compared to ASIR-V 0%, SNR was significantly higher with ASIR-V 60% in LM (P < .01), LAD (P < .05), LCX (P < .05), and RCA (P < .01). Compared to ASIR-V 0%, CNR for ASIR-V ≥60% was significantly improved in LM (P < .01), LAD (P < .05), and RCA (P < .01), whereas LCX demonstrated a significant improvement with ASIR-V ≥80%. ASIR-V 60% had significantly better Likert image quality scores compared to ASIR-V 0% in segment-, vessel-, and patient-based analyses (P < .01). Reconstruction with ASIR-V 60% provides the optimal balance between image noise, SNR, CNR, and image quality. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Online approximation of the multichannel Wiener filter with preservation of interaural level difference for binaural hearing-aids.

PubMed

Marques do Carmo, Diego; Costa, Márcio Holsbach

2018-04-01

This work presents an online approximation method for the multichannel Wiener filter (MWF) noise reduction technique with preservation of the noise interaural level difference (ILD) for binaural hearing-aids. The steepest descent method is applied to a previously proposed MWF-ILD cost function to both approximate the optimal linear estimator of the desired speech and keep the subjective perception of the original acoustic scenario. The computational cost of the resulting algorithm is estimated in terms of multiply and accumulate operations, whose number can be controlled by setting the number of iterations at each time frame. Simulation results for the particular case of one speech and one-directional noise source show that the proposed method increases the signal-to-noise ratio SNR of the originally acquired speech by up to 16.9 dB in the assessed scenarios. As compared to the online implementation of the conventional MWF technique, the proposed technique provides a reduction of up to 7 dB in the noise ILD error at the price of a reduction of up 3 dB in the output SNR. Subjective experiments with volunteers complement these objective measures with psychoacoustic results, which corroborate the expected spatial preservation of the original acoustic scenario. The proposed method allows practical online implementation of the MWF-ILD noise reduction technique under constrained computational resources. Predicted SNR improvements from 12 dB to 16.9 dB can be obtained in application-specific integrated circuits for hearing-aids and state-of-the-art digital signal processors. Copyright © 2018 Elsevier Ltd. All rights reserved.

MS-REDUCE: an ultrafast technique for reduction of big mass spectrometry data for high-throughput processing.

PubMed

Awan, Muaaz Gul; Saeed, Fahad

2016-05-15

Modern proteomics studies utilize high-throughput mass spectrometers which can produce data at an astonishing rate. These big mass spectrometry (MS) datasets can easily reach peta-scale level creating storage and analytic problems for large-scale systems biology studies. Each spectrum consists of thousands of peaks which have to be processed to deduce the peptide. However, only a small percentage of peaks in a spectrum are useful for peptide deduction as most of the peaks are either noise or not useful for a given spectrum. This redundant processing of non-useful peaks is a bottleneck for streaming high-throughput processing of big MS data. One way to reduce the amount of computation required in a high-throughput environment is to eliminate non-useful peaks. Existing noise removing algorithms are limited in their data-reduction capability and are compute intensive making them unsuitable for big data and high-throughput environments. In this paper we introduce a novel low-complexity technique based on classification, quantization and sampling of MS peaks. We present a novel data-reductive strategy for analysis of Big MS data. Our algorithm, called MS-REDUCE, is capable of eliminating noisy peaks as well as peaks that do not contribute to peptide deduction before any peptide deduction is attempted. Our experiments have shown up to 100× speed up over existing state of the art noise elimination algorithms while maintaining comparable high quality matches. Using our approach we were able to process a million spectra in just under an hour on a moderate server. The developed tool and strategy has been made available to wider proteomics and parallel computing community and the code can be found at https://github.com/pcdslab/MSREDUCE CONTACT: : fahad.saeed@wmich.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Phase noise optimization in temporal phase-shifting digital holography with partial coherence light sources and its application in quantitative cell imaging.

PubMed

Remmersmann, Christian; Stürwald, Stephan; Kemper, Björn; Langehanenberg, Patrik; von Bally, Gert

2009-03-10

In temporal phase-shifting-based digital holographic microscopy, high-resolution phase contrast imaging requires optimized conditions for hologram recording and phase retrieval. To optimize the phase resolution, for the example of a variable three-step algorithm, a theoretical analysis on statistical errors, digitalization errors, uncorrelated errors, and errors due to a misaligned temporal phase shift is carried out. In a second step the theoretically predicted results are compared to the measured phase noise obtained from comparative experimental investigations with several coherent and partially coherent light sources. Finally, the applicability for noise reduction is demonstrated by quantitative phase contrast imaging of pancreas tumor cells.

CUDA-based acceleration of collateral filtering in brain MR images

NASA Astrophysics Data System (ADS)

Li, Cheng-Yuan; Chang, Herng-Hua

2017-02-01

Image denoising is one of the fundamental and essential tasks within image processing. In medical imaging, finding an effective algorithm that can remove random noise in MR images is important. This paper proposes an effective noise reduction method for brain magnetic resonance (MR) images. Our approach is based on the collateral filter which is a more powerful method than the bilateral filter in many cases. However, the computation of the collateral filter algorithm is quite time-consuming. To solve this problem, we improved the collateral filter algorithm with parallel computing using GPU. We adopted CUDA, an application programming interface for GPU by NVIDIA, to accelerate the computation. Our experimental evaluation on an Intel Xeon CPU E5-2620 v3 2.40GHz with a NVIDIA Tesla K40c GPU indicated that the proposed implementation runs dramatically faster than the traditional collateral filter. We believe that the proposed framework has established a general blueprint for achieving fast and robust filtering in a wide variety of medical image denoising applications.

Active impulsive noise control using maximum correntropy with adaptive kernel size

NASA Astrophysics Data System (ADS)

Lu, Lu; Zhao, Haiquan

2017-03-01

The active noise control (ANC) based on the principle of superposition is an attractive method to attenuate the noise signals. However, the impulsive noise in the ANC systems will degrade the performance of the controller. In this paper, a filtered-x recursive maximum correntropy (FxRMC) algorithm is proposed based on the maximum correntropy criterion (MCC) to reduce the effect of outliers. The proposed FxRMC algorithm does not requires any priori information of the noise characteristics and outperforms the filtered-x least mean square (FxLMS) algorithm for impulsive noise. Meanwhile, in order to adjust the kernel size of FxRMC algorithm online, a recursive approach is proposed through taking into account the past estimates of error signals over a sliding window. Simulation and experimental results in the context of active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.

3D noise power spectrum applied on clinical MDCT scanners: effects of reconstruction algorithms and reconstruction filters

NASA Astrophysics Data System (ADS)

Miéville, Frédéric A.; Bolard, Gregory; Benkreira, Mohamed; Ayestaran, Paul; Gudinchet, François; Bochud, François; Verdun, Francis R.

2011-03-01

The noise power spectrum (NPS) is the reference metric for understanding the noise content in computed tomography (CT) images. To evaluate the noise properties of clinical multidetector (MDCT) scanners, local 2D and 3D NPSs were computed for different acquisition reconstruction parameters. A 64- and a 128-MDCT scanners were employed. Measurements were performed on a water phantom in axial and helical acquisition modes. CT dose index was identical for both installations. Influence of parameters such as the pitch, the reconstruction filter (soft, standard and bone) and the reconstruction algorithm (filtered-back projection (FBP), adaptive statistical iterative reconstruction (ASIR)) were investigated. Images were also reconstructed in the coronal plane using a reformat process. Then 2D and 3D NPS methods were computed. In axial acquisition mode, the 2D axial NPS showed an important magnitude variation as a function of the z-direction when measured at the phantom center. In helical mode, a directional dependency with lobular shape was observed while the magnitude of the NPS was kept constant. Important effects of the reconstruction filter, pitch and reconstruction algorithm were observed on 3D NPS results for both MDCTs. With ASIR, a reduction of the NPS magnitude and a shift of the NPS peak to the low frequency range were visible. 2D coronal NPS obtained from the reformat images was impacted by the interpolation when compared to 2D coronal NPS obtained from 3D measurements. The noise properties of volume measured in last generation MDCTs was studied using local 3D NPS metric. However, impact of the non-stationarity noise effect may need further investigations.

Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users.

PubMed

Goehring, Tobias; Bolner, Federico; Monaghan, Jessica J M; van Dijk, Bas; Zarowski, Andrzej; Bleeck, Stefan

2017-02-01

Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is used to attenuate noise-dominated and retain speech-dominated CI channels for electrical stimulation, as in traditional n-of-m CI coding strategies. The proposed algorithm was evaluated by measuring the speech-in-noise performance of 14 CI users using three types of background noise. Two NNSE algorithms were compared: a speaker-dependent algorithm, that was trained on the target speaker used for testing, and a speaker-independent algorithm, that was trained on different speakers. Significant improvements in the intelligibility of speech in stationary and fluctuating noises were found relative to the unprocessed condition for the speaker-dependent algorithm in all noise types and for the speaker-independent algorithm in 2 out of 3 noise types. The NNSE algorithms used noise-specific neural networks that generalized to novel segments of the same noise type and worked over a range of SNRs. The proposed algorithm has the potential to improve the intelligibility of speech in noise for CI users while meeting the requirements of low computational complexity and processing delay for application in CI devices. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain

PubMed Central

Sonka, Milan; Abramoff, Michael D.

2013-01-01

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR. PMID:24222760

Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Samei, Ehsan, E-mail: samei@duke.edu; Richard, Samuel

2015-01-15

Purpose: Different computed tomography (CT) reconstruction techniques offer different image quality attributes of resolution and noise, challenging the ability to compare their dose reduction potential against each other. The purpose of this study was to evaluate and compare the task-based imaging performance of CT systems to enable the assessment of the dose performance of a model-based iterative reconstruction (MBIR) to that of an adaptive statistical iterative reconstruction (ASIR) and a filtered back projection (FBP) technique. Methods: The ACR CT phantom (model 464) was imaged across a wide range of mA setting on a 64-slice CT scanner (GE Discovery CT750 HD,more » Waukesha, WI). Based on previous work, the resolution was evaluated in terms of a task-based modulation transfer function (MTF) using a circular-edge technique and images from the contrast inserts located in the ACR phantom. Noise performance was assessed in terms of the noise-power spectrum (NPS) measured from the uniform section of the phantom. The task-based MTF and NPS were combined with a task function to yield a task-based estimate of imaging performance, the detectability index (d′). The detectability index was computed as a function of dose for two imaging tasks corresponding to the detection of a relatively small and a relatively large feature (1.5 and 25 mm, respectively). The performance of MBIR in terms of the d′ was compared with that of ASIR and FBP to assess its dose reduction potential. Results: Results indicated that MBIR exhibits a variability spatial resolution with respect to object contrast and noise while significantly reducing image noise. The NPS measurements for MBIR indicated a noise texture with a low-pass quality compared to the typical midpass noise found in FBP-based CT images. At comparable dose, the d′ for MBIR was higher than those of FBP and ASIR by at least 61% and 19% for the small feature and the large feature tasks, respectively. Compared to FBP and ASIR, MBIR indicated a 46%–84% dose reduction potential, depending on task, without compromising the modeled detection performance. Conclusions: The presented methodology based on ACR phantom measurements extends current possibilities for the assessment of CT image quality under the complex resolution and noise characteristics exhibited with statistical and iterative reconstruction algorithms. The findings further suggest that MBIR can potentially make better use of the projections data to reduce CT dose by approximately a factor of 2. Alternatively, if the dose held unchanged, it can improve image quality by different levels for different tasks.« less

Evaluation of Moving Object Detection Based on Various Input Noise Using Fixed Camera

NASA Astrophysics Data System (ADS)

Kiaee, N.; Hashemizadeh, E.; Zarrinpanjeh, N.

2017-09-01

Detecting and tracking objects in video has been as a research area of interest in the field of image processing and computer vision. This paper evaluates the performance of a novel method for object detection algorithm in video sequences. This process helps us to know the advantage of this method which is being used. The proposed framework compares the correct and wrong detection percentage of this algorithm. This method was evaluated with the collected data in the field of urban transport which include car and pedestrian in fixed camera situation. The results show that the accuracy of the algorithm will decreases because of image resolution reduction.

Adaptive Temporal Matched Filtering for Noise Suppression in Fiber Optic Distributed Acoustic Sensing.

PubMed

Ölçer, İbrahim; Öncü, Ahmet

2017-06-05

Distributed vibration sensing based on phase-sensitive optical time domain reflectometry ( ϕ -OTDR) is being widely used in several applications. However, one of the main challenges in coherent detection-based ϕ -OTDR systems is the fading noise, which impacts the detection performance. In addition, typical signal averaging and differentiating techniques are not suitable for detecting high frequency events. This paper presents a new approach for reducing the effect of fading noise in fiber optic distributed acoustic vibration sensing systems without any impact on the frequency response of the detection system. The method is based on temporal adaptive processing of ϕ -OTDR signals. The fundamental theory underlying the algorithm, which is based on signal-to-noise ratio (SNR) maximization, is presented, and the efficacy of our algorithm is demonstrated with laboratory experiments and field tests. With the proposed digital processing technique, the results show that more than 10 dB of SNR values can be achieved without any reduction in the system bandwidth and without using additional optical amplifier stages in the hardware. We believe that our proposed adaptive processing approach can be effectively used to develop fiber optic-based distributed acoustic vibration sensing systems.

Adaptive Temporal Matched Filtering for Noise Suppression in Fiber Optic Distributed Acoustic Sensing

PubMed Central

Ölçer, İbrahim; Öncü, Ahmet

2017-01-01

Distributed vibration sensing based on phase-sensitive optical time domain reflectometry (ϕ-OTDR) is being widely used in several applications. However, one of the main challenges in coherent detection-based ϕ-OTDR systems is the fading noise, which impacts the detection performance. In addition, typical signal averaging and differentiating techniques are not suitable for detecting high frequency events. This paper presents a new approach for reducing the effect of fading noise in fiber optic distributed acoustic vibration sensing systems without any impact on the frequency response of the detection system. The method is based on temporal adaptive processing of ϕ-OTDR signals. The fundamental theory underlying the algorithm, which is based on signal-to-noise ratio (SNR) maximization, is presented, and the efficacy of our algorithm is demonstrated with laboratory experiments and field tests. With the proposed digital processing technique, the results show that more than 10 dB of SNR values can be achieved without any reduction in the system bandwidth and without using additional optical amplifier stages in the hardware. We believe that our proposed adaptive processing approach can be effectively used to develop fiber optic-based distributed acoustic vibration sensing systems. PMID:28587240

Electrocardiogram signal denoising based on a new improved wavelet thresholding

NASA Astrophysics Data System (ADS)

Han, Guoqiang; Xu, Zhijun

2016-08-01

Good quality electrocardiogram (ECG) is utilized by physicians for the interpretation and identification of physiological and pathological phenomena. In general, ECG signals may mix various noises such as baseline wander, power line interference, and electromagnetic interference in gathering and recording process. As ECG signals are non-stationary physiological signals, wavelet transform is investigated to be an effective tool to discard noises from corrupted signals. A new compromising threshold function called sigmoid function-based thresholding scheme is adopted in processing ECG signals. Compared with other methods such as hard/soft thresholding or other existing thresholding functions, the new algorithm has many advantages in the noise reduction of ECG signals. It perfectly overcomes the discontinuity at ±T of hard thresholding and reduces the fixed deviation of soft thresholding. The improved wavelet thresholding denoising can be proved to be more efficient than existing algorithms in ECG signal denoising. The signal to noise ratio, mean square error, and percent root mean square difference are calculated to verify the denoising performance as quantitative tools. The experimental results reveal that the waves including P, Q, R, and S waves of ECG signals after denoising coincide with the original ECG signals by employing the new proposed method.

WE-AB-207A-08: BEST IN PHYSICS (IMAGING): Advanced Scatter Correction and Iterative Reconstruction for Improved Cone-Beam CT Imaging On the TrueBeam Radiotherapy Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, A; Paysan, P; Brehm, M

2016-06-15

Purpose: To improve CBCT image quality for image-guided radiotherapy by applying advanced reconstruction algorithms to overcome scatter, noise, and artifact limitations Methods: CBCT is used extensively for patient setup in radiotherapy. However, image quality generally falls short of diagnostic CT, limiting soft-tissue based positioning and potential applications such as adaptive radiotherapy. The conventional TrueBeam CBCT reconstructor uses a basic scatter correction and FDK reconstruction, resulting in residual scatter artifacts, suboptimal image noise characteristics, and other artifacts like cone-beam artifacts. We have developed an advanced scatter correction that uses a finite-element solver (AcurosCTS) to model the behavior of photons as theymore » pass (and scatter) through the object. Furthermore, iterative reconstruction is applied to the scatter-corrected projections, enforcing data consistency with statistical weighting and applying an edge-preserving image regularizer to reduce image noise. The combined algorithms have been implemented on a GPU. CBCT projections from clinically operating TrueBeam systems have been used to compare image quality between the conventional and improved reconstruction methods. Planning CT images of the same patients have also been compared. Results: The advanced scatter correction removes shading and inhomogeneity artifacts, reducing the scatter artifact from 99.5 HU to 13.7 HU in a typical pelvis case. Iterative reconstruction provides further benefit by reducing image noise and eliminating streak artifacts, thereby improving soft-tissue visualization. In a clinical head and pelvis CBCT, the noise was reduced by 43% and 48%, respectively, with no change in spatial resolution (assessed visually). Additional benefits include reduction of cone-beam artifacts and reduction of metal artifacts due to intrinsic downweighting of corrupted rays. Conclusion: The combination of an advanced scatter correction with iterative reconstruction substantially improves CBCT image quality. It is anticipated that clinically acceptable reconstruction times will result from a multi-GPU implementation (the algorithms are under active development and not yet commercially available). All authors are employees of and (may) own stock of Varian Medical Systems.« less

Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maier, Andreas; Wigstroem, Lars; Hofmann, Hannes G.

2011-11-15

Purpose: The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. Methods: 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-raymore » views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections + angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. Results: The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8.9-fold speed-up of the processing (from 1336 to 150 s). Conclusions: Adaptive anisotropic filtering has the potential to substantially improve image quality and/or reduce the radiation dose required for obtaining 3D image data using cone beam CT.« less

A novel coupling of noise reduction algorithms for particle flow simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zimoń, M.J., E-mail: malgorzata.zimon@stfc.ac.uk; James Weir Fluids Lab, Mechanical and Aerospace Engineering Department, The University of Strathclyde, Glasgow G1 1XJ; Reese, J.M.

2016-09-15

Proper orthogonal decomposition (POD) and its extension based on time-windows have been shown to greatly improve the effectiveness of recovering smooth ensemble solutions from noisy particle data. However, to successfully de-noise any molecular system, a large number of measurements still need to be provided. In order to achieve a better efficiency in processing time-dependent fields, we have combined POD with a well-established signal processing technique, wavelet-based thresholding. In this novel hybrid procedure, the wavelet filtering is applied within the POD domain and referred to as WAVinPOD. The algorithm exhibits promising results when applied to both synthetically generated signals and particlemore » data. In this work, the simulations compare the performance of our new approach with standard POD or wavelet analysis in extracting smooth profiles from noisy velocity and density fields. Numerical examples include molecular dynamics and dissipative particle dynamics simulations of unsteady force- and shear-driven liquid flows, as well as phase separation phenomenon. Simulation results confirm that WAVinPOD preserves the dimensionality reduction obtained using POD, while improving its filtering properties through the sparse representation of data in wavelet basis. This paper shows that WAVinPOD outperforms the other estimators for both synthetically generated signals and particle-based measurements, achieving a higher signal-to-noise ratio from a smaller number of samples. The new filtering methodology offers significant computational savings, particularly for multi-scale applications seeking to couple continuum informations with atomistic models. It is the first time that a rigorous analysis has compared de-noising techniques for particle-based fluid simulations.« less

Airfoil optimization for unsteady flows with application to high-lift noise reduction

NASA Astrophysics Data System (ADS)

Rumpfkeil, Markus Peer

The use of steady-state aerodynamic optimization methods in the computational fluid dynamic (CFD) community is fairly well established. In particular, the use of adjoint methods has proven to be very beneficial because their cost is independent of the number of design variables. The application of numerical optimization to airframe-generated noise, however, has not received as much attention, but with the significant quieting of modern engines, airframe noise now competes with engine noise. Optimal control techniques for unsteady flows are needed in order to be able to reduce airframe-generated noise. In this thesis, a general framework is formulated to calculate the gradient of a cost function in a nonlinear unsteady flow environment via the discrete adjoint method. The unsteady optimization algorithm developed in this work utilizes a Newton-Krylov approach since the gradient-based optimizer uses the quasi-Newton method BFGS, Newton's method is applied to the nonlinear flow problem, GMRES is used to solve the resulting linear problem inexactly, and last but not least the linear adjoint problem is solved using Bi-CGSTAB. The flow is governed by the unsteady two-dimensional compressible Navier-Stokes equations in conjunction with a one-equation turbulence model, which are discretized using structured grids and a finite difference approach. The effectiveness of the unsteady optimization algorithm is demonstrated by applying it to several problems of interest including shocktubes, pulses in converging-diverging nozzles, rotating cylinders, transonic buffeting, and an unsteady trailing-edge flow. In order to address radiated far-field noise, an acoustic wave propagation program based on the Ffowcs Williams and Hawkings (FW-H) formulation is implemented and validated. The general framework is then used to derive the adjoint equations for a novel hybrid URANS/FW-H optimization algorithm in order to be able to optimize the shape of airfoils based on their calculated far-field pressure fluctuations. Validation and application results for this novel hybrid URANS/FW-H optimization algorithm show that it is possible to optimize the shape of an airfoil in an unsteady flow environment to minimize its radiated far-field noise while maintaining good aerodynamic performance.

A simple method for low-contrast detectability, image quality and dose optimisation with CT iterative reconstruction algorithms and model observers.

PubMed

Bellesi, Luca; Wyttenbach, Rolf; Gaudino, Diego; Colleoni, Paolo; Pupillo, Francesco; Carrara, Mauro; Braghetti, Antonio; Puligheddu, Carla; Presilla, Stefano

2017-01-01

The aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings (i.e. mAs) and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment. Images of supraslice low-contrast objects of a CT phantom were acquired using different mAs values. Images were reconstructed using filtered back projection (FBP), hybrid and iterative model-based methods. Image quality parameters were evaluated in terms of modulation transfer function; noise, and uniformity using two software resources. For the definition of low-contrast detectability, studies based on both human (i.e. four-alternative forced-choice test) and model observers were performed across the various images. Compared to FBP, image quality parameters were improved by using iterative reconstruction (IR) algorithms. In particular, IR model-based methods provided a 60% noise reduction and a 70% dose reduction, preserving image quality and low-contrast detectability for human radiological evaluation. According to the model observer, the diameters of the minimum detectable detail were around 2 mm (up to 100 mAs). Below 100 mAs, the model observer was unable to provide a result. IR methods improve CT protocol quality, providing a potential dose reduction while maintaining a good image detectability. Model observer can in principle be useful to assist human performance in CT low-contrast detection tasks and in dose optimisation.

Achieving routine submillisievert CT scanning: report from the summit on management of radiation dose in CT.

PubMed

McCollough, Cynthia H; Chen, Guang Hong; Kalender, Willi; Leng, Shuai; Samei, Ehsan; Taguchi, Katsuyuki; Wang, Ge; Yu, Lifeng; Pettigrew, Roderic I

2012-08-01

This Special Report presents the consensus of the Summit on Management of Radiation Dose in Computed Tomography (CT) (held in February 2011), which brought together participants from academia, clinical practice, industry, and regulatory and funding agencies to identify the steps required to reduce the effective dose from routine CT examinations to less than 1 mSv. The most promising technologies and methods discussed at the summit include innovations and developments in x-ray sources; detectors; and image reconstruction, noise reduction, and postprocessing algorithms. Access to raw projection data and standard data sets for algorithm validation and optimization is a clear need, as is the need for new, clinically relevant metrics of image quality and diagnostic performance. Current commercially available techniques such as automatic exposure control, optimization of tube potential, beam-shaping filters, and dynamic z-axis collimators are important, and education to successfully implement these methods routinely is critically needed. Other methods that are just becoming widely available, such as iterative reconstruction, noise reduction, and postprocessing algorithms, will also have an important role. Together, these existing techniques can reduce dose by a factor of two to four. Technical advances that show considerable promise for additional dose reduction but are several years or more from commercial availability include compressed sensing, volume of interest and interior tomography techniques, and photon-counting detectors. This report offers a strategic roadmap for the CT user and research and manufacturer communities toward routinely achieving effective doses of less than 1 mSv, which is well below the average annual dose from naturally occurring sources of radiation.

Cognitive Processing Speed, Working Memory, and the Intelligibility of Hearing Aid-Processed Speech in Persons with Hearing Impairment

PubMed Central

Yumba, Wycliffe Kabaywe

2017-01-01

Previous studies have demonstrated that successful listening with advanced signal processing in digital hearing aids is associated with individual cognitive capacity, particularly working memory capacity (WMC). This study aimed to examine the relationship between cognitive abilities (cognitive processing speed and WMC) and individual listeners’ responses to digital signal processing settings in adverse listening conditions. A total of 194 native Swedish speakers (83 women and 111 men), aged 33–80 years (mean = 60.75 years, SD = 8.89), with bilateral, symmetrical mild to moderate sensorineural hearing loss who had completed a lexical decision speed test (measuring cognitive processing speed) and semantic word-pair span test (SWPST, capturing WMC) participated in this study. The Hagerman test (capturing speech recognition in noise) was conducted using an experimental hearing aid with three digital signal processing settings: (1) linear amplification without noise reduction (NoP), (2) linear amplification with noise reduction (NR), and (3) non-linear amplification without NR (“fast-acting compression”). The results showed that cognitive processing speed was a better predictor of speech intelligibility in noise, regardless of the types of signal processing algorithms used. That is, there was a stronger association between cognitive processing speed and NR outcomes and fast-acting compression outcomes (in steady state noise). We observed a weaker relationship between working memory and NR, but WMC did not relate to fast-acting compression. WMC was a relatively weaker predictor of speech intelligibility in noise. These findings might have been different if the participants had been provided with training and or allowed to acclimatize to binary masking noise reduction or fast-acting compression. PMID:28861009

Objective Prediction of Hearing Aid Benefit Across Listener Groups Using Machine Learning: Speech Recognition Performance With Binaural Noise-Reduction Algorithms.

PubMed

Schädler, Marc R; Warzybok, Anna; Kollmeier, Birger

2018-01-01

The simulation framework for auditory discrimination experiments (FADE) was adopted and validated to predict the individual speech-in-noise recognition performance of listeners with normal and impaired hearing with and without a given hearing-aid algorithm. FADE uses a simple automatic speech recognizer (ASR) to estimate the lowest achievable speech reception thresholds (SRTs) from simulated speech recognition experiments in an objective way, independent from any empirical reference data. Empirical data from the literature were used to evaluate the model in terms of predicted SRTs and benefits in SRT with the German matrix sentence recognition test when using eight single- and multichannel binaural noise-reduction algorithms. To allow individual predictions of SRTs in binaural conditions, the model was extended with a simple better ear approach and individualized by taking audiograms into account. In a realistic binaural cafeteria condition, FADE explained about 90% of the variance of the empirical SRTs for a group of normal-hearing listeners and predicted the corresponding benefits with a root-mean-square prediction error of 0.6 dB. This highlights the potential of the approach for the objective assessment of benefits in SRT without prior knowledge about the empirical data. The predictions for the group of listeners with impaired hearing explained 75% of the empirical variance, while the individual predictions explained less than 25%. Possibly, additional individual factors should be considered for more accurate predictions with impaired hearing. A competing talker condition clearly showed one limitation of current ASR technology, as the empirical performance with SRTs lower than -20 dB could not be predicted.

Objective Prediction of Hearing Aid Benefit Across Listener Groups Using Machine Learning: Speech Recognition Performance With Binaural Noise-Reduction Algorithms

PubMed Central

Schädler, Marc R.; Warzybok, Anna; Kollmeier, Birger

2018-01-01

The simulation framework for auditory discrimination experiments (FADE) was adopted and validated to predict the individual speech-in-noise recognition performance of listeners with normal and impaired hearing with and without a given hearing-aid algorithm. FADE uses a simple automatic speech recognizer (ASR) to estimate the lowest achievable speech reception thresholds (SRTs) from simulated speech recognition experiments in an objective way, independent from any empirical reference data. Empirical data from the literature were used to evaluate the model in terms of predicted SRTs and benefits in SRT with the German matrix sentence recognition test when using eight single- and multichannel binaural noise-reduction algorithms. To allow individual predictions of SRTs in binaural conditions, the model was extended with a simple better ear approach and individualized by taking audiograms into account. In a realistic binaural cafeteria condition, FADE explained about 90% of the variance of the empirical SRTs for a group of normal-hearing listeners and predicted the corresponding benefits with a root-mean-square prediction error of 0.6 dB. This highlights the potential of the approach for the objective assessment of benefits in SRT without prior knowledge about the empirical data. The predictions for the group of listeners with impaired hearing explained 75% of the empirical variance, while the individual predictions explained less than 25%. Possibly, additional individual factors should be considered for more accurate predictions with impaired hearing. A competing talker condition clearly showed one limitation of current ASR technology, as the empirical performance with SRTs lower than −20 dB could not be predicted. PMID:29692200

A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals

NASA Astrophysics Data System (ADS)

Bozchalooi, I. Soltani; Liang, Ming

2008-05-01

The vibration signal measured from a bearing contains vital information for the prognostic and health assessment purposes. However, when bearings are installed as part of a complex mechanical system, the measured signal is often heavily clouded by various noises due to the compounded effect of interferences of other machine elements and background noises present in the measuring device. As such, reliable condition monitoring would not be possible without proper de-noising. This is particularly true for incipient bearing faults with very weak signature signals. A new de-noising scheme is proposed in this paper to enhance the vibration signals acquired from faulty bearings. This de-noising scheme features a spectral subtraction to trim down the in-band noise prior to wavelet filtering. The Gabor wavelet is used in the wavelet transform and its parameters, i.e., scale and shape factor are selected in separate steps. The proper scale is found based on a novel resonance estimation algorithm. This algorithm makes use of the information derived from the variable shaft rotational speed though such variation is highly undesirable in fault detection since it complicates the process substantially. The shape factor value is then selected by minimizing a smoothness index. This index is defined as the ratio of the geometric mean to the arithmetic mean of the wavelet coefficient moduli. De-noising results are presented for simulated signals and experimental data acquired from both normal and faulty bearings with defective outer race, inner race, and rolling element.

Assessment of phase based dose modulation for improved dose efficiency in cardiac CT on an anthropomorphic motion phantom

NASA Astrophysics Data System (ADS)

Budde, Adam; Nilsen, Roy; Nett, Brian

2014-03-01

State of the art automatic exposure control modulates the tube current across view angle and Z based on patient anatomy for use in axial full scan reconstructions. Cardiac CT, however, uses a fundamentally different image reconstruction that applies a temporal weighting to reduce motion artifacts. This paper describes a phase based mA modulation that goes beyond axial and ECG modulation; it uses knowledge of the temporal view weighting applied within the reconstruction algorithm to improve dose efficiency in cardiac CT scanning. Using physical phantoms and synthetic noise emulation, we measure how knowledge of sinogram temporal weighting and the prescribed cardiac phase can be used to improve dose efficiency. First, we validated that a synthetic CT noise emulation method produced realistic image noise. Next, we used the CT noise emulation method to simulate mA modulation on scans of a physical anthropomorphic phantom where a motion profile corresponding to a heart rate of 60 beats per minute was used. The CT noise emulation method matched noise to lower dose scans across the image within 1.5% relative error. Using this noise emulation method to simulate modulating the mA while keeping the total dose constant, the image variance was reduced by an average of 11.9% on a scan with 50 msec padding, demonstrating improved dose efficiency. Radiation dose reduction in cardiac CT can be achieved while maintaining the same level of image noise through phase based dose modulation that incorporates knowledge of the cardiac reconstruction algorithm.

Chlorophyll-a Algorithms for Oligotrophic Oceans: A Novel Approach Based on Three-Band Reflectance Difference

NASA Technical Reports Server (NTRS)

Hu, Chuanmin; Lee, Zhongping; Franz, Bryan

2011-01-01

A new empirical algorithm is proposed to estimate surface chlorophyll-a concentrations (Chl) in the global ocean for Chl less than or equal to 0.25 milligrams per cubic meters (approximately 77% of the global ocean area). The algorithm is based on a color index (CI), defined as the difference between remote sensing reflectance (R(sub rs), sr(sup -1) in the green and a reference formed linearly between R(sub rs) in the blue and red. For low Chl waters, in situ data showed a tighter (and therefore better) relationship between CI and Chl than between traditional band-ratios and Chl, which was further validated using global data collected concurrently by ship-borne and SeaWiFS satellite instruments. Model simulations showed that for low Chl waters, compared with the band-ratio algorithm, the CI-based algorithm (CIA) was more tolerant to changes in chlorophyll-specific backscattering coefficient, and performed similarly for different relative contributions of non-phytoplankton absorption. Simulations using existing atmospheric correction approaches further demonstrated that the CIA was much less sensitive than band-ratio algorithms to various errors induced by instrument noise and imperfect atmospheric correction (including sun glint and whitecap corrections). Image and time-series analyses of SeaWiFS and MODIS/Aqua data also showed improved performance in terms of reduced image noise, more coherent spatial and temporal patterns, and consistency between the two sensors. The reduction in noise and other errors is particularly useful to improve the detection of various ocean features such as eddies. Preliminary tests over MERIS and CZCS data indicate that the new approach should be generally applicable to all existing and future ocean color instruments.

Active noise control technique for diesel train locomotor exhaust noise abatement

NASA Astrophysics Data System (ADS)

Cotana, Franco; Rossi, Federico

2002-11-01

An original prototype for train locomotor exhaust gas pipe noise reduction (electronic muffler) is proposed: the system is based on an active noise control technique. An acoustical measurement campaign has shown that locomotor exhaust noise is characterized by very low frequency components (less than 80 Hz) and very high acoustic power (up to 110 dB). A peculiar electronic muffler characterized by high acoustical efficiency at very low frequencies has been designed and realized at Perugia University Acoustic Laboratory; it has been installed on an Italian D.245 train locomotor, equipped with a 500-kW diesel engine. The electronic muffler has been added to the traditional passive muffler. Very low transmission losses are introduced by the electronic muffler because of its particular shape; thus, engine efficiency does not further decrease. Canceling noise is generated by means of DSP-based numerical algorithm. Disturbing noise and canceling noise destructively interfere at the exhaust duct outlet section; outgoing noise is thus reduced. The control system reduces exhaust noise both in the steady and unsteady engine regime. Measurement results have shown that electronic muffler introduces up to 15 dB noise abatement in the low-frequency components.

BATMAN: Bayesian Technique for Multi-image Analysis

NASA Astrophysics Data System (ADS)

Casado, J.; Ascasibar, Y.; García-Benito, R.; Guidi, G.; Choudhury, O. S.; Bellocchi, E.; Sánchez, S. F.; Díaz, A. I.

2017-04-01

This paper describes the Bayesian Technique for Multi-image Analysis (BATMAN), a novel image-segmentation technique based on Bayesian statistics that characterizes any astronomical data set containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (I.e. identical signal within the errors). We illustrate its operation and performance with a set of test cases including both synthetic and real integral-field spectroscopic data. The output segmentations adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. The quality of the recovered signal represents an improvement with respect to the input, especially in regions with low signal-to-noise ratio. However, the algorithm may be sensitive to small-scale random fluctuations, and its performance in presence of spatial gradients is limited. Due to these effects, errors may be underestimated by as much as a factor of 2. Our analysis reveals that the algorithm prioritizes conservation of all the statistically significant information over noise reduction, and that the precise choice of the input data has a crucial impact on the results. Hence, the philosophy of BaTMAn is not to be used as a 'black box' to improve the signal-to-noise ratio, but as a new approach to characterize spatially resolved data prior to its analysis. The source code is publicly available at http://astro.ft.uam.es/SELGIFS/BaTMAn.

Spectral CT Image Restoration via an Average Image-Induced Nonlocal Means Filter.

PubMed

Zeng, Dong; Huang, Jing; Zhang, Hua; Bian, Zhaoying; Niu, Shanzhou; Zhang, Zhang; Feng, Qianjin; Chen, Wufan; Ma, Jianhua

2016-05-01

Spectral computed tomography (SCT) images reconstructed by an analytical approach often suffer from a poor signal-to-noise ratio and strong streak artifacts when sufficient photon counts are not available in SCT imaging. In reducing noise-induced artifacts in SCT images, in this study, we propose an average image-induced nonlocal means (aviNLM) filter for each energy-specific image restoration.  Methods:  The present aviNLM algorithm exploits redundant information in the whole energy domain. Specifically, the proposed aviNLM algorithm yields the restored results by performing a nonlocal weighted average operation on the noisy energy-specific images with the nonlocal weight matrix between the target and prior images, in which the prior image is generated from all of the images reconstructed in each energy bin.  Results: Qualitative and quantitative studies are conducted to evaluate the aviNLM filter by using the data of digital phantom, physical phantom, and clinical patient data acquired from the energy-resolved and -integrated detectors, respectively. Experimental results show that the present aviNLM filter can achieve promising results for SCT image restoration in terms of noise-induced artifact suppression, cross profile, and contrast-to-noise ratio and material decomposition assessment. Conclusion and Significance: The present aviNLM algorithm has useful potential for radiation dose reduction by lowering the mAs in SCT imaging, and it may be useful for some other clinical applications, such as in myocardial perfusion imaging and radiotherapy.

Noise-enhanced convolutional neural networks.

PubMed

Audhkhasi, Kartik; Osoba, Osonde; Kosko, Bart

2016-06-01

Injecting carefully chosen noise can speed convergence in the backpropagation training of a convolutional neural network (CNN). The Noisy CNN algorithm speeds training on average because the backpropagation algorithm is a special case of the generalized expectation-maximization (EM) algorithm and because such carefully chosen noise always speeds up the EM algorithm on average. The CNN framework gives a practical way to learn and recognize images because backpropagation scales with training data. It has only linear time complexity in the number of training samples. The Noisy CNN algorithm finds a special separating hyperplane in the network's noise space. The hyperplane arises from the likelihood-based positivity condition that noise-boosts the EM algorithm. The hyperplane cuts through a uniform-noise hypercube or Gaussian ball in the noise space depending on the type of noise used. Noise chosen from above the hyperplane speeds training on average. Noise chosen from below slows it on average. The algorithm can inject noise anywhere in the multilayered network. Adding noise to the output neurons reduced the average per-iteration training-set cross entropy by 39% on a standard MNIST image test set of handwritten digits. It also reduced the average per-iteration training-set classification error by 47%. Adding noise to the hidden layers can also reduce these performance measures. The noise benefit is most pronounced for smaller data sets because the largest EM hill-climbing gains tend to occur in the first few iterations. This noise effect can assist random sampling from large data sets because it allows a smaller random sample to give the same or better performance than a noiseless sample gives. Copyright © 2015 Elsevier Ltd. All rights reserved.

A generalised background correction algorithm for a Halo Doppler lidar and its application to data from Finland

DOE PAGES

Manninen, Antti J.; O'Connor, Ewan J.; Vakkari, Ville; ...

2016-03-03

Current commercially available Doppler lidars provide an economical and robust solution for measuring vertical and horizontal wind velocities, together with the ability to provide co- and cross-polarised backscatter profiles. The high temporal resolution of these instruments allows turbulent properties to be obtained from studying the variation in radial velocities. However, the instrument specifications mean that certain characteristics, especially the background noise behaviour, become a limiting factor for the instrument sensitivity in regions where the aerosol load is low. Turbulent calculations require an accurate estimate of the contribution from velocity uncertainty estimates, which are directly related to the signal-to-noise ratio. Anymore » bias in the signal-to-noise ratio will propagate through as a bias in turbulent properties. In this paper we present a method to correct for artefacts in the background noise behaviour of commercially available Doppler lidars and reduce the signal-to-noise ratio threshold used to discriminate between noise, and cloud or aerosol signals. We show that, for Doppler lidars operating continuously at a number of locations in Finland, the data availability can be increased by as much as 50 % after performing this background correction and subsequent reduction in the threshold. Furthermore the reduction in bias also greatly improves subsequent calculations of turbulent properties in weak signal regimes.« less

A generalised background correction algorithm for a Halo Doppler lidar and its application to data from Finland

DOE Office of Scientific and Technical Information (OSTI.GOV)

Manninen, Antti J.; O'Connor, Ewan J.; Vakkari, Ville

Current commercially available Doppler lidars provide an economical and robust solution for measuring vertical and horizontal wind velocities, together with the ability to provide co- and cross-polarised backscatter profiles. The high temporal resolution of these instruments allows turbulent properties to be obtained from studying the variation in radial velocities. However, the instrument specifications mean that certain characteristics, especially the background noise behaviour, become a limiting factor for the instrument sensitivity in regions where the aerosol load is low. Turbulent calculations require an accurate estimate of the contribution from velocity uncertainty estimates, which are directly related to the signal-to-noise ratio. Anymore » bias in the signal-to-noise ratio will propagate through as a bias in turbulent properties. In this paper we present a method to correct for artefacts in the background noise behaviour of commercially available Doppler lidars and reduce the signal-to-noise ratio threshold used to discriminate between noise, and cloud or aerosol signals. We show that, for Doppler lidars operating continuously at a number of locations in Finland, the data availability can be increased by as much as 50 % after performing this background correction and subsequent reduction in the threshold. Furthermore the reduction in bias also greatly improves subsequent calculations of turbulent properties in weak signal regimes.« less

Investigation of optical current transformer signal processing method based on an improved Kalman algorithm

NASA Astrophysics Data System (ADS)

Shen, Yan; Ge, Jin-ming; Zhang, Guo-qing; Yu, Wen-bin; Liu, Rui-tong; Fan, Wei; Yang, Ying-xuan

2018-01-01

This paper explores the problem of signal processing in optical current transformers (OCTs). Based on the noise characteristics of OCTs, such as overlapping signals, noise frequency bands, low signal-to-noise ratios, and difficulties in acquiring statistical features of noise power, an improved standard Kalman filtering algorithm was proposed for direct current (DC) signal processing. The state-space model of the OCT DC measurement system is first established, and then mixed noise can be processed by adding mixed noise into measurement and state parameters. According to the minimum mean squared error criterion, state predictions and update equations of the improved Kalman algorithm could be deduced based on the established model. An improved central difference Kalman filter was proposed for alternating current (AC) signal processing, which improved the sampling strategy and noise processing of colored noise. Real-time estimation and correction of noise were achieved by designing AC and DC noise recursive filters. Experimental results show that the improved signal processing algorithms had a good filtering effect on the AC and DC signals with mixed noise of OCT. Furthermore, the proposed algorithm was able to achieve real-time correction of noise during the OCT filtering process.

Assessment of noise reduction potential and image quality improvement of a new generation adaptive statistical iterative reconstruction (ASIR-V) in chest CT.

PubMed

Tang, Hui; Yu, Nan; Jia, Yongjun; Yu, Yong; Duan, Haifeng; Han, Dong; Ma, Guangming; Ren, Chenglong; He, Taiping

2018-01-01

To evaluate the image quality improvement and noise reduction in routine dose, non-enhanced chest CT imaging by using a new generation adaptive statistical iterative reconstruction (ASIR-V) in comparison with ASIR algorithm. 30 patients who underwent routine dose, non-enhanced chest CT using GE Discovery CT750HU (GE Healthcare, Waukesha, WI) were included. The scan parameters included tube voltage of 120 kVp, automatic tube current modulation to obtain a noise index of 14HU, rotation speed of 0.6 s, pitch of 1.375:1 and slice thickness of 5 mm. After scanning, all scans were reconstructed with the recommended level of 40%ASIR for comparison purpose and different percentages of ASIR-V from 10% to 100% in a 10% increment. The CT attenuation values and SD of the subcutaneous fat, back muscle and descending aorta were measured at the level of tracheal carina of all reconstructed images. The signal-to-noise ratio (SNR) was calculated with SD representing image noise. The subjective image quality was independently evaluated by two experienced radiologists. For all ASIR-V images, the objective image noise (SD) of fat, muscle and aorta decreased and SNR increased along with increasing ASIR-V percentage. The SD of 30% ASIR-V to 100% ASIR-V was significantly lower than that of 40% ASIR (p < 0.05). In terms of subjective image evaluation, all ASIR-V reconstructions had good diagnostic acceptability. However, the 50% ASIR-V to 70% ASIR-V series showed significantly superior visibility of small structures when compared with the 40% ASIR and ASIR-V of other percentages (p < 0.05), and 60% ASIR-V was the best series of all ASIR-V images, with a highest subjective image quality. The image sharpness was significantly decreased in images reconstructed by 80% ASIR-V and higher. In routine dose, non-enhanced chest CT, ASIR-V shows greater potential in reducing image noise and artefacts and maintaining image sharpness when compared to the recommended level of 40%ASIR algorithm. Combining both the objective and subjective evaluation of images, non-enhanced chest CT images reconstructed with 60% ASIR-V have the highest image quality. Advances in knowledge: This is the first clinical study to evaluate the clinical value of ASIR-V in the same patients using the same CT scanner in the non-enhanced chest CT scans. It suggests that ASIR-V provides the better image quality and higher diagnostic confidence in comparison with ASIR algorithm.

Microphone directionality, pre-emphasis filter, and wind noise in cochlear implants.

PubMed

Chung, King; McKibben, Nicholas

2011-10-01

Wind noise can be a nuisance or a debilitating masker for cochlear implant users in outdoor environments. Previous studies indicated that wind noise at the microphone/hearing aid output had high levels of low-frequency energy and the amount of noise generated is related to the microphone directionality. Currently, cochlear implants only offer either directional microphones or omnidirectional microphones for users at-large. As all cochlear implants utilize pre-emphasis filters to reduce low-frequency energy before the signal is encoded, effective wind noise reduction algorithms for hearing aids might not be applicable for cochlear implants. The purposes of this study were to investigate the effect of microphone directionality on speech recognition and perceived sound quality of cochlear implant users in wind noise and to derive effective wind noise reduction strategies for cochlear implants. A repeated-measure design was used to examine the effects of spectral and temporal masking created by wind noise recorded through directional and omnidirectional microphones and the effects of pre-emphasis filters on cochlear implant performance. A digital hearing aid was programmed to have linear amplification and relatively flat in-situ frequency responses for the directional and omnidirectional modes. The hearing aid output was then recorded from 0 to 360° at flow velocities of 4.5 and 13.5 m/sec in a quiet wind tunnel. Sixteen postlingually deafened adult cochlear implant listeners who reported to be able to communicate on the phone with friends and family without text messages participated in the study. Cochlear implant users listened to speech in wind noise recorded at locations that the directional and omnidirectional microphones yielded the lowest noise levels. Cochlear implant listeners repeated the sentences and rated the sound quality of the testing materials. Spectral and temporal characteristics of flow noise, as well as speech and/or noise characteristics before and after the pre-emphasis filter, were analyzed. Correlation coefficients between speech recognition scores and crest factors of wind noise before and after pre-emphasis filtering were also calculated. Listeners obtained higher scores using the omnidirectional than the directional microphone mode at 13.5 m/sec, but they obtained similar speech recognition scores for the two microphone modes at 4.5 m/sec. Higher correlation coefficients were obtained between speech recognition scores and crest factors of wind noise after pre-emphasis filtering rather than before filtering. Cochlear implant users would benefit from both directional and omnidirectional microphones to reduce far-field background noise and near-field wind noise. Automatic microphone switching algorithms can be more effective if the incoming signal were analyzed after pre-emphasis filters for microphone switching decisions. American Academy of Audiology.

The simulation of magnetic resonance elastography through atherosclerosis.

PubMed

Thomas-Seale, L E J; Hollis, L; Klatt, D; Sack, I; Roberts, N; Pankaj, P; Hoskins, P R

2016-06-14

The clinical diagnosis of atherosclerosis via the measurement of stenosis size is widely acknowledged as an imperfect criterion. The vulnerability of an atherosclerotic plaque to rupture is associated with its mechanical properties. The potential to image these mechanical properties using magnetic resonance elastography (MRE) was investigated through synthetic datasets. An image of the steady state wave propagation, equivalent to the first harmonic, can be extracted directly from finite element analysis. Inversion of this displacement data yields a map of the shear modulus, known as an elastogram. The variation of plaque composition, stenosis size, Gaussian noise, filter thresholds and excitation frequency were explored. A decreasing mean shear modulus with an increasing lipid composition was identified through all stenosis sizes. However the inversion algorithm showed sensitivity to parameter variation leading to artefacts which disrupted both the elastograms and quantitative trends. As noise was increased up to a realistic level, the contrast was maintained between the fully fibrous and lipid plaques but lost between the interim compositions. Although incorporating a Butterworth filter improved the performance of the algorithm, restrictive filter thresholds resulted in a reduction of the sensitivity of the algorithm to composition and noise variation. Increasing the excitation frequency improved the techniques ability to image the magnitude of the shear modulus and identify a contrast between compositions. In conclusion, whilst the technique has the potential to image the shear modulus of atherosclerotic plaques, future research will require the integration of a heterogeneous inversion algorithm. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fully Convolutional Architecture for Low-Dose CT Image Noise Reduction

NASA Astrophysics Data System (ADS)

Badretale, S.; Shaker, F.; Babyn, P.; Alirezaie, J.

2017-10-01

One of the critical topics in medical low-dose Computed Tomography (CT) imaging is how best to maintain image quality. As the quality of images decreases with lowering the X-ray radiation dose, improving image quality is extremely important and challenging. We have proposed a novel approach to denoise low-dose CT images. Our algorithm learns directly from an end-to-end mapping from the low-dose Computed Tomography images for denoising the normal-dose CT images. Our method is based on a deep convolutional neural network with rectified linear units. By learning various low-level to high-level features from a low-dose image the proposed algorithm is capable of creating a high-quality denoised image. We demonstrate the superiority of our technique by comparing the results with two other state-of-the-art methods in terms of the peak signal to noise ratio, root mean square error, and a structural similarity index.

Commissioning of the FTS-2 Data Reduction Pipeline

NASA Astrophysics Data System (ADS)

Sherwood, M.; Naylor, D.; Gom, B.; Bell, G.; Friberg, P.; Bintley, D.

2015-09-01

FTS-2 is the intermediate resolution Fourier Transform Spectrometer coupled to the SCUBA-2 facility bolometer camera at the James Clerk Maxwell Telescope in Hawaii. Although in principle FTS instruments have the advantage of relatively simple optics compared to other spectrometers, they require more sophisticated data processing to compute spectra from the recorded interferogram signal. In the case of FTS-2, the complicated optical design required to interface with the existing telescope optics introduces performance compromises that complicate spectral and spatial calibration, and the response of the SCUBA-2 arrays introduce interferogram distortions that are a challenge for data reduction algorithms. We present an overview of the pipeline and discuss new algorithms that have been written to correct the noise introduced by unexpected behavior of the SCUBA-2 arrays.

Speckle noise reduction for optical coherence tomography based on adaptive 2D dictionary

NASA Astrophysics Data System (ADS)

Lv, Hongli; Fu, Shujun; Zhang, Caiming; Zhai, Lin

2018-05-01

As a high-resolution biomedical imaging modality, optical coherence tomography (OCT) is widely used in medical sciences. However, OCT images often suffer from speckle noise, which can mask some important image information, and thus reduce the accuracy of clinical diagnosis. Taking full advantage of nonlocal self-similarity and adaptive 2D-dictionary-based sparse representation, in this work, a speckle noise reduction algorithm is proposed for despeckling OCT images. To reduce speckle noise while preserving local image features, similar nonlocal patches are first extracted from the noisy image and put into groups using a gamma- distribution-based block matching method. An adaptive 2D dictionary is then learned for each patch group. Unlike traditional vector-based sparse coding, we express each image patch by the linear combination of a few matrices. This image-to-matrix method can exploit the local correlation between pixels. Since each image patch might belong to several groups, the despeckled OCT image is finally obtained by aggregating all filtered image patches. The experimental results demonstrate the superior performance of the proposed method over other state-of-the-art despeckling methods, in terms of objective metrics and visual inspection.

Penalized weighted least-squares approach for low-dose x-ray computed tomography

NASA Astrophysics Data System (ADS)

Wang, Jing; Li, Tianfang; Lu, Hongbing; Liang, Zhengrong

2006-03-01

The noise of low-dose computed tomography (CT) sinogram follows approximately a Gaussian distribution with nonlinear dependence between the sample mean and variance. The noise is statistically uncorrelated among detector bins at any view angle. However the correlation coefficient matrix of data signal indicates a strong signal correlation among neighboring views. Based on above observations, Karhunen-Loeve (KL) transform can be used to de-correlate the signal among the neighboring views. In each KL component, a penalized weighted least-squares (PWLS) objective function can be constructed and optimal sinogram can be estimated by minimizing the objective function, followed by filtered backprojection (FBP) for CT image reconstruction. In this work, we compared the KL-PWLS method with an iterative image reconstruction algorithm, which uses the Gauss-Seidel iterative calculation to minimize the PWLS objective function in image domain. We also compared the KL-PWLS with an iterative sinogram smoothing algorithm, which uses the iterated conditional mode calculation to minimize the PWLS objective function in sinogram space, followed by FBP for image reconstruction. Phantom experiments show a comparable performance of these three PWLS methods in suppressing the noise-induced artifacts and preserving resolution in reconstructed images. Computer simulation concurs with the phantom experiments in terms of noise-resolution tradeoff and detectability in low contrast environment. The KL-PWLS noise reduction may have the advantage in computation for low-dose CT imaging, especially for dynamic high-resolution studies.

Principal Component Noise Filtering for NAST-I Radiometric Calibration

NASA Technical Reports Server (NTRS)

Tian, Jialin; Smith, William L., Sr.

2011-01-01

The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed- Interferometer (NAST-I) instrument is a high-resolution scanning interferometer that measures emitted thermal radiation between 3.3 and 18 microns. The NAST-I radiometric calibration is achieved using internal blackbody calibration references at ambient and hot temperatures. In this paper, we introduce a refined calibration technique that utilizes a principal component (PC) noise filter to compensate for instrument distortions and artifacts, therefore, further improve the absolute radiometric calibration accuracy. To test the procedure and estimate the PC filter noise performance, we form dependent and independent test samples using odd and even sets of blackbody spectra. To determine the optimal number of eigenvectors, the PC filter algorithm is applied to both dependent and independent blackbody spectra with a varying number of eigenvectors. The optimal number of PCs is selected so that the total root-mean-square (RMS) error is minimized. To estimate the filter noise performance, we examine four different scenarios: apply PC filtering to both dependent and independent datasets, apply PC filtering to dependent calibration data only, apply PC filtering to independent data only, and no PC filters. The independent blackbody radiances are predicted for each case and comparisons are made. The results show significant reduction in noise in the final calibrated radiances with the implementation of the PC filtering algorithm.

Speckle reduction during all-fiber common-path optical coherence tomography of the cavernous nerves

NASA Astrophysics Data System (ADS)

Chitchian, Shahab; Fiddy, Michael; Fried, Nathaniel M.

2009-02-01

Improvements in identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery, which are responsible for erectile function, may improve nerve preservation and postoperative sexual potency. In this study, we use a rat prostate, ex vivo, to evaluate the feasibility of optical coherence tomography (OCT) as a diagnostic tool for real-time imaging and identification of the cavernous nerves. A novel OCT system based on an all single-mode fiber common-path interferometer-based scanning system is used for this purpose. A wavelet shrinkage denoising technique using Stein's unbiased risk estimator (SURE) algorithm to calculate a data-adaptive threshold is implemented for speckle noise reduction in the OCT image. The signal-to-noise ratio (SNR) was improved by 9 dB and the image quality metrics of the cavernous nerves also improved significantly.

Reduction of Magnetic Noise Associated with Ocean Waves by Sage-Husa Adaptive Kalman Filter in Towed Overhauser Marine Magnetic Sensor

NASA Astrophysics Data System (ADS)

GE, J.; Dong, H.; Liu, H.; Luo, W.

2016-12-01

In the extreme sea conditions and deep-sea detection, the towed Overhauser marine magnetic sensor is easily affected by the magnetic noise associated with ocean waves. We demonstrate the reduction of the magnetic noise by Sage-Husa adaptive Kalman filter. Based on Weaver's model, we analyze the induced magnetic field variations associated with the different ocean depths, wave periods and amplitudes in details. Furthermore, we take advantage of the classic Kalman filter to reduce the magnetic noise and improve the signal to noise ratio of the magnetic anomaly data. In the practical marine magnetic surveys, the extreme sea conditions can change priori statistics of the noise, and may decrease the effect of Kalman filtering estimation. To solve this problem, an improved Sage-Husa adaptive filtering algorithm is used to reduce the dependence on the prior statistics. In addition, we implement a towed Overhauser marine magnetometer (Figure 1) to test the proposed method, and it consists of a towfish, an Overhauser total field sensor, a console, and other condition monitoring sensors. Over all, the comparisons of simulation experiments with and without the filter show that the power spectral density of the magnetic noise is reduced to 0.1 nT/Hz1/2@1Hz from 1 nT/Hz1/2@1Hz. The contrasts between the Sage-Husa filter and the classic Kalman filter (Figure 2) show the filtering accuracy and adaptive capacity are improved.

High reliability - low noise radionuclide signature identification algorithms for border security applications

NASA Astrophysics Data System (ADS)

Lee, Sangkyu

Illicit trafficking and smuggling of radioactive materials and special nuclear materials (SNM) are considered as one of the most important recent global nuclear threats. Monitoring the transport and safety of radioisotopes and SNM are challenging due to their weak signals and easy shielding. Great efforts worldwide are focused at developing and improving the detection technologies and algorithms, for accurate and reliable detection of radioisotopes of interest in thus better securing the borders against nuclear threats. In general, radiation portal monitors enable detection of gamma and neutron emitting radioisotopes. Passive or active interrogation techniques, present and/or under the development, are all aimed at increasing accuracy, reliability, and in shortening the time of interrogation as well as the cost of the equipment. Equally important efforts are aimed at advancing algorithms to process the imaging data in an efficient manner providing reliable "readings" of the interiors of the examined volumes of various sizes, ranging from cargos to suitcases. The main objective of this thesis is to develop two synergistic algorithms with the goal to provide highly reliable - low noise identification of radioisotope signatures. These algorithms combine analysis of passive radioactive detection technique with active interrogation imaging techniques such as gamma radiography or muon tomography. One algorithm consists of gamma spectroscopy and cosmic muon tomography, and the other algorithm is based on gamma spectroscopy and gamma radiography. The purpose of fusing two detection methodologies per algorithm is to find both heavy-Z radioisotopes and shielding materials, since radionuclides can be identified with gamma spectroscopy, and shielding materials can be detected using muon tomography or gamma radiography. These combined algorithms are created and analyzed based on numerically generated images of various cargo sizes and materials. In summary, the three detection methodologies are fused into two algorithms with mathematical functions providing: reliable identification of radioisotopes in gamma spectroscopy; noise reduction and precision enhancement in muon tomography; and the atomic number and density estimation in gamma radiography. It is expected that these new algorithms maybe implemented at portal scanning systems with the goal to enhance the accuracy and reliability in detecting nuclear materials inside the cargo containers.

A new algorithm for automatic Outlier Detection in GPS Time Series

NASA Astrophysics Data System (ADS)

Cannavo', Flavio; Mattia, Mario; Rossi, Massimo; Palano, Mimmo; Bruno, Valentina

2010-05-01

Nowadays continuous GPS time series are considered a crucial product of GPS permanent networks, useful in many geo-science fields, such as active tectonics, seismology, crustal deformation and volcano monitoring (Altamimi et al. 2002, Elósegui et al. 2006, Aloisi et al. 2009). Although the GPS data elaboration software has increased in reliability, the time series are still affected by different kind of noise, from the intrinsic noise (e.g. thropospheric delay) to the un-modeled noise (e.g. cycle slips, satellite faults, parameters changing). Typically GPS Time Series present characteristic noise that is a linear combination of white noise and correlated colored noise, and this characteristic is fractal in the sense that is evident for every considered time scale or sampling rate. The un-modeled noise sources result in spikes, outliers and steps. These kind of errors can appreciably influence the estimation of velocities of the monitored sites. The outlier detection in generic time series is a widely treated problem in literature (Wei, 2005), while is not fully developed for the specific kind of GPS series. We propose a robust automatic procedure for cleaning the GPS time series from the outliers and, especially for long daily series, steps due to strong seismic or volcanic events or merely instrumentation changing such as antenna and receiver upgrades. The procedure is basically divided in two steps: a first step for the colored noise reduction and a second step for outlier detection through adaptive series segmentation. Both algorithms present novel ideas and are nearly unsupervised. In particular, we propose an algorithm to estimate an autoregressive model for colored noise in GPS time series in order to subtract the effect of non Gaussian noise on the series. This step is useful for the subsequent step (i.e. adaptive segmentation) which requires the hypothesis of Gaussian noise. The proposed algorithms are tested in a benchmark case study and the results confirm that the algorithms are effective and reasonable. Bibliography - Aloisi M., A. Bonaccorso, F. Cannavò, S. Gambino, M. Mattia, G. Puglisi, E. Boschi, A new dyke intrusion style for the Mount Etna May 2008 eruption modelled through continuous tilt and GPS data, Terra Nova, Volume 21 Issue 4 , Pages 316 - 321, doi: 10.1111/j.1365-3121.2009.00889.x (August 2009) - Altamimi Z., Sillard P., Boucher C., ITRF2000: A new release of the International Terrestrial Reference frame for earth science applications, J Geophys Res-Solid Earth, 107 (B10): art. no.-2214, (Oct 2002) - Elósegui, P., J. L. Davis, D. Oberlander, R. Baena, and G. Ekström , Accuracy of high-rate GPS for seismology, Geophys. Res. Lett., 33, L11308, doi:10.1029/2006GL026065 (2006) - Wei W. S., Time Series Analysis: Univariate and Multivariate Methods, Addison Wesley (2 edition), ISBN-10: 0321322169 (July, 2005)

Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms

NASA Astrophysics Data System (ADS)

Ott, Julien G.; Becce, Fabio; Monnin, Pascal; Schmidt, Sabine; Bochud, François O.; Verdun, Francis R.

2014-08-01

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.

Noise reduction in spectral CT: reducing dose and breaking the trade-off between image noise and energy bin selection.

PubMed

Leng, Shuai; Yu, Lifeng; Wang, Jia; Fletcher, Joel G; Mistretta, Charles A; McCollough, Cynthia H

2011-09-01

Our purpose was to reduce image noise in spectral CT by exploiting data redundancies in the energy domain to allow flexible selection of the number, width, and location of the energy bins. Using a variety of spectral CT imaging methods, conventional filtered backprojection (FBP) reconstructions were performed and resulting images were compared to those processed using a Local HighlY constrained backPRojection Reconstruction (HYPR-LR) algorithm. The mean and standard deviation of CT numbers were measured within regions of interest (ROIs), and results were compared between FBP and HYPR-LR. For these comparisons, the following spectral CT imaging methods were used:(i) numerical simulations based on a photon-counting, detector-based CT system, (ii) a photon-counting, detector-based micro CT system using rubidium and potassium chloride solutions, (iii) a commercial CT system equipped with integrating detectors utilizing tube potentials of 80, 100, 120, and 140 kV, and (iv) a clinical dual-energy CT examination. The effects of tube energy and energy bin width were evaluated appropriate to each CT system. The mean CT number in each ROI was unchanged between FBP and HYPR-LR images for each of the spectral CT imaging scenarios, irrespective of bin width or tube potential. However, image noise, as represented by the standard deviation of CT numbers in each ROI, was reduced by 36%-76%. In all scenarios, image noise after HYPR-LR algorithm was similar to that of composite images, which used all available photons. No difference in spatial resolution was observed between HYPR-LR processing and FBP. Dual energy patient data processed using HYPR-LR demonstrated reduced noise in the individual, low- and high-energy images, as well as in the material-specific basis images. Noise reduction can be accomplished for spectral CT by exploiting data redundancies in the energy domain. HYPR-LR is a robust method for reducing image noise in a variety of spectral CT imaging systems without losing spatial resolution or CT number accuracy. This method improves the flexibility to select energy bins in the manner that optimizes material identification and separation without paying the penalty of increased image noise or its corollary, increased patient dose.

Data reduction of isotope-resolved LC-MS spectra.

PubMed

Du, Peicheng; Sudha, Rajagopalan; Prystowsky, Michael B; Angeletti, Ruth Hogue

2007-06-01

Data reduction of liquid chromatography-mass spectrometry (LC-MS) spectra can be a challenge due to the inherent complexity of biological samples, noise and non-flat baseline. We present a new algorithm, LCMS-2D, for reliable data reduction of LC-MS proteomics data. LCMS-2D can reliably reduce LC-MS spectra with multiple scans to a list of elution peaks, and subsequently to a list of peptide masses. It is capable of noise removal, and deconvoluting peaks that overlap in m/z, in retention time, or both, by using a novel iterative peak-picking step, a 'rescue' step, and a modified variable selection method. LCMS-2D performs well with three sets of annotated LC-MS spectra, yielding results that are better than those from PepList, msInspect and the vendor software BioAnalyst. The software LCMS-2D is available under the GNU general public license from http://www.bioc.aecom.yu.edu/labs/angellab/as a standalone C program running on LINUX.

Diversity combining in laser Doppler vibrometry for improved signal reliability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dräbenstedt, Alexander

2014-05-27

Because of the speckle nature of the light reflected from rough surfaces the signal quality of a vibrometer suffers from varying signal power. Deep signal outages manifest themselves as noise bursts and spikes in the demodulated velocity signal. Here we show that the signal quality of a single point vibrometer can be substantially improved by diversity reception. This concept is widely used in RF communication and can be transferred into optical interferometry. When two statistically independent measurement channels are available which measure the same motion on the same spot, the probability for both channels to see a signal drop-out atmore » the same time is very low. We built a prototype instrument that uses polarization diversity to constitute two independent reception channels that are separately demodulated into velocity signals. Send and receive beams go through different parts of the aperture so that the beams can be spatially separated. The two velocity channels are mixed into one more reliable signal by a PC program in real time with the help of the signal power information. An algorithm has been developed that ensures a mixing of two or more channels with minimum resulting variance. The combination algorithm delivers also an equivalent signal power for the combined signal. The combined signal lacks the vast majority of spikes that are present in the raw signals and it extracts the true vibration information present in both channels. A statistical analysis shows that the probability for deep signal outages is largely decreased. A 60 fold improvement can be shown. The reduction of spikes and noise bursts reduces the noise in the spectral analysis of vibrations too. Over certain frequency bands a reduction of the noise density by a factor above 10 can be shown.« less

A generalized leaky FxLMS algorithm for tuning the waterbed effect of feedback active noise control systems

NASA Astrophysics Data System (ADS)

Wu, Lifu; Qiu, Xiaojun; Guo, Yecai

2018-06-01

To tune the noise amplification in the feedback system caused by the waterbed effect effectively, an adaptive algorithm is proposed in this paper by replacing the scalar leaky factor of the leaky FxLMS algorithm with a real symmetric Toeplitz matrix. The elements in the matrix are calculated explicitly according to the noise amplification constraints, which are defined based on a simple but efficient method. Simulations in an ANC headphone application demonstrate that the proposed algorithm can adjust the frequency band of noise amplification more effectively than the FxLMS algorithm and the leaky FxLMS algorithm.

Investigation of Diesel’s Residual Noise on Predictive Vehicles Noise Cancelling using LMS Adaptive Algorithm

NASA Astrophysics Data System (ADS)

Arttini Dwi Prasetyowati, Sri; Susanto, Adhi; Widihastuti, Ida

2017-04-01

Every noise problems require different solution. In this research, the noise that must be cancelled comes from roadway. Least Mean Square (LMS) adaptive is one of the algorithm that can be used to cancel that noise. Residual noise always appears and could not be erased completely. This research aims to know the characteristic of residual noise from vehicle’s noise and analysis so that it is no longer appearing as a problem. LMS algorithm was used to predict the vehicle’s noise and minimize the error. The distribution of the residual noise could be observed to determine the specificity of the residual noise. The statistic of the residual noise close to normal distribution with = 0,0435, = 1,13 and the autocorrelation of the residual noise forming impulse. As a conclusion the residual noise is insignificant.

A robust fuzzy local Information c-means clustering algorithm with noise detection

NASA Astrophysics Data System (ADS)

Shang, Jiayu; Li, Shiren; Huang, Junwei

2018-04-01

Fuzzy c-means clustering (FCM), especially with spatial constraints (FCM_S), is an effective algorithm suitable for image segmentation. Its reliability contributes not only to the presentation of fuzziness for belongingness of every pixel but also to exploitation of spatial contextual information. But these algorithms still remain some problems when processing the image with noise, they are sensitive to the parameters which have to be tuned according to prior knowledge of the noise. In this paper, we propose a new FCM algorithm, combining the gray constraints and spatial constraints, called spatial and gray-level denoised fuzzy c-means (SGDFCM) algorithm. This new algorithm conquers the parameter disadvantages mentioned above by considering the possibility of noise of each pixel, which aims to improve the robustness and obtain more detail information. Furthermore, the possibility of noise can be calculated in advance, which means the algorithm is effective and efficient.

BVI induced vibration and noise alleviation by active and passive approaches

NASA Astrophysics Data System (ADS)

Liu, Li

This dissertation describes the development of a comprehensive aeroelastic/aeroacoustic simulation capability for the modeling of vibration and noise in rotorcraft induced by blade-vortex interaction (BVI). Subsequently this capability is applied to study vibration and noise reduction, using active and passive control approaches. The active approach employed is the actively controlled partial span trailing edge flaps (ACF), implemented in single and dual, servo and plain flap configurations. The passive approach is based on varying the sweep and anhedral on the tip of the rotor. Two different modern helicopters are chosen as the baseline for the implementation of ACF approach, one resembling a four-bladed MBB BO-105 hingeless rotor and the other similar to a five-bladed MD-900 bearingless rotor. The structural model is based on a finite element approach capable of simulating composite helicopter blades with swept tips, and representing multiple load paths at the blade root which is a characteristic of bearingless rotors. An unsteady compressible aerodynamic model based on a rational function approximation (RFA) approach is combined with a free wake analysis which has been enhanced by improving the wake analysis resolution and modeling a dual vortex structure. These enhancements are important for capturing BVI effects. A method for predicting compressible unsteady blade surface pressure distribution on rotor blades has been developed, which is required by the acoustic analysis. A modified version of helicopter noise code WOPWOP with provisions for blade flexibility has been combined with the aeroelastic analysis to predict the BVI noise. Several variants of the higher harmonic control (HHC) algorithm have been applied for the active noise control, as well as the simultaneous vibration and noise control. Active control of BVI noise is accomplished using feedback from an onboard microphone. The simulation has been extensively validated against experimental data and other comprehensive rotorcraft codes, and overall good correlation is obtained. Subsequently, the effectiveness of the ACF approach for vibration and BVI noise reduction has been explored, using the two different helicopter configurations. Vibration reductions of up to 86% and 60% are shown for the hingeless and bearingless rotor, respectively. Noise reductions of up to 6dB and 3dB are also demonstrated for these two configurations. (Abstract shortened by UMI.)

Adaptive nonlocal means filtering based on local noise level for CT denoising

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Zhoubo; Trzasko, Joshua D.; Lake, David S.

2014-01-15

Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analyticalmore » noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the shape and peak frequency of the noise power spectrum better than commercial smoothing kernels, and indicate that the spatial resolution at low contrast levels is not significantly degraded. Both the subjective evaluation using the ACR phantom and the objective evaluation on a low-contrast detection task using a CHO model observer demonstrate an improvement on low-contrast performance. The GPU implementation can process and transfer 300 slice images within 5 min. On patient data, the adaptive NLM algorithm provides more effective denoising of CT data throughout a volume than standard NLM, and may allow significant lowering of radiation dose. After a two week pilot study of lower dose CT urography and CT enterography exams, both GI and GU radiology groups elected to proceed with permanent implementation of adaptive NLM in their GI and GU CT practices. Conclusions: This work describes and validates a computationally efficient technique for noise map estimation directly from CT images, and an adaptive NLM filtering based on this noise map, on phantom and patient data. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with clinical workflow. The adaptive NLM algorithm provides effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose.« less

Methodological aspects of an adaptive multidirectional pattern search to optimize speech perception using three hearing-aid algorithms

NASA Astrophysics Data System (ADS)

Franck, Bas A. M.; Dreschler, Wouter A.; Lyzenga, Johannes

2004-12-01

In this study we investigated the reliability and convergence characteristics of an adaptive multidirectional pattern search procedure, relative to a nonadaptive multidirectional pattern search procedure. The procedure was designed to optimize three speech-processing strategies. These comprise noise reduction, spectral enhancement, and spectral lift. The search is based on a paired-comparison paradigm, in which subjects evaluated the listening comfort of speech-in-noise fragments. The procedural and nonprocedural factors that influence the reliability and convergence of the procedure are studied using various test conditions. The test conditions combine different tests, initial settings, background noise types, and step size configurations. Seven normal hearing subjects participated in this study. The results indicate that the reliability of the optimization strategy may benefit from the use of an adaptive step size. Decreasing the step size increases accuracy, while increasing the step size can be beneficial to create clear perceptual differences in the comparisons. The reliability also depends on starting point, stop criterion, step size constraints, background noise, algorithms used, as well as the presence of drifting cues and suboptimal settings. There appears to be a trade-off between reliability and convergence, i.e., when the step size is enlarged the reliability improves, but the convergence deteriorates. .

Adaptively Tuned Iterative Low Dose CT Image Denoising

PubMed Central

Hashemi, SayedMasoud; Paul, Narinder S.; Beheshti, Soosan; Cobbold, Richard S. C.

2015-01-01

Improving image quality is a critical objective in low dose computed tomography (CT) imaging and is the primary focus of CT image denoising. State-of-the-art CT denoising algorithms are mainly based on iterative minimization of an objective function, in which the performance is controlled by regularization parameters. To achieve the best results, these should be chosen carefully. However, the parameter selection is typically performed in an ad hoc manner, which can cause the algorithms to converge slowly or become trapped in a local minimum. To overcome these issues a noise confidence region evaluation (NCRE) method is used, which evaluates the denoising residuals iteratively and compares their statistics with those produced by additive noise. It then updates the parameters at the end of each iteration to achieve a better match to the noise statistics. By combining NCRE with the fundamentals of block matching and 3D filtering (BM3D) approach, a new iterative CT image denoising method is proposed. It is shown that this new denoising method improves the BM3D performance in terms of both the mean square error and a structural similarity index. Moreover, simulations and patient results show that this method preserves the clinically important details of low dose CT images together with a substantial noise reduction. PMID:26089972

Wiener filter preprocessing for OFDM systems in the presence of both nonstationary and stationary phase noises

NASA Astrophysics Data System (ADS)

Zhong, Ke; Lei, Xia; Li, Shaoqian

2013-12-01

Statistics-based intercarrier interference (ICI) mitigation algorithm is proposed for orthogonal frequency division multiplexing systems in presence of both nonstationary and stationary phase noises. By utilizing the statistics of phase noise, which can be obtained from measurements or data sheets, a Wiener filter preprocessing algorithm for ICI mitigation is proposed. The proposed algorithm can be regarded as a performance-improving technique for the previous researches on phase noise cancelation. Simulation results show that the proposed algorithm can effectively mitigate ICI and lower the error floor, and therefore significantly improve the performances of previous researches on phase noise cancelation, especially in the presence of severe phase noise.

Measuring the performance of super-resolution reconstruction algorithms

NASA Astrophysics Data System (ADS)

Dijk, Judith; Schutte, Klamer; van Eekeren, Adam W. M.; Bijl, Piet

2012-06-01

For many military operations situational awareness is of great importance. This situational awareness and related tasks such as Target Acquisition can be acquired using cameras, of which the resolution is an important characteristic. Super resolution reconstruction algorithms can be used to improve the effective sensor resolution. In order to judge these algorithms and the conditions under which they operate best, performance evaluation methods are necessary. This evaluation, however, is not straightforward for several reasons. First of all, frequency-based evaluation techniques alone will not provide a correct answer, due to the fact that they are unable to discriminate between structure-related and noise-related effects. Secondly, most super-resolution packages perform additional image enhancement techniques such as noise reduction and edge enhancement. As these algorithms improve the results they cannot be evaluated separately. Thirdly, a single high-resolution ground truth is rarely available. Therefore, evaluation of the differences in high resolution between the estimated high resolution image and its ground truth is not that straightforward. Fourth, different artifacts can occur due to super-resolution reconstruction, which are not known on forehand and hence are difficult to evaluate. In this paper we present a set of new evaluation techniques to assess super-resolution reconstruction algorithms. Some of these evaluation techniques are derived from processing on dedicated (synthetic) imagery. Other evaluation techniques can be evaluated on both synthetic and natural images (real camera data). The result is a balanced set of evaluation algorithms that can be used to assess the performance of super-resolution reconstruction algorithms.

Can image enhancement allow radiation dose to be reduced whilst maintaining the perceived diagnostic image quality required for coronary angiography?

PubMed Central

Joshi, Anuja; Gislason-Lee, Amber J; Keeble, Claire; Sivananthan, Uduvil M

2017-01-01

Objective: The aim of this research was to quantify the reduction in radiation dose facilitated by image processing alone for percutaneous coronary intervention (PCI) patient angiograms, without reducing the perceived image quality required to confidently make a diagnosis. Methods: Incremental amounts of image noise were added to five PCI angiograms, simulating the angiogram as having been acquired at corresponding lower dose levels (10–89% dose reduction). 16 observers with relevant experience scored the image quality of these angiograms in 3 states—with no image processing and with 2 different modern image processing algorithms applied. These algorithms are used on state-of-the-art and previous generation cardiac interventional X-ray systems. Ordinal regression allowing for random effects and the delta method were used to quantify the dose reduction possible by the processing algorithms, for equivalent image quality scores. Results: Observers rated the quality of the images processed with the state-of-the-art and previous generation image processing with a 24.9% and 15.6% dose reduction, respectively, as equivalent in quality to the unenhanced images. The dose reduction facilitated by the state-of-the-art image processing relative to previous generation processing was 10.3%. Conclusion: Results demonstrate that statistically significant dose reduction can be facilitated with no loss in perceived image quality using modern image enhancement; the most recent processing algorithm was more effective in preserving image quality at lower doses. Advances in knowledge: Image enhancement was shown to maintain perceived image quality in coronary angiography at a reduced level of radiation dose using computer software to produce synthetic images from real angiograms simulating a reduction in dose. PMID:28124572

Achieving Routine Submillisievert CT Scanning: Report from the Summit on Management of Radiation Dose in CT

PubMed Central

Chen, Guang Hong; Kalender, Willi; Leng, Shuai; Samei, Ehsan; Taguchi, Katsuyuki; Wang, Ge; Yu, Lifeng; Pettigrew, Roderic I.

2012-01-01

This Special Report presents the consensus of the Summit on Management of Radiation Dose in Computed Tomography (CT) (held in February 2011), which brought together participants from academia, clinical practice, industry, and regulatory and funding agencies to identify the steps required to reduce the effective dose from routine CT examinations to less than 1 mSv. The most promising technologies and methods discussed at the summit include innovations and developments in x-ray sources; detectors; and image reconstruction, noise reduction, and postprocessing algorithms. Access to raw projection data and standard data sets for algorithm validation and optimization is a clear need, as is the need for new, clinically relevant metrics of image quality and diagnostic performance. Current commercially available techniques such as automatic exposure control, optimization of tube potential, beam-shaping filters, and dynamic z-axis collimators are important, and education to successfully implement these methods routinely is critically needed. Other methods that are just becoming widely available, such as iterative reconstruction, noise reduction, and postprocessing algorithms, will also have an important role. Together, these existing techniques can reduce dose by a factor of two to four. Technical advances that show considerable promise for additional dose reduction but are several years or more from commercial availability include compressed sensing, volume of interest and interior tomography techniques, and photon-counting detectors. This report offers a strategic roadmap for the CT user and research and manufacturer communities toward routinely achieving effective doses of less than 1 mSv, which is well below the average annual dose from naturally occurring sources of radiation. © RSNA, 2012 PMID:22692035

MO-FG-204-03: Using Edge-Preserving Algorithm for Significantly Improved Image-Domain Material Decomposition in Dual Energy CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, W; Niu, T; Xing, L

2015-06-15

Purpose: To significantly improve dual energy CT (DECT) imaging by establishing a new theoretical framework of image-domain material decomposition with incorporation of edge-preserving techniques. Methods: The proposed algorithm, HYPR-NLM, combines the edge-preserving non-local mean filter (NLM) with the HYPR-LR (Local HighlY constrained backPRojection Reconstruction) framework. Image denoising using HYPR-LR framework depends on the noise level of the composite image which is the average of the different energy images. For DECT, the composite image is the average of high- and low-energy images. To further reduce noise, one may want to increase the window size of the filter of the HYPR-LR, leadingmore » resolution degradation. By incorporating the NLM filtering and the HYPR-LR framework, HYPR-NLM reduces the boost material decomposition noise using energy information redundancies as well as the non-local mean. We demonstrate the noise reduction and resolution preservation of the algorithm with both iodine concentration numerical phantom and clinical patient data by comparing the HYPR-NLM algorithm to the direct matrix inversion, HYPR-LR and iterative image-domain material decomposition (Iter-DECT). Results: The results show iterative material decomposition method reduces noise to the lowest level and provides improved DECT images. HYPR-NLM significantly reduces noise while preserving the accuracy of quantitative measurement and resolution. For the iodine concentration numerical phantom, the averaged noise levels are about 2.0, 0.7, 0.2 and 0.4 for direct inversion, HYPR-LR, Iter- DECT and HYPR-NLM, respectively. For the patient data, the noise levels of the water images are about 0.36, 0.16, 0.12 and 0.13 for direct inversion, HYPR-LR, Iter-DECT and HYPR-NLM, respectively. Difference images of both HYPR-LR and Iter-DECT show edge effect, while no significant edge effect is shown for HYPR-NLM, suggesting spatial resolution is well preserved for HYPR-NLM. Conclusion: HYPR-NLM provides an effective way to reduce the generic magnified image noise of dual–energy material decomposition while preserving resolution. This work is supported in part by NIH grants 7R01HL111141 and 1R01-EB016777. This work is also supported by the Natural Science Foundation of China (NSFC Grant No. 81201091), Fundamental Research Funds for the Central Universities in China, and Fund Project for Excellent Abroad Scholar Personnel in Science and Technology.« less

Sign Language Recognition System using Neural Network for Digital Hardware Implementation

NASA Astrophysics Data System (ADS)

Vargas, Lorena P.; Barba, Leiner; Torres, C. O.; Mattos, L.

2011-01-01

This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

SU-E-I-13: Evaluation of Metal Artifact Reduction (MAR) Software On Computed Tomography (CT) Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, V; Kohli, K

2015-06-15

Purpose: A new commercially available metal artifact reduction (MAR) software in computed tomography (CT) imaging was evaluated with phantoms in the presence of metals. The goal was to assess the ability of the software to restore the CT number in the vicinity of the metals without impacting the image quality. Methods: A Catphan 504 was scanned with a GE Optima RT 580 CT scanner (GE Healthcare, Milwaukee, WI) and the images were reconstructed with and without the MAR software. Both datasets were analyzed with Image Owl QA software (Image Owl Inc, Greenwich, NY). CT number sensitometry, MTF, low contrast, uniformity,more » noise and spatial accuracy were compared for scans with and without MAR software. In addition, an in-house made phantom was scanned with and without a stainless steel insert at three different locations. The accuracy of the CT number and metal insert dimension were investigated as well. Results: Comparisons between scans with and without MAR algorithm on the Catphan phantom demonstrate similar results for image quality. However, noise was slightly higher for the MAR algorithm. Evaluation of the CT number at various locations of the in-house made phantom was also performed. The baseline HU, obtained from the scan without metal insert, was compared to scans with the stainless steel insert at 3 different locations. The HU difference between the baseline scan versus metal scan was improved when the MAR algorithm was applied. In addition, the physical diameter of the stainless steel rod was over-estimated by the MAR algorithm by 0.9 mm. Conclusion: This work indicates with the presence of metal in CT scans, the MAR algorithm is capable of providing a more accurate CT number without compromising the overall image quality. Future work will include the dosimetric impact on the MAR algorithm.« less

The combination of a reduction in contrast agent dose with low tube voltage and an adaptive statistical iterative reconstruction algorithm in CT enterography: Effects on image quality and radiation dose.

PubMed

Feng, Cui; Zhu, Di; Zou, Xianlun; Li, Anqin; Hu, Xuemei; Li, Zhen; Hu, Daoyu

2018-03-01

To investigate the subjective and quantitative image quality and radiation exposure of CT enterography (CTE) examination performed at low tube voltage and low concentration of contrast agent with adaptive statistical iterative reconstruction (ASIR) algorithm, compared with conventional CTE.One hundred thirty-seven patients with suspected or proved gastrointestinal diseases underwent contrast enhanced CTE in a multidetector computed tomography (MDCT) scanner. All cases were assigned to 2 groups. Group A (n = 79) underwent CT with low tube voltage based on patient body mass index (BMI) (BMI < 23 kg/m, 80 kVp; BMI ≥ 23 kg/m, 100 kVp) and low concentration of contrast agent (270 mg I/mL), the images were reconstructed with standard filtered back projection (FBP) algorithm and 50% ASIR algorithm. Group B (n = 58) underwent conventional CTE with 120 kVp and 350 mg I/mL contrast agent, the images were reconstructed with FBP algorithm. The computed tomography dose index volume (CTDIvol), dose length product (DLP), effective dose (ED), and total iodine dosage were calculated and compared. The CT values, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of the normal bowel wall, gastrointestinal lesions, and mesenteric vessels were assessed and compared. The subjective image quality was assessed independently and blindly by 2 radiologists using a 5-point Likert scale.The differences of values for CTDIvol (8.64 ± 2.72 vs 11.55 ± 3.95, P < .001), ED (6.34 ± 2.24 vs 8.52 ± 3.02, P < .001), and DLP (422.6 ± 149.40 vs 568.30 ± 213.90, P < .001) were significant between group A and group B, with a reduction of 25.2%, 25.7%, and 25.7% in group A, respectively. The total iodine dosage in group A was reduced by 26.1%. The subjective image quality did not differ between the 2 groups (P > .05) and all image quality scores were greater than or equal to 3 (moderate). Fifty percent ASIR-A group images provided lower image noise, but similar or higher quantitative image quality in comparison with FBP-B group images.Compared with the conventional protocol, CTE performed at low tube voltage, low concentration of contrast agent with 50% ASIR algorithm produce a diagnostically acceptable image quality with a mean ED of 6.34 mSv and a total iodine dose reduction of 26.1%.

Extremum seeking-based optimization of high voltage converter modulator rise-time

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scheinker, Alexander; Bland, Michael; Krstic, Miroslav

2013-02-01

We digitally implement an extremum seeking (ES) algorithm, which optimizes the rise time of the output voltage of a high voltage converter modulator (HVCM) at the Los Alamos Neutron Science Center (LANSCE) HVCM test stand by iteratively, simultaneously tuning the first 8 switching edges of each of the three phase drive waveforms (24 variables total). We achieve a 50 μs rise time, which is reduction in half compared to the 100 μs achieved at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory. Considering that HVCMs typically operate with an output voltage of 100 kV, with a 60Hz repetitionmore » rate, the 50 μs rise time reduction will result in very significant energy savings. The ES algorithm will prove successful, despite the noisy measurements and cost calculations, confirming the theoretical results that the algorithm is not affected by noise whose frequency components are independent of the perturbing frequencies.« less

Incrementing data quality of multi-frequency echograms using the Adaptive Wiener Filter (AWF) denoising algorithm

NASA Astrophysics Data System (ADS)

Peña, M.

2016-10-01

Achieving acceptable signal-to-noise ratio (SNR) can be difficult when working in sparsely populated waters and/or when species have low scattering such as fluid filled animals. The increasing use of higher frequencies and the study of deeper depths in fisheries acoustics, as well as the use of commercial vessels, is raising the need to employ good denoising algorithms. The use of a lower Sv threshold to remove noise or unwanted targets is not suitable in many cases and increases the relative background noise component in the echogram, demanding more effectiveness from denoising algorithms. The Adaptive Wiener Filter (AWF) denoising algorithm is presented in this study. The technique is based on the AWF commonly used in digital photography and video enhancement. The algorithm firstly increments the quality of the data with a variance-dependent smoothing, before estimating the noise level as the envelope of the Sv minima. The AWF denoising algorithm outperforms existing algorithms in the presence of gaussian, speckle and salt & pepper noise, although impulse noise needs to be previously removed. Cleaned echograms present homogenous echotraces with outlined edges.

Convergence and objective functions of some fault/noise-injection-based online learning algorithms for RBF networks.

PubMed

Ho, Kevin I-J; Leung, Chi-Sing; Sum, John

2010-06-01

In the last two decades, many online fault/noise injection algorithms have been developed to attain a fault tolerant neural network. However, not much theoretical works related to their convergence and objective functions have been reported. This paper studies six common fault/noise-injection-based online learning algorithms for radial basis function (RBF) networks, namely 1) injecting additive input noise, 2) injecting additive/multiplicative weight noise, 3) injecting multiplicative node noise, 4) injecting multiweight fault (random disconnection of weights), 5) injecting multinode fault during training, and 6) weight decay with injecting multinode fault. Based on the Gladyshev theorem, we show that the convergence of these six online algorithms is almost sure. Moreover, their true objective functions being minimized are derived. For injecting additive input noise during training, the objective function is identical to that of the Tikhonov regularizer approach. For injecting additive/multiplicative weight noise during training, the objective function is the simple mean square training error. Thus, injecting additive/multiplicative weight noise during training cannot improve the fault tolerance of an RBF network. Similar to injective additive input noise, the objective functions of other fault/noise-injection-based online algorithms contain a mean square error term and a specialized regularization term.

Multiadaptive Bionic Wavelet Transform: Application to ECG Denoising and Baseline Wandering Reduction

NASA Astrophysics Data System (ADS)

Sayadi, Omid; Shamsollahi, Mohammad B.

2007-12-01

We present a new modified wavelet transform, called the multiadaptive bionic wavelet transform (MABWT), that can be applied to ECG signals in order to remove noise from them under a wide range of variations for noise. By using the definition of bionic wavelet transform and adaptively determining both the center frequency of each scale together with the[InlineEquation not available: see fulltext.]-function, the problem of desired signal decomposition is solved. Applying a new proposed thresholding rule works successfully in denoising the ECG. Moreover by using the multiadaptation scheme, lowpass noisy interference effects on the baseline of ECG will be removed as a direct task. The method was extensively clinically tested with real and simulated ECG signals which showed high performance of noise reduction, comparable to those of wavelet transform (WT). Quantitative evaluation of the proposed algorithm shows that the average SNR improvement of MABWT is 1.82 dB more than the WT-based results, for the best case. Also the procedure has largely proved advantageous over wavelet-based methods for baseline wandering cancellation, including both DC components and baseline drifts.

Modular design and implementation of field-programmable-gate-array-based Gaussian noise generator

NASA Astrophysics Data System (ADS)

Li, Yuan-Ping; Lee, Ta-Sung; Hwang, Jeng-Kuang

2016-05-01

The modular design of a Gaussian noise generator (GNG) based on field-programmable gate array (FPGA) technology was studied. A new range reduction architecture was included in a series of elementary function evaluation modules and was integrated into the GNG system. The approximation and quantisation errors for the square root module with a first polynomial approximation were high; therefore, we used the central limit theorem (CLT) to improve the noise quality. This resulted in an output rate of one sample per clock cycle. We subsequently applied Newton's method for the square root module, thus eliminating the need for the use of the CLT because applying the CLT resulted in an output rate of two samples per clock cycle (>200 million samples per second). Two statistical tests confirmed that our GNG is of high quality. Furthermore, the range reduction, which is used to solve a limited interval of the function approximation algorithms of the System Generator platform using Xilinx FPGAs, appeared to have a higher numerical accuracy, was operated at >350 MHz, and can be suitably applied for any function evaluation.

Magnetic resonance image restoration via dictionary learning under spatially adaptive constraints.

PubMed

Wang, Shanshan; Xia, Yong; Dong, Pei; Feng, David Dagan; Luo, Jianhua; Huang, Qiu

2013-01-01

This paper proposes a spatially adaptive constrained dictionary learning (SAC-DL) algorithm for Rician noise removal in magnitude magnetic resonance (MR) images. This algorithm explores both the strength of dictionary learning to preserve image structures and the robustness of local variance estimation to remove signal-dependent Rician noise. The magnitude image is first separated into a number of partly overlapping image patches. The statistics of each patch are collected and analyzed to obtain a local noise variance. To better adapt to Rician noise, a correction factor is formulated with the local signal-to-noise ratio (SNR). Finally, the trained dictionary is used to denoise each image patch under spatially adaptive constraints. The proposed algorithm has been compared to the popular nonlocal means (NLM) filtering and unbiased NLM (UNLM) algorithm on simulated T1-weighted, T2-weighted and PD-weighted MR images. Our results suggest that the SAC-DL algorithm preserves more image structures while effectively removing the noise than NLM and it is also superior to UNLM at low noise levels.

A semi-learning algorithm for noise rejection: an fNIRS study on ADHD children

NASA Astrophysics Data System (ADS)

Sutoko, Stephanie; Funane, Tsukasa; Katura, Takusige; Sato, Hiroki; Kiguchi, Masashi; Maki, Atsushi; Monden, Yukifumi; Nagashima, Masako; Yamagata, Takanori; Dan, Ippeita

2017-02-01

In pediatrics studies, the quality of functional near infrared spectroscopy (fNIRS) signals is often reduced by motion artifacts. These artifacts likely mislead brain functionality analysis, causing false discoveries. While noise correction methods and their performance have been investigated, these methods require several parameter assumptions that apparently result in noise overfitting. In contrast, the rejection of noisy signals serves as a preferable method because it maintains the originality of the signal waveform. Here, we describe a semi-learning algorithm to detect and eliminate noisy signals. The algorithm dynamically adjusts noise detection according to the predetermined noise criteria, which are spikes, unusual activation values (averaged amplitude signals within the brain activation period), and high activation variances (among trials). Criteria were sequentially organized in the algorithm and orderly assessed signals based on each criterion. By initially setting an acceptable rejection rate, particular criteria causing excessive data rejections are neglected, whereas others with tolerable rejections practically eliminate noises. fNIRS data measured during the attention response paradigm (oddball task) in children with attention deficit/hyperactivity disorder (ADHD) were utilized to evaluate and optimize the algorithm's performance. This algorithm successfully substituted the visual noise identification done in the previous studies and consistently found significantly lower activation of the right prefrontal and parietal cortices in ADHD patients than in typical developing children. Thus, we conclude that the semi-learning algorithm confers more objective and standardized judgment for noise rejection and presents a promising alternative to visual noise rejection

Sensor fusion methods for reducing false alarms in heart rate monitoring.

PubMed

Borges, Gabriel; Brusamarello, Valner

2016-12-01

Automatic patient monitoring is an essential resource in hospitals for good health care management. While alarms caused by abnormal physiological conditions are important for the delivery of fast treatment, they can be also a source of unnecessary noise because of false alarms caused by electromagnetic interference or motion artifacts. One significant source of false alarms is related to heart rate, which is triggered when the heart rhythm of the patient is too fast or too slow. In this work, the fusion of different physiological sensors is explored in order to create a robust heart rate estimation. A set of algorithms using heart rate variability index, Bayesian inference, neural networks, fuzzy logic and majority voting is proposed to fuse the information from the electrocardiogram, arterial blood pressure and photoplethysmogram. Three kinds of information are extracted from each source, namely, heart rate variability, the heart rate difference between sensors and the spectral analysis of low and high noise of each sensor. This information is used as input to the algorithms. Twenty recordings selected from the MIMIC database were used to validate the system. The results showed that neural networks fusion had the best false alarm reduction of 92.5 %, while the Bayesian technique had a reduction of 84.3 %, fuzzy logic 80.6 %, majority voter 72.5 % and the heart rate variability index 67.5 %. Therefore, the proposed algorithms showed good performance and could be useful in bedside monitors.

Development, implementation and evaluation of a dedicated metal artefact reduction method for interventional flat-detector CT.

PubMed

Prell, D; Kalender, W A; Kyriakou, Y

2010-12-01

The purpose of this study was to develop, implement and evaluate a dedicated metal artefact reduction (MAR) method for flat-detector CT (FDCT). The algorithm uses the multidimensional raw data space to calculate surrogate attenuation values for the original metal traces in the raw data domain. The metal traces are detected automatically by a three-dimensional, threshold-based segmentation algorithm in an initial reconstructed image volume, based on twofold histogram information for calculating appropriate metal thresholds. These thresholds are combined with constrained morphological operations in the projection domain. A subsequent reconstruction of the modified raw data yields an artefact-reduced image volume that is further processed by a combining procedure that reinserts the missing metal information. For image quality assessment, measurements on semi-anthropomorphic phantoms containing metallic inserts were evaluated in terms of CT value accuracy, image noise and spatial resolution before and after correction. Measurements of the same phantoms without prostheses were used as ground truth for comparison. Cadaver measurements were performed on complex and realistic cases and to determine the influences of our correction method on the tissue surrounding the prostheses. The results showed a significant reduction of metal-induced streak artefacts (CT value differences were reduced to below 22 HU and image noise reduction of up to 200%). The cadaver measurements showed excellent results for imaging areas close to the implant and exceptional artefact suppression in these areas. Furthermore, measurements in the knee and spine regions confirmed the superiority of our method to standard one-dimensional, linear interpolation.

Robustness analysis of superpixel algorithms to image blur, additive Gaussian noise, and impulse noise

NASA Astrophysics Data System (ADS)

Brekhna, Brekhna; Mahmood, Arif; Zhou, Yuanfeng; Zhang, Caiming

2017-11-01

Superpixels have gradually become popular in computer vision and image processing applications. However, no comprehensive study has been performed to evaluate the robustness of superpixel algorithms in regard to common forms of noise in natural images. We evaluated the robustness of 11 recently proposed algorithms to different types of noise. The images were corrupted with various degrees of Gaussian blur, additive white Gaussian noise, and impulse noise that either made the object boundaries weak or added extra information to it. We performed a robustness analysis of simple linear iterative clustering (SLIC), Voronoi Cells (VCells), flooding-based superpixel generation (FCCS), bilateral geodesic distance (Bilateral-G), superpixel via geodesic distance (SSS-G), manifold SLIC (M-SLIC), Turbopixels, superpixels extracted via energy-driven sampling (SEEDS), lazy random walk (LRW), real-time superpixel segmentation by DBSCAN clustering, and video supervoxels using partially absorbing random walks (PARW) algorithms. The evaluation process was carried out both qualitatively and quantitatively. For quantitative performance comparison, we used achievable segmentation accuracy (ASA), compactness, under-segmentation error (USE), and boundary recall (BR) on the Berkeley image database. The results demonstrated that all algorithms suffered performance degradation due to noise. For Gaussian blur, Bilateral-G exhibited optimal results for ASA and USE measures, SLIC yielded optimal compactness, whereas FCCS and DBSCAN remained optimal for BR. For the case of additive Gaussian and impulse noises, FCCS exhibited optimal results for ASA, USE, and BR, whereas Bilateral-G remained a close competitor in ASA and USE for Gaussian noise only. Additionally, Turbopixel demonstrated optimal performance for compactness for both types of noise. Thus, no single algorithm was able to yield optimal results for all three types of noise across all performance measures. Conclusively, to solve real-world problems effectively, more robust superpixel algorithms must be developed.

False-nearest-neighbors algorithm and noise-corrupted time series

NASA Astrophysics Data System (ADS)

Rhodes, Carl; Morari, Manfred

1997-05-01

The false-nearest-neighbors (FNN) algorithm was originally developed to determine the embedding dimension for autonomous time series. For noise-free computer-generated time series, the algorithm does a good job in predicting the embedding dimension. However, the problem of predicting the embedding dimension when the time-series data are corrupted by noise was not fully examined in the original studies of the FNN algorithm. Here it is shown that with large data sets, even small amounts of noise can lead to incorrect prediction of the embedding dimension. Surprisingly, as the length of the time series analyzed by FNN grows larger, the cause of incorrect prediction becomes more pronounced. An analysis of the effect of noise on the FNN algorithm and a solution for dealing with the effects of noise are given here. Some results on the theoretically correct choice of the FNN threshold are also presented.

Linear-time general decoding algorithm for the surface code

NASA Astrophysics Data System (ADS)

Darmawan, Andrew S.; Poulin, David

2018-05-01

A quantum error correcting protocol can be substantially improved by taking into account features of the physical noise process. We present an efficient decoder for the surface code which can account for general noise features, including coherences and correlations. We demonstrate that the decoder significantly outperforms the conventional matching algorithm on a variety of noise models, including non-Pauli noise and spatially correlated noise. The algorithm is based on an approximate calculation of the logical channel using a tensor-network description of the noisy state.

A novel encoding Lempel-Ziv complexity algorithm for quantifying the irregularity of physiological time series.

PubMed

Zhang, Yatao; Wei, Shoushui; Liu, Hai; Zhao, Lina; Liu, Chengyu

2016-09-01

The Lempel-Ziv (LZ) complexity and its variants have been extensively used to analyze the irregularity of physiological time series. To date, these measures cannot explicitly discern between the irregularity and the chaotic characteristics of physiological time series. Our study compared the performance of an encoding LZ (ELZ) complexity algorithm, a novel variant of the LZ complexity algorithm, with those of the classic LZ (CLZ) and multistate LZ (MLZ) complexity algorithms. Simulation experiments on Gaussian noise, logistic chaotic, and periodic time series showed that only the ELZ algorithm monotonically declined with the reduction in irregularity in time series, whereas the CLZ and MLZ approaches yielded overlapped values for chaotic time series and time series mixed with Gaussian noise, demonstrating the accuracy of the proposed ELZ algorithm in capturing the irregularity, rather than the complexity, of physiological time series. In addition, the effect of sequence length on the ELZ algorithm was more stable compared with those on CLZ and MLZ, especially when the sequence length was longer than 300. A sensitivity analysis for all three LZ algorithms revealed that both the MLZ and the ELZ algorithms could respond to the change in time sequences, whereas the CLZ approach could not. Cardiac interbeat (RR) interval time series from the MIT-BIH database were also evaluated, and the results showed that the ELZ algorithm could accurately measure the inherent irregularity of the RR interval time series, as indicated by lower LZ values yielded from a congestive heart failure group versus those yielded from a normal sinus rhythm group (p < 0.01). Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Fast noise level estimation algorithm based on principal component analysis transform and nonlinear rectification

NASA Astrophysics Data System (ADS)

Xu, Shaoping; Zeng, Xiaoxia; Jiang, Yinnan; Tang, Yiling

2018-01-01

We proposed a noniterative principal component analysis (PCA)-based noise level estimation (NLE) algorithm that addresses the problem of estimating the noise level with a two-step scheme. First, we randomly extracted a number of raw patches from a given noisy image and took the smallest eigenvalue of the covariance matrix of the raw patches as the preliminary estimation of the noise level. Next, the final estimation was directly obtained with a nonlinear mapping (rectification) function that was trained on some representative noisy images corrupted with different known noise levels. Compared with the state-of-art NLE algorithms, the experiment results show that the proposed NLE algorithm can reliably infer the noise level and has robust performance over a wide range of image contents and noise levels, showing a good compromise between speed and accuracy in general.

Robust local search for spacecraft operations using adaptive noise

NASA Technical Reports Server (NTRS)

Fukunaga, Alex S.; Rabideau, Gregg; Chien, Steve

2004-01-01

Randomization is a standard technique for improving the performance of local search algorithms for constraint satisfaction. However, it is well-known that local search algorithms are constraints satisfaction. However, it is well-known that local search algorithms are to the noise values selected. We investigate the use of an adaptive noise mechanism in an iterative repair-based planner/scheduler for spacecraft operations. Preliminary results indicate that adaptive noise makes the use of randomized repair moves safe and robust; that is, using adaptive noise makes it possible to consistently achieve, performance comparable with the best tuned noise setting without the need for manually tuning the noise parameter.

Hybrid Feedforward-Feedback Noise Control Using Virtual Sensors

NASA Technical Reports Server (NTRS)

Bean, Jacob; Fuller, Chris; Schiller, Noah

2016-01-01

Several approaches to active noise control using virtual sensors are evaluated for eventual use in an active headrest. Specifically, adaptive feedforward, feedback, and hybrid control structures are compared. Each controller incorporates the traditional filtered-x least mean squares algorithm. The feedback controller is arranged in an internal model configuration to draw comparisons with standard feedforward control theory results. Simulation and experimental results are presented that illustrate each controllers ability to minimize the pressure at both physical and virtual microphone locations. The remote microphone technique is used to obtain pressure estimates at the virtual locations. It is shown that a hybrid controller offers performance benefits over the traditional feedforward and feedback controllers. Stability issues associated with feedback and hybrid controllers are also addressed. Experimental results show that 15-20 dB reduction in broadband disturbances can be achieved by minimizing the measured pressure, whereas 10-15 dB reduction is obtained when minimizing the estimated pressure at a virtual location.

Experimental and Numerical Analysis of Structural Acousticcontrol Interior Noise Reduction

NASA Technical Reports Server (NTRS)

Mei, Chuh; Bevan, Jeffrey S.

1999-01-01

The research results contained in this technical report were performed under the NASA grant entitled "Experimental and Numerical Structural Acoustic Control for Interior Noise Reduction". The report is based essentially on partial progress of the Ph.D. dissertation prepared by Jeffrey S. Bevan under direct guidance of Dr. Chuh Mei. The document presents a finite element formulation and control of sound radiated from cylindrical panels embedded with piezoceramic actuators. The extended MIN6 shallow shell element is fully electrical-structural coupled. A piezoelectric modal actuator participation (PMAP) is defined which indicates the actuator performance to each of the offending modes. Genetic algorithm is also employed to validate the sensor and actuator locations determined by the PMAP criteria. The work was conducted at the Department of Aerospace Engineering, Old Dominion University. Mr. Travis L. Turner, Structural Acoustics Branch, NASA Langley Research Center is the technical monitor.

Comparison of spike-sorting algorithms for future hardware implementation.

PubMed

Gibson, Sarah; Judy, Jack W; Markovic, Dejan

2008-01-01

Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.

Detection of honeycomb cell walls from measurement data based on Harris corner detection algorithm

NASA Astrophysics Data System (ADS)

Qin, Yan; Dong, Zhigang; Kang, Renke; Yang, Jie; Ayinde, Babajide O.

2018-06-01

A honeycomb core is a discontinuous material with a thin-wall structure—a characteristic that makes accurate surface measurement difficult. This paper presents a cell wall detection method based on the Harris corner detection algorithm using laser measurement data. The vertexes of honeycomb cores are recognized with two different methods: one method is the reduction of data density, and the other is the optimization of the threshold of the Harris corner detection algorithm. Each cell wall is then identified in accordance with the neighboring relationships of its vertexes. Experiments were carried out for different types and surface shapes of honeycomb cores, where the proposed method was proved effective in dealing with noise due to burrs and/or deformation of cell walls.

The a priori SDR Estimation Techniques with Reduced Speech Distortion for Acoustic Echo and Noise Suppression

NASA Astrophysics Data System (ADS)

Thoonsaengngam, Rattapol; Tangsangiumvisai, Nisachon

This paper proposes an enhanced method for estimating the a priori Signal-to-Disturbance Ratio (SDR) to be employed in the Acoustic Echo and Noise Suppression (AENS) system for full-duplex hands-free communications. The proposed a priori SDR estimation technique is modified based upon the Two-Step Noise Reduction (TSNR) algorithm to suppress the background noise while preserving speech spectral components. In addition, a practical approach to determine accurately the Echo Spectrum Variance (ESV) is presented based upon the linear relationship assumption between the power spectrum of far-end speech and acoustic echo signals. The ESV estimation technique is then employed to alleviate the acoustic echo problem. The performance of the AENS system that employs these two proposed estimation techniques is evaluated through the Echo Attenuation (EA), Noise Attenuation (NA), and two speech distortion measures. Simulation results based upon real speech signals guarantee that our improved AENS system is able to mitigate efficiently the problem of acoustic echo and background noise, while preserving the speech quality and speech intelligibility.

Improved Kalman Filter Method for Measurement Noise Reduction in Multi Sensor RFID Systems

PubMed Central

Eom, Ki Hwan; Lee, Seung Joon; Kyung, Yeo Sun; Lee, Chang Won; Kim, Min Chul; Jung, Kyung Kwon

2011-01-01

Recently, the range of available Radio Frequency Identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less Mean Squared Error (MSE) than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments. PMID:22346641

Improved Kalman filter method for measurement noise reduction in multi sensor RFID systems.

PubMed

Eom, Ki Hwan; Lee, Seung Joon; Kyung, Yeo Sun; Lee, Chang Won; Kim, Min Chul; Jung, Kyung Kwon

2011-01-01

Recently, the range of available radio frequency identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less mean squared error (MSE) than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.

GIFTS SM EDU Data Processing and Algorithms

NASA Technical Reports Server (NTRS)

Tian, Jialin; Johnson, David G.; Reisse, Robert A.; Gazarik, Michael J.

2007-01-01

The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) Sensor Module (SM) Engineering Demonstration Unit (EDU) is a high resolution spectral imager designed to measure infrared (IR) radiances using a Fourier transform spectrometer (FTS). The GIFTS instrument employs three Focal Plane Arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The raw interferogram measurements are radiometrically and spectrally calibrated to produce radiance spectra, which are further processed to obtain atmospheric profiles via retrieval algorithms. This paper describes the processing algorithms involved in the calibration stage. The calibration procedures can be subdivided into three stages. In the pre-calibration stage, a phase correction algorithm is applied to the decimated and filtered complex interferogram. The resulting imaginary part of the spectrum contains only the noise component of the uncorrected spectrum. Additional random noise reduction can be accomplished by applying a spectral smoothing routine to the phase-corrected blackbody reference spectra. In the radiometric calibration stage, we first compute the spectral responsivity based on the previous results, from which, the calibrated ambient blackbody (ABB), hot blackbody (HBB), and scene spectra can be obtained. During the post-processing stage, we estimate the noise equivalent spectral radiance (NESR) from the calibrated ABB and HBB spectra. We then implement a correction scheme that compensates for the effect of fore-optics offsets. Finally, for off-axis pixels, the FPA off-axis effects correction is performed. To estimate the performance of the entire FPA, we developed an efficient method of generating pixel performance assessments. In addition, a random pixel selection scheme is designed based on the pixel performance evaluation.

A de-noising algorithm based on wavelet threshold-exponential adaptive window width-fitting for ground electrical source airborne transient electromagnetic signal

NASA Astrophysics Data System (ADS)

Ji, Yanju; Li, Dongsheng; Yu, Mingmei; Wang, Yuan; Wu, Qiong; Lin, Jun

2016-05-01

The ground electrical source airborne transient electromagnetic system (GREATEM) on an unmanned aircraft enjoys considerable prospecting depth, lateral resolution and detection efficiency, etc. In recent years it has become an important technical means of rapid resources exploration. However, GREATEM data are extremely vulnerable to stationary white noise and non-stationary electromagnetic noise (sferics noise, aircraft engine noise and other human electromagnetic noises). These noises will cause degradation of the imaging quality for data interpretation. Based on the characteristics of the GREATEM data and major noises, we propose a de-noising algorithm utilizing wavelet threshold method and exponential adaptive window width-fitting. Firstly, the white noise is filtered in the measured data using the wavelet threshold method. Then, the data are segmented using data window whose step length is even logarithmic intervals. The data polluted by electromagnetic noise are identified within each window based on the discriminating principle of energy detection, and the attenuation characteristics of the data slope are extracted. Eventually, an exponential fitting algorithm is adopted to fit the attenuation curve of each window, and the data polluted by non-stationary electromagnetic noise are replaced with their fitting results. Thus the non-stationary electromagnetic noise can be effectively removed. The proposed algorithm is verified by the synthetic and real GREATEM signals. The results show that in GREATEM signal, stationary white noise and non-stationary electromagnetic noise can be effectively filtered using the wavelet threshold-exponential adaptive window width-fitting algorithm, which enhances the imaging quality.

Radiation dose reduction using 100-kVp and a sinogram-affirmed iterative reconstruction algorithm in adolescent head CT: Impact on grey-white matter contrast and image noise.

PubMed

Nagayama, Yasunori; Nakaura, Takeshi; Tsuji, Akinori; Urata, Joji; Furusawa, Mitsuhiro; Yuki, Hideaki; Hirarta, Kenichiro; Kidoh, Masafumi; Oda, Seitaro; Utsunomiya, Daisuke; Yamashita, Yasuyuki

2017-07-01

To retrospectively evaluate the image quality and radiation dose of 100-kVp scans with sinogram-affirmed iterative reconstruction (IR) for unenhanced head CT in adolescents. Sixty-nine patients aged 12-17 years underwent head CT under 120- (n = 34) or 100-kVp (n = 35) protocols. The 120-kVp images were reconstructed with filtered back-projection (FBP), 100-kVp images with FBP (100-kVp-F) and sinogram-affirmed IR (100-kVp-S). We compared the effective dose (ED), grey-white matter (GM-WM) contrast, image noise, and contrast-to-noise ratio (CNR) between protocols in supratentorial (ST) and posterior fossa (PS). We also assessed GM-WM contrast, image noise, sharpness, artifacts, and overall image quality on a four-point scale. ED was 46% lower with 100- than 120-kVp (p < 0.001). GM-WM contrast was higher, and image noise was lower, on 100-kVp-S than 120-kVp at ST (p < 0.001). CNR of 100-kVp-S was higher than of 120-kVp (p < 0.001). GM-WM contrast of 100-kVp-S was subjectively rated as better than of 120-kVp (p < 0.001). There were no significant differences in the other criteria between 100-kVp-S and 120-kVp (p = 0.072-0.966). The 100-kVp with sinogram-affirmed IR facilitated dramatic radiation reduction and better GM-WM contrast without increasing image noise in adolescent head CT. • 100-kVp head CT provides 46% radiation dose reduction compared with 120-kVp. • 100-kVp scanning improves subjective and objective GM-WM contrast. • Sinogram-affirmed IR decreases head CT image noise, especially in supratentorial region. • 100-kVp protocol with sinogram-affirmed IR is suited for adolescent head CT.

Penalized Weighted Least-Squares Approach to Sinogram Noise Reduction and Image Reconstruction for Low-Dose X-Ray Computed Tomography

PubMed Central

Wang, Jing; Li, Tianfang; Lu, Hongbing; Liang, Zhengrong

2006-01-01

Reconstructing low-dose X-ray CT (computed tomography) images is a noise problem. This work investigated a penalized weighted least-squares (PWLS) approach to address this problem in two dimensions, where the WLS considers first- and second-order noise moments and the penalty models signal spatial correlations. Three different implementations were studied for the PWLS minimization. One utilizes a MRF (Markov random field) Gibbs functional to consider spatial correlations among nearby detector bins and projection views in sinogram space and minimizes the PWLS cost function by iterative Gauss-Seidel algorithm. Another employs Karhunen-Loève (KL) transform to de-correlate data signals among nearby views and minimizes the PWLS adaptively to each KL component by analytical calculation, where the spatial correlation among nearby bins is modeled by the same Gibbs functional. The third one models the spatial correlations among image pixels in image domain also by a MRF Gibbs functional and minimizes the PWLS by iterative successive over-relaxation algorithm. In these three implementations, a quadratic functional regularization was chosen for the MRF model. Phantom experiments showed a comparable performance of these three PWLS-based methods in terms of suppressing noise-induced streak artifacts and preserving resolution in the reconstructed images. Computer simulations concurred with the phantom experiments in terms of noise-resolution tradeoff and detectability in low contrast environment. The KL-PWLS implementation may have the advantage in terms of computation for high-resolution dynamic low-dose CT imaging. PMID:17024831

Noise effect in an improved conjugate gradient algorithm to invert particle size distribution and the algorithm amendment.

PubMed

Wei, Yongjie; Ge, Baozhen; Wei, Yaolin

2009-03-20

In general, model-independent algorithms are sensitive to noise during laser particle size measurement. An improved conjugate gradient algorithm (ICGA) that can be used to invert particle size distribution (PSD) from diffraction data is presented. By use of the ICGA to invert simulated data with multiplicative or additive noise, we determined that additive noise is the main factor that induces distorted results. Thus the ICGA is amended by introduction of an iteration step-adjusting parameter and is used experimentally on simulated data and some samples. The experimental results show that the sensitivity of the ICGA to noise is reduced and the inverted results are in accord with the real PSD.

Real-time spectrum estimation–based dual-channel speech-enhancement algorithm for cochlear implant

PubMed Central

2012-01-01

Background Improvement of the cochlear implant (CI) front-end signal acquisition is needed to increase speech recognition in noisy environments. To suppress the directional noise, we introduce a speech-enhancement algorithm based on microphone array beamforming and spectral estimation. The experimental results indicate that this method is robust to directional mobile noise and strongly enhances the desired speech, thereby improving the performance of CI devices in a noisy environment. Methods The spectrum estimation and the array beamforming methods were combined to suppress the ambient noise. The directivity coefficient was estimated in the noise-only intervals, and was updated to fit for the mobile noise. Results The proposed algorithm was realized in the CI speech strategy. For actual parameters, we use Maxflat filter to obtain fractional sampling points and cepstrum method to differentiate the desired speech frame and the noise frame. The broadband adjustment coefficients were added to compensate the energy loss in the low frequency band. Discussions The approximation of the directivity coefficient is tested and the errors are discussed. We also analyze the algorithm constraint for noise estimation and distortion in CI processing. The performance of the proposed algorithm is analyzed and further be compared with other prevalent methods. Conclusions The hardware platform was constructed for the experiments. The speech-enhancement results showed that our algorithm can suppresses the non-stationary noise with high SNR. Excellent performance of the proposed algorithm was obtained in the speech enhancement experiments and mobile testing. And signal distortion results indicate that this algorithm is robust with high SNR improvement and low speech distortion. PMID:23006896

MO-PIS-Exhibit Hall-01: Imaging: CT Dose Optimization Technologies I

DOE Office of Scientific and Technical Information (OSTI.GOV)

Denison, K; Smith, S

Partners in Solutions is an exciting new program in which AAPM partners with our vendors to present practical “hands-on” information about the equipment and software systems that we use in our clinics. The imaging topic this year is CT scanner dose optimization capabilities. Note that the sessions are being held in a special purpose room built on the Exhibit Hall Floor, to encourage further interaction with the vendors. Dose Optimization Capabilities of GE Computed Tomography Scanners Presentation Time: 11:15 – 11:45 AM GE Healthcare is dedicated to the delivery of high quality clinical images through the development of technologies, whichmore » optimize the application of ionizing radiation. In computed tomography, dose management solutions fall into four categories: employs projection data and statistical modeling to decrease noise in the reconstructed image - creating an opportunity for mA reduction in the acquisition of diagnostic images. Veo represents true Model Based Iterative Reconstruction (MBiR). Using high-level algorithms in tandem with advanced computing power, Veo enables lower pixel noise standard deviation and improved spatial resolution within a single image. Advanced Adaptive Image Filters allow for maintenance of spatial resolution while reducing image noise. Examples of adaptive image space filters include Neuro 3-D filters and Cardiac Noise Reduction Filters. AutomA adjusts mA along the z-axis and is the CT equivalent of auto exposure control in conventional x-ray systems. Dynamic Z-axis Tracking offers an additional opportunity for dose reduction in helical acquisitions while SmartTrack Z-axis Tracking serves to ensure beam, collimator and detector alignment during tube rotation. SmartmA provides angular mA modulation. ECG Helical Modulation reduces mA during the systolic phase of the heart cycle. SmartBeam optimization uses bowtie beam-shaping hardware and software to filter off-axis x-rays - minimizing dose and reducing x-ray scatter. The DICOM Radiation Dose Structured Report (RDSR) generates a dose report at the conclusion of every examination. Dose Check preemptively notifies CT operators when scan parameters exceed user-defined dose thresholds. DoseWatch is an information technology application providing vendor-agnostic dose tracking and analysis for CT (and all other diagnostic x-ray modalities) SnapShot Pulse improves coronary CTA dose management. VolumeShuttle uses two acquisitions to increase coverage, decrease dose, and conserve on contrast administration. Color-Coding for Kids applies the Broselow-Luten Pediatric System to facilitate pediatric emergency care and reduce medical errors. FeatherLight achieves dose optimization through pediatric procedure-based protocols. Adventure Series scanners provide a child-friendly imaging environment promoting patient cooperation with resultant reduction in retakes and patient motion. Philips CT Dose Optimization Tools and Advanced Reconstruction Presentation Time: 11:45 ‘ 12:15 PM The first part of the talk will cover “Dose Reduction and Dose Optimization Technologies” present in Philips CT Scanners. The main Technologies to be presented include: DoseRight and tube current modulation (DoseRight, Z-DOM, 3D-DOM, DoseRight Cardiac) Special acquisition modes Beam filtration and beam shapers Eclipse collimator and ClearRay collimator NanoPanel detector DoseRight will cover automatic tube current selection that automatically adjusts the dose for the individual patient. The presentation will explore the modulation techniques currently employed in Philips CT scanners and will include the algorithmic concepts as well as illustrative examples. Modulation and current selection technologies to be covered include the Automatic Current Selection component of DoseRight, ZDOM longitudinal dose modulation, 3D-DOM (combination of longitudinal and rotational dose modulation), Cardiac Dose right (an ECG based dose modulation scheme), and the DoseRight Index (DRI) IQ index. The special acquisition modes covers acquisition techniques such as prospective gating that is designed to reduce exposure to the patient through the Cardiac Step and Shoot scan mode. This mode can substitute the much higher dose retrospective scan modes for certain types of cardiac imaging. The beam filtration and beam shaper portion will discuss the variety of filtration and beam shaping configurations available on Philips scanners. This topic includes the x-ray beam characteristics, tube filtration as well as dose compensator characteristics. The Eclipse collimator, ClearRay collimator and the NanoPanel detector portion will discuss additional technologies specific to wide coverage CT that address some of the unique challenges encountered and techniques employed to optimize image quality and optimize dose utilization. The Eclipse collimator reduces extraneous exposure by actively blocking the radiation tails at either end of helical scans that do not contribute to the image generation. The ClearRay collimator and the NanoPanel detector optimize the quality of the signal that reaches the detectors by addressing the increased scattered radiation present in wide coverage and the NanoPanel detector adds superior electronic noise characteristics valuable when imaging at a low dose level. The second part of the talk will present “Advanced Reconstruction Technologies” currently available on Philips CT Scanners. The talk will cover filtered back projection (FBP), iDose4 and Iterative Model Reconstruction (IMR). Each reconstruction method will include a discussion of the algorithm as well as similarities and differences between the algorithms. Examples illustrating the merits of each algorithm will be presented, and techniques and metrics to characterize the performance of each type of algorithm will be presented. The Filtered Back projection portion will discuss and provide a brief summary of relevant standard image reconstruction techniques in common use, and discuss the common tradeoffs when using the FBP algorithm. The iDose4 portion will present the algorithms used for iDose4 as well the different levels. The meaning of different levels of iDose4 available will be presented and quantified. Guidelines for selection iDose4 parameters based on the imaging need will be explained. The different image quality goals available with iDose4 and specifically how iDose4 enables noise reduction, spatial resolution improvement or both will be explained. The approaches to leveraging the benefits of iDose4 such as improved spatial resolution, decreased noise, and artifact prevention will be described and quantified; and measurements and metrics behind the improvements will be presented. The image quality benefits in specific imaging situations as well as how to best combine the technology with other dose reduction strategies to ensure the best image quality at a given dose level will be presented. Insight into the IMR algorithm as well as contrast to the iDose4 techniques and performance characteristics will be discussed. Metrics and techniques for characterizing this class of algorithm and IQ performance will be presented. The image quality benefits and the dose reduction capabilities of IMR will be explored. Illustrative examples of the noise reduction, spatial resolution improvement, and low contrast detectability improvements of the reconstruction method will be presented: clinical cases and phantom measurements demonstrating the benefits of IMR in the areas of low dose imaging, spatial resolution and low contrast resolution are discussed and the technical details behind the measurements will be presented compared to both iDose4 and traditional filtered back projection (FBP)« less

Active control of impulsive noise with symmetric α-stable distribution based on an improved step-size normalized adaptive algorithm

NASA Astrophysics Data System (ADS)

Zhou, Yali; Zhang, Qizhi; Yin, Yixin

2015-05-01

In this paper, active control of impulsive noise with symmetric α-stable (SαS) distribution is studied. A general step-size normalized filtered-x Least Mean Square (FxLMS) algorithm is developed based on the analysis of existing algorithms, and the Gaussian distribution function is used to normalize the step size. Compared with existing algorithms, the proposed algorithm needs neither the parameter selection and thresholds estimation nor the process of cost function selection and complex gradient computation. Computer simulations have been carried out to suggest that the proposed algorithm is effective for attenuating SαS impulsive noise, and then the proposed algorithm has been implemented in an experimental ANC system. Experimental results show that the proposed scheme has good performance for SαS impulsive noise attenuation.

Design and Testing of a Low Noise Flight Guidance Concept

NASA Technical Reports Server (NTRS)

Williams, David H.; Oseguera-Lohr, Rosa M.; Lewis, Elliot T.

2004-01-01

A flight guidance concept was developed to assist in flying continuous descent approach (CDA) procedures designed to lower the noise under the flight path of jet transport aircraft during arrival operations at an airport. The guidance consists of a trajectory prediction algorithm that was tuned to produce a high-efficiency, low noise flight profile with accompanying autopilot and flight display elements needed by the flight control system and pilot to fly the approach. A key component of the flight guidance was a real-time display of energy error relative to the predicted flight path. The guidance was integrated with the conventional Flight Management System (FMS) guidance of a modern jet transport airplane and tested in a high fidelity flight simulation. A charted arrival procedure, which allowed flying conventional arrivals, CDA arrivals with standard guidance, and CDA arrivals with the new low noise guidance, was developed to assist in the testing and evaluation of the low noise guidance concept. Results of the simulation testing showed the low noise guidance was easy to use by airline pilot test subjects and effective in achieving the desired noise reduction. Noise under the flight path was reduced by at least 2 decibels in Sound Exposure Level (SEL) at distances from about 3 nautical miles out to about 17.5 nautical miles from the runway, with a peak reduction of 8.5 decibels at about 10.5 nautical miles. Fuel consumption was also reduced by about 17% for the LNG conditions compared to baseline runs for the same flight distance. Pilot acceptance and understanding of the guidance was quite high with favorable comments and ratings received from all test subjects.

Wavelet-based de-noising algorithm for images acquired with parallel magnetic resonance imaging (MRI).

PubMed

Delakis, Ioannis; Hammad, Omer; Kitney, Richard I

2007-07-07

Wavelet-based de-noising has been shown to improve image signal-to-noise ratio in magnetic resonance imaging (MRI) while maintaining spatial resolution. Wavelet-based de-noising techniques typically implemented in MRI require that noise displays uniform spatial distribution. However, images acquired with parallel MRI have spatially varying noise levels. In this work, a new algorithm for filtering images with parallel MRI is presented. The proposed algorithm extracts the edges from the original image and then generates a noise map from the wavelet coefficients at finer scales. The noise map is zeroed at locations where edges have been detected and directional analysis is also used to calculate noise in regions of low-contrast edges that may not have been detected. The new methodology was applied on phantom and brain images and compared with other applicable de-noising techniques. The performance of the proposed algorithm was shown to be comparable with other techniques in central areas of the images, where noise levels are high. In addition, finer details and edges were maintained in peripheral areas, where noise levels are low. The proposed methodology is fully automated and can be applied on final reconstructed images without requiring sensitivity profiles or noise matrices of the receiver coils, therefore making it suitable for implementation in a clinical MRI setting.

Restoration for Noise Removal in Quantum Images

NASA Astrophysics Data System (ADS)

Liu, Kai; Zhang, Yi; Lu, Kai; Wang, Xiaoping

2017-09-01

Quantum computation has become increasingly attractive in the past few decades due to its extraordinary performance. As a result, some studies focusing on image representation and processing via quantum mechanics have been done. However, few of them have considered the quantum operations for images restoration. To address this problem, three noise removal algorithms are proposed in this paper based on the novel enhanced quantum representation model, oriented to two kinds of noise pollution (Salt-and-Pepper noise and Gaussian noise). For the first algorithm Q-Mean, it is designed to remove the Salt-and-Pepper noise. The noise points are extracted through comparisons with the adjacent pixel values, after which the restoration operation is finished by mean filtering. As for the second method Q-Gauss, a special mask is applied to weaken the Gaussian noise pollution. The third algorithm Q-Adapt is effective for the source image containing unknown noise. The type of noise can be judged through the quantum statistic operations for the color value of the whole image, and then different noise removal algorithms are used to conduct image restoration respectively. Performance analysis reveals that our methods can offer high restoration quality and achieve significant speedup through inherent parallelism of quantum computation.

Signal processing using sparse derivatives with applications to chromatograms and ECG

NASA Astrophysics Data System (ADS)

Ning, Xiaoran

In this thesis, we investigate the sparsity exist in the derivative domain. Particularly, we focus on the type of signals which posses up to Mth (M > 0) order sparse derivatives. Efforts are put on formulating proper penalty functions and optimization problems to capture properties related to sparse derivatives, searching for fast, computationally efficient solvers. Also the effectiveness of these algorithms are applied to two real world applications. In the first application, we provide an algorithm which jointly addresses the problems of chromatogram baseline correction and noise reduction. The series of chromatogram peaks are modeled as sparse with sparse derivatives, and the baseline is modeled as a low-pass signal. A convex optimization problem is formulated so as to encapsulate these non-parametric models. To account for the positivity of chromatogram peaks, an asymmetric penalty function is also utilized with symmetric penalty functions. A robust, computationally efficient, iterative algorithm is developed that is guaranteed to converge to the unique optimal solution. The approach, termed Baseline Estimation And Denoising with Sparsity (BEADS), is evaluated and compared with two state-of-the-art methods using both simulated and real chromatogram data. Promising result is obtained. In the second application, a novel Electrocardiography (ECG) enhancement algorithm is designed also based on sparse derivatives. In the real medical environment, ECG signals are often contaminated by various kinds of noise or artifacts, for example, morphological changes due to motion artifact, non-stationary noise due to muscular contraction (EMG), etc. Some of these contaminations severely affect the usefulness of ECG signals, especially when computer aided algorithms are utilized. By solving the proposed convex l1 optimization problem, artifacts are reduced by modeling the clean ECG signal as a sum of two signals whose second and third-order derivatives (differences) are sparse respectively. At the end, the algorithm is applied to a QRS detection system and validated using the MIT-BIH Arrhythmia database (109452 anotations), resulting a sensitivity of Se = 99.87%$ and a positive prediction of +P = 99.88%.

Integrated identification, modeling and control with applications

NASA Astrophysics Data System (ADS)

Shi, Guojun

This thesis deals with the integration of system design, identification, modeling and control. In particular, six interdisciplinary engineering problems are addressed and investigated. Theoretical results are established and applied to structural vibration reduction and engine control problems. First, the data-based LQG control problem is formulated and solved. It is shown that a state space model is not necessary to solve this problem; rather a finite sequence from the impulse response is the only model data required to synthesize an optimal controller. The new theory avoids unnecessary reliance on a model, required in the conventional design procedure. The infinite horizon model predictive control problem is addressed for multivariable systems. The basic properties of the receding horizon implementation strategy is investigated and the complete framework for solving the problem is established. The new theory allows the accommodation of hard input constraints and time delays. The developed control algorithms guarantee the closed loop stability. A closed loop identification and infinite horizon model predictive control design procedure is established for engine speed regulation. The developed algorithms are tested on the Cummins Engine Simulator and desired results are obtained. A finite signal-to-noise ratio model is considered for noise signals. An information quality index is introduced which measures the essential information precision required for stabilization. The problems of minimum variance control and covariance control are formulated and investigated. Convergent algorithms are developed for solving the problems of interest. The problem of the integrated passive and active control design is addressed in order to improve the overall system performance. A design algorithm is developed, which simultaneously finds: (i) the optimal values of the stiffness and damping ratios for the structure, and (ii) an optimal output variance constrained stabilizing controller such that the active control energy is minimized. A weighted q-Markov COVER method is introduced for identification with measurement noise. The result is use to develop an iterative closed loop identification/control design algorithm. The effectiveness of the algorithm is illustrated by experimental results.

Impulsive noise removal from color video with morphological filtering

NASA Astrophysics Data System (ADS)

Ruchay, Alexey; Kober, Vitaly

2017-09-01

This paper deals with impulse noise removal from color video. The proposed noise removal algorithm employs a switching filtering for denoising of color video; that is, detection of corrupted pixels by means of a novel morphological filtering followed by removal of the detected pixels on the base of estimation of uncorrupted pixels in the previous scenes. With the help of computer simulation we show that the proposed algorithm is able to well remove impulse noise in color video. The performance of the proposed algorithm is compared in terms of image restoration metrics with that of common successful algorithms.

CONEDEP: COnvolutional Neural network based Earthquake DEtection and Phase Picking

NASA Astrophysics Data System (ADS)

Zhou, Y.; Huang, Y.; Yue, H.; Zhou, S.; An, S.; Yun, N.

2017-12-01

We developed an automatic local earthquake detection and phase picking algorithm based on Fully Convolutional Neural network (FCN). The FCN algorithm detects and segments certain features (phases) in 3 component seismograms to realize efficient picking. We use STA/LTA algorithm and template matching algorithm to construct the training set from seismograms recorded 1 month before and after the Wenchuan earthquake. Precise P and S phases are identified and labeled to construct the training set. Noise data are produced by combining back-ground noise and artificial synthetic noise to form the equivalent scale of noise set as the signal set. Training is performed on GPUs to achieve efficient convergence. Our algorithm has significantly improved performance in terms of the detection rate and precision in comparison with STA/LTA and template matching algorithms.

A fully-automated multiscale kernel graph cuts based particle localization scheme for temporal focusing two-photon microscopy

NASA Astrophysics Data System (ADS)

Huang, Xia; Li, Chunqiang; Xiao, Chuan; Sun, Wenqing; Qian, Wei

2017-03-01

The temporal focusing two-photon microscope (TFM) is developed to perform depth resolved wide field fluorescence imaging by capturing frames sequentially. However, due to strong nonignorable noises and diffraction rings surrounding particles, further researches are extremely formidable without a precise particle localization technique. In this paper, we developed a fully-automated scheme to locate particles positions with high noise tolerance. Our scheme includes the following procedures: noise reduction using a hybrid Kalman filter method, particle segmentation based on a multiscale kernel graph cuts global and local segmentation algorithm, and a kinematic estimation based particle tracking method. Both isolated and partial-overlapped particles can be accurately identified with removal of unrelated pixels. Based on our quantitative analysis, 96.22% isolated particles and 84.19% partial-overlapped particles were successfully detected.

Noise reduction experience at Hughes Helicopter, Inc.

NASA Astrophysics Data System (ADS)

Janakiram, D. S.

1982-07-01

Noise reduction is mostly limited to light helicopters whose noise signature is dominated by their tail rotors. It is primarily hardware oriented. Well known noise reduction techniques such as reduction of rotor speeds with an accompanying increase in solidity to maintain performance, engine noise reduction with the use of exhaust mufflers, and acoustic blanketing of transmission and engine compartment are used. The concept of blade phasing as a means of reducing tail rotor noise is also used. Engine noise (exhaust noise), power train noise and airframe noise becomes important at low rotor tip speeds and means must be found to reduce these noise sources if further noise reductions are desired. The use of a special test rig aids in isolating the various noise sources and arriving at the penalties (performance or payload) involved in quieting them. Significant noise reduction are achieved for the light helicopter with minimum performance or weight penalties because of the dominance of a single noise source (the tail rotor).

Noise reduction experience at Hughes Helicopter, Inc.

NASA Technical Reports Server (NTRS)

Janakiram, D. S.

1982-01-01

Noise reduction is mostly limited to light helicopters whose noise signature is dominated by their tail rotors. It is primarily hardware oriented. Well known noise reduction techniques such as reduction of rotor speeds with an accompanying increase in solidity to maintain performance, engine noise reduction with the use of exhaust mufflers, and acoustic blanketing of transmission and engine compartment are used. The concept of blade phasing as a means of reducing tail rotor noise is also used. Engine noise (exhaust noise), power train noise and airframe noise becomes important at low rotor tip speeds and means must be found to reduce these noise sources if further noise reductions are desired. The use of a special test rig aids in isolating the various noise sources and arriving at the penalties (performance or payload) involved in quieting them. Significant noise reduction are achieved for the light helicopter with minimum performance or weight penalties because of the dominance of a single noise source (the tail rotor).

An algorithm to improve speech recognition in noise for hearing-impaired listeners

PubMed Central

Healy, Eric W.; Yoho, Sarah E.; Wang, Yuxuan; Wang, DeLiang

2013-01-01

Despite considerable effort, monaural (single-microphone) algorithms capable of increasing the intelligibility of speech in noise have remained elusive. Successful development of such an algorithm is especially important for hearing-impaired (HI) listeners, given their particular difficulty in noisy backgrounds. In the current study, an algorithm based on binary masking was developed to separate speech from noise. Unlike the ideal binary mask, which requires prior knowledge of the premixed signals, the masks used to segregate speech from noise in the current study were estimated by training the algorithm on speech not used during testing. Sentences were mixed with speech-shaped noise and with babble at various signal-to-noise ratios (SNRs). Testing using normal-hearing and HI listeners indicated that intelligibility increased following processing in all conditions. These increases were larger for HI listeners, for the modulated background, and for the least-favorable SNRs. They were also often substantial, allowing several HI listeners to improve intelligibility from scores near zero to values above 70%. PMID:24116438

A Fourier dimensionality reduction model for big data interferometric imaging

NASA Astrophysics Data System (ADS)

Vijay Kartik, S.; Carrillo, Rafael E.; Thiran, Jean-Philippe; Wiaux, Yves

2017-06-01

Data dimensionality reduction in radio interferometry can provide savings of computational resources for image reconstruction through reduced memory footprints and lighter computations per iteration, which is important for the scalability of imaging methods to the big data setting of the next-generation telescopes. This article sheds new light on dimensionality reduction from the perspective of the compressed sensing theory and studies its interplay with imaging algorithms designed in the context of convex optimization. We propose a post-gridding linear data embedding to the space spanned by the left singular vectors of the measurement operator, providing a dimensionality reduction below image size. This embedding preserves the null space of the measurement operator and hence its sampling properties are also preserved in light of the compressed sensing theory. We show that this can be approximated by first computing the dirty image and then applying a weighted subsampled discrete Fourier transform to obtain the final reduced data vector. This Fourier dimensionality reduction model ensures a fast implementation of the full measurement operator, essential for any iterative image reconstruction method. The proposed reduction also preserves the independent and identically distributed Gaussian properties of the original measurement noise. For convex optimization-based imaging algorithms, this is key to justify the use of the standard ℓ2-norm as the data fidelity term. Our simulations confirm that this dimensionality reduction approach can be leveraged by convex optimization algorithms with no loss in imaging quality relative to reconstructing the image from the complete visibility data set. Reconstruction results in simulation settings with no direction dependent effects or calibration errors show promising performance of the proposed dimensionality reduction. Further tests on real data are planned as an extension of the current work. matlab code implementing the proposed reduction method is available on GitHub.

Study on Underwater Image Denoising Algorithm Based on Wavelet Transform

NASA Astrophysics Data System (ADS)

Jian, Sun; Wen, Wang

2017-02-01

This paper analyzes the application of MATLAB in underwater image processing, the transmission characteristics of the underwater laser light signal and the kinds of underwater noise has been described, the common noise suppression algorithm: Wiener filter, median filter, average filter algorithm is brought out. Then the advantages and disadvantages of each algorithm in image sharpness and edge protection areas have been compared. A hybrid filter algorithm based on wavelet transform has been proposed which can be used for Color Image Denoising. At last the PSNR and NMSE of each algorithm has been given out, which compares the ability to de-noising

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Applications of data compression techniques in modal analysis for on-orbit system identification

NASA Technical Reports Server (NTRS)

Carlin, Robert A.; Saggio, Frank; Garcia, Ephrahim

1992-01-01

Data compression techniques have been investigated for use with modal analysis applications. A redundancy-reduction algorithm was used to compress frequency response functions (FRFs) in order to reduce the amount of disk space necessary to store the data and/or save time in processing it. Tests were performed for both single- and multiple-degree-of-freedom (SDOF and MDOF, respectively) systems, with varying amounts of noise. Analysis was done on both the compressed and uncompressed FRFs using an SDOF Nyquist curve fit as well as the Eigensystem Realization Algorithm. Significant savings were realized with minimal errors incurred by the compression process.

AgRISTARS. Supporting research: Algorithms for scene modelling

NASA Technical Reports Server (NTRS)

Rassbach, M. E. (Principal Investigator)

1982-01-01

The requirements for a comprehensive analysis of LANDSAT or other visual data scenes are defined. The development of a general model of a scene and a computer algorithm for finding the particular model for a given scene is discussed. The modelling system includes a boundary analysis subsystem, which detects all the boundaries and lines in the image and builds a boundary graph; a continuous variation analysis subsystem, which finds gradual variations not well approximated by a boundary structure; and a miscellaneous features analysis, which includes texture, line parallelism, etc. The noise reduction capabilities of this method and its use in image rectification and registration are discussed.

Technical Note: Modification of the standard gain correction algorithm to compensate for the number of used reference flat frames in detector performance studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Konstantinidis, Anastasios C.; Olivo, Alessandro; Speller, Robert D.

2011-12-15

Purpose: The x-ray performance evaluation of digital x-ray detectors is based on the calculation of the modulation transfer function (MTF), the noise power spectrum (NPS), and the resultant detective quantum efficiency (DQE). The flat images used for the extraction of the NPS should not contain any fixed pattern noise (FPN) to avoid contamination from nonstochastic processes. The ''gold standard'' method used for the reduction of the FPN (i.e., the different gain between pixels) in linear x-ray detectors is based on normalization with an average reference flat-field. However, the noise in the corrected image depends on the number of flat framesmore » used for the average flat image. The aim of this study is to modify the standard gain correction algorithm to make it independent on the used reference flat frames. Methods: Many publications suggest the use of 10-16 reference flat frames, while other studies use higher numbers (e.g., 48 frames) to reduce the propagated noise from the average flat image. This study quantifies experimentally the effect of the number of used reference flat frames on the NPS and DQE values and appropriately modifies the gain correction algorithm to compensate for this effect. Results: It is shown that using the suggested gain correction algorithm a minimum number of reference flat frames (i.e., down to one frame) can be used to eliminate the FPN from the raw flat image. This saves computer memory and time during the x-ray performance evaluation. Conclusions: The authors show that the method presented in the study (a) leads to the maximum DQE value that one would have by using the conventional method and very large number of frames and (b) has been compared to an independent gain correction method based on the subtraction of flat-field images, leading to identical DQE values. They believe this provides robust validation of the proposed method.« less

Inverse determination of the penalty parameter in penalized weighted least-squares algorithm for noise reduction of low-dose CBCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jing; Guan, Huaiqun; Solberg, Timothy

2011-07-15

Purpose: A statistical projection restoration algorithm based on the penalized weighted least-squares (PWLS) criterion can substantially improve the image quality of low-dose CBCT images. The performance of PWLS is largely dependent on the choice of the penalty parameter. Previously, the penalty parameter was chosen empirically by trial and error. In this work, the authors developed an inverse technique to calculate the penalty parameter in PWLS for noise suppression of low-dose CBCT in image guided radiotherapy (IGRT). Methods: In IGRT, a daily CBCT is acquired for the same patient during a treatment course. In this work, the authors acquired the CBCTmore » with a high-mAs protocol for the first session and then a lower mAs protocol for the subsequent sessions. The high-mAs projections served as the goal (ideal) toward, which the low-mAs projections were to be smoothed by minimizing the PWLS objective function. The penalty parameter was determined through an inverse calculation of the derivative of the objective function incorporating both the high and low-mAs projections. Then the parameter obtained can be used for PWLS to smooth the noise in low-dose projections. CBCT projections for a CatPhan 600 and an anthropomorphic head phantom, as well as for a brain patient, were used to evaluate the performance of the proposed technique. Results: The penalty parameter in PWLS was obtained for each CBCT projection using the proposed strategy. The noise in the low-dose CBCT images reconstructed from the smoothed projections was greatly suppressed. Image quality in PWLS-processed low-dose CBCT was comparable to its corresponding high-dose CBCT. Conclusions: A technique was proposed to estimate the penalty parameter for PWLS algorithm. It provides an objective and efficient way to obtain the penalty parameter for image restoration algorithms that require predefined smoothing parameters.« less

High Speed Research Noise Prediction Code (HSRNOISE) User's and Theoretical Manual

NASA Technical Reports Server (NTRS)

Golub, Robert (Technical Monitor); Rawls, John W., Jr.; Yeager, Jessie C.

2004-01-01

This report describes a computer program, HSRNOISE, that predicts noise levels for a supersonic aircraft powered by mixed flow turbofan engines with rectangular mixer-ejector nozzles. It fully documents the noise prediction algorithms, provides instructions for executing the HSRNOISE code, and provides predicted noise levels for the High Speed Research (HSR) program Technology Concept (TC) aircraft. The component source noise prediction algorithms were developed jointly by Boeing, General Electric Aircraft Engines (GEAE), NASA and Pratt & Whitney during the course of the NASA HSR program. Modern Technologies Corporation developed an alternative mixer ejector jet noise prediction method under contract to GEAE that has also been incorporated into the HSRNOISE prediction code. Algorithms for determining propagation effects and calculating noise metrics were taken from the NASA Aircraft Noise Prediction Program.

Reduction of metal artifacts from hip prostheses on CT images of the pelvis: value of iterative reconstructions.

PubMed

Morsbach, Fabian; Bickelhaupt, Sebastian; Wanner, Guido A; Krauss, Andreas; Schmidt, Bernhard; Alkadhi, Hatem

2013-07-01

To assess the value of iterative frequency split-normalized (IFS) metal artifact reduction (MAR) for computed tomography (CT) of hip prostheses. This study had institutional review board and local ethics committee approval. First, a hip phantom with steel and titanium prostheses that had inlays of water, fat, and contrast media in the pelvis was used to optimize the IFS algorithm. Second, 41 consecutive patients with hip prostheses who were undergoing CT were included. Data sets were reconstructed with filtered back projection, the IFS algorithm, and a linear interpolation MAR algorithm. Two blinded, independent readers evaluated axial, coronal, and sagittal CT reformations for overall image quality, image quality of pelvic organs, and assessment of pelvic abnormalities. CT attenuation and image noise were measured. Statistical analysis included the Friedman test, Wilcoxon signed-rank test, and Levene test. Ex vivo experiments demonstrated an optimized IFS algorithm by using a threshold of 2200 HU with four iterations for both steel and titanium prostheses. Measurements of CT attenuation of the inlays were significantly (P < .001) more accurate for IFS when compared with filtered back projection. In patients, best overall and pelvic organ image quality was found in all reformations with IFS (P < .001). Pelvic abnormalities in 11 of 41 patients (27%) were diagnosed with significantly (P = .002) higher confidence on the basis of IFS images. CT attenuation of bladder (P < .001) and muscle (P = .043) was significantly less variable with IFS compared with filtered back projection and linear interpolation MAR. In comparison with that of FBP and linear interpolation MAR, noise with IFS was similar close to and far from the prosthesis (P = .295). The IFS algorithm for CT image reconstruction significantly reduces metal artifacts from hip prostheses, improves the reliability of CT number measurements, and improves the confidence for depicting pelvic abnormalities.

A novel cost-effective parallel narrowband ANC system with local secondary-path estimation

NASA Astrophysics Data System (ADS)

Delegà, Riccardo; Bernasconi, Giancarlo; Piroddi, Luigi

2017-08-01

Many noise reduction applications are targeted at multi-tonal disturbances. Active noise control (ANC) solutions for such problems are generally based on the combination of multiple adaptive notch filters. Both the performance and the computational cost are negatively affected by an increase in the number of controlled frequencies. In this work we study a different modeling approach for the secondary path, based on the estimation of various small local models in adjacent frequency subbands, that greatly reduces the impact of reference-filtering operations in the ANC algorithm. Furthermore, in combination with a frequency-specific step size tuning method it provides a balanced attenuation performance over the whole controlled frequency range (and particularly in the high end of the range). Finally, the use of small local models is greatly beneficial for the reactivity of the online secondary path modeling algorithm when the characteristics of the acoustic channels are time-varying. Several simulations are provided to illustrate the positive features of the proposed method compared to other well-known techniques.

Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy

NASA Astrophysics Data System (ADS)

Tang, Jing; Rahmim, Arman

2015-01-01

A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE-MAP algorithm resulted in comparable regional mean values to those from the maximum likelihood algorithm while reducing noise. Achieving robust performance in various noise-level simulation and patient studies, the WJE-MAP algorithm demonstrates its potential in clinical quantitative PET imaging.

Reconstructing signals from noisy data with unknown signal and noise covariance.

PubMed

Oppermann, Niels; Robbers, Georg; Ensslin, Torsten A

2011-10-01

We derive a method to reconstruct Gaussian signals from linear measurements with Gaussian noise. This new algorithm is intended for applications in astrophysics and other sciences. The starting point of our considerations is the principle of minimum Gibbs free energy, which was previously used to derive a signal reconstruction algorithm handling uncertainties in the signal covariance. We extend this algorithm to simultaneously uncertain noise and signal covariances using the same principles in the derivation. The resulting equations are general enough to be applied in many different contexts. We demonstrate the performance of the algorithm by applying it to specific example situations and compare it to algorithms not allowing for uncertainties in the noise covariance. The results show that the method we suggest performs very well under a variety of circumstances and is indeed qualitatively superior to the other methods in cases where uncertainty in the noise covariance is present.

Comparing multiple turbulence restoration algorithms performance on noisy anisoplanatic imagery

NASA Astrophysics Data System (ADS)

Rucci, Michael A.; Hardie, Russell C.; Dapore, Alexander J.

2017-05-01

In this paper, we compare the performance of multiple turbulence mitigation algorithms to restore imagery degraded by atmospheric turbulence and camera noise. In order to quantify and compare algorithm performance, imaging scenes were simulated by applying noise and varying levels of turbulence. For the simulation, a Monte-Carlo wave optics approach is used to simulate the spatially and temporally varying turbulence in an image sequence. A Poisson-Gaussian noise mixture model is then used to add noise to the observed turbulence image set. These degraded image sets are processed with three separate restoration algorithms: Lucky Look imaging, bispectral speckle imaging, and a block matching method with restoration filter. These algorithms were chosen because they incorporate different approaches and processing techniques. The results quantitatively show how well the algorithms are able to restore the simulated degraded imagery.

Accuracy metrics for judging time scale algorithms

NASA Technical Reports Server (NTRS)

Douglas, R. J.; Boulanger, J.-S.; Jacques, C.

1994-01-01

Time scales have been constructed in different ways to meet the many demands placed upon them for time accuracy, frequency accuracy, long-term stability, and robustness. Usually, no single time scale is optimum for all purposes. In the context of the impending availability of high-accuracy intermittently-operated cesium fountains, we reconsider the question of evaluating the accuracy of time scales which use an algorithm to span interruptions of the primary standard. We consider a broad class of calibration algorithms that can be evaluated and compared quantitatively for their accuracy in the presence of frequency drift and a full noise model (a mixture of white PM, flicker PM, white FM, flicker FM, and random walk FM noise). We present the analytic techniques for computing the standard uncertainty for the full noise model and this class of calibration algorithms. The simplest algorithm is evaluated to find the average-frequency uncertainty arising from the noise of the cesium fountain's local oscillator and from the noise of a hydrogen maser transfer-standard. This algorithm and known noise sources are shown to permit interlaboratory frequency transfer with a standard uncertainty of less than 10(exp -15) for periods of 30-100 days.

Total variation optimization for imaging through turbid media with transmission matrix

NASA Astrophysics Data System (ADS)

Gong, Changmei; Shao, Xiaopeng; Wu, Tengfei; Liu, Jietao; Zhang, Jianqi

2016-12-01

With the transmission matrix (TM) of the whole optical system measured, the image of the object behind a turbid medium can be recovered from its speckle field by means of an image reconstruction algorithm. Instead of Tikhonov regularization algorithm (TRA), the total variation minimization by augmented Lagrangian and alternating direction algorithms (TVAL3) is introduced to recover object images. As a total variation (TV)-based approach, TVAL3 allows to effectively damp more noise and preserve more edges compared with TRA, thus providing more outstanding image quality. Different levels of detector noise and TM-measurement noise are successively added to analyze the antinoise performance of these two algorithms. Simulation results show that TVAL3 is able to recover more details and suppress more noise than TRA under different noise levels, thus providing much more excellent image quality. Furthermore, whether it be detector noise or TM-measurement noise, the reconstruction images obtained by TVAL3 at SNR=15 dB are far superior to those by TRA at SNR=50 dB.

Noise suppression methods for robust speech processing

NASA Astrophysics Data System (ADS)

Boll, S. F.; Ravindra, H.; Randall, G.; Armantrout, R.; Power, R.

1980-05-01

Robust speech processing in practical operating environments requires effective environmental and processor noise suppression. This report describes the technical findings and accomplishments during this reporting period for the research program funded to develop real time, compressed speech analysis synthesis algorithms whose performance in invariant under signal contamination. Fulfillment of this requirement is necessary to insure reliable secure compressed speech transmission within realistic military command and control environments. Overall contributions resulting from this research program include the understanding of how environmental noise degrades narrow band, coded speech, development of appropriate real time noise suppression algorithms, and development of speech parameter identification methods that consider signal contamination as a fundamental element in the estimation process. This report describes the current research and results in the areas of noise suppression using the dual input adaptive noise cancellation using the short time Fourier transform algorithms, articulation rate change techniques, and a description of an experiment which demonstrated that the spectral subtraction noise suppression algorithm can improve the intelligibility of 2400 bps, LPC 10 coded, helicopter speech by 10.6 point.

Ultra-low noise miniaturized neural amplifier with hardware averaging.

PubMed

Dweiri, Yazan M; Eggers, Thomas; McCallum, Grant; Durand, Dominique M

2015-08-01

Peripheral nerves carry neural signals that could be used to control hybrid bionic systems. Cuff electrodes provide a robust and stable interface but the recorded signal amplitude is small (<3 μVrms 700 Hz-7 kHz), thereby requiring a baseline noise of less than 1 μVrms for a useful signal-to-noise ratio (SNR). Flat interface nerve electrode (FINE) contacts alone generate thermal noise of at least 0.5 μVrms therefore the amplifier should add as little noise as possible. Since mainstream neural amplifiers have a baseline noise of 2 μVrms or higher, novel designs are required. Here we apply the concept of hardware averaging to nerve recordings obtained with cuff electrodes. An optimization procedure is developed to minimize noise and power simultaneously. The novel design was based on existing neural amplifiers (Intan Technologies, LLC) and is validated with signals obtained from the FINE in chronic dog experiments. We showed that hardware averaging leads to a reduction in the total recording noise by a factor of 1/√N or less depending on the source resistance. Chronic recording of physiological activity with FINE using the presented design showed significant improvement on the recorded baseline noise with at least two parallel operation transconductance amplifiers leading to a 46.1% reduction at N = 8. The functionality of these recordings was quantified by the SNR improvement and shown to be significant for N = 3 or more. The present design was shown to be capable of generating <1.5 μVrms total recording baseline noise when connected to a FINE placed on the sciatic nerve of an awake animal. An algorithm was introduced to find the value of N that can minimize both the power consumption and the noise in order to design a miniaturized ultralow-noise neural amplifier. These results demonstrate the efficacy of hardware averaging on noise improvement for neural recording with cuff electrodes, and can accommodate the presence of high source impedances that are associated with the miniaturized contacts and the high channel count in electrode arrays. This technique can be adopted for other applications where miniaturized and implantable multichannel acquisition systems with ultra-low noise and low power are required.

A 2D range Hausdorff approach to 3D facial recognition.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Koch, Mark William; Russ, Trina Denise; Little, Charles Quentin

2004-11-01

This paper presents a 3D facial recognition algorithm based on the Hausdorff distance metric. The standard 3D formulation of the Hausdorff matching algorithm has been modified to operate on a 2D range image, enabling a reduction in computation from O(N2) to O(N) without large storage requirements. The Hausdorff distance is known for its robustness to data outliers and inconsistent data between two data sets, making it a suitable choice for dealing with the inherent problems in many 3D datasets due to sensor noise and object self-occlusion. For optimal performance, the algorithm assumes a good initial alignment between probe and templatemore » datasets. However, to minimize the error between two faces, the alignment can be iteratively refined. Results from the algorithm are presented using 3D face images from the Face Recognition Grand Challenge database version 1.0.« less

Comparison of vessel enhancement algorithms applied to time-of-flight MRA images for cerebrovascular segmentation.

PubMed

Phellan, Renzo; Forkert, Nils D

2017-11-01

Vessel enhancement algorithms are often used as a preprocessing step for vessel segmentation in medical images to improve the overall segmentation accuracy. Each algorithm uses different characteristics to enhance vessels, such that the most suitable algorithm may vary for different applications. This paper presents a comparative analysis of the accuracy gains in vessel segmentation generated by the use of nine vessel enhancement algorithms: Multiscale vesselness using the formulas described by Erdt (MSE), Frangi (MSF), and Sato (MSS), optimally oriented flux (OOF), ranking orientations responses path operator (RORPO), the regularized Perona-Malik approach (RPM), vessel enhanced diffusion (VED), hybrid diffusion with continuous switch (HDCS), and the white top hat algorithm (WTH). The filters were evaluated and compared based on time-of-flight MRA datasets and corresponding manual segmentations from 5 healthy subjects and 10 patients with an arteriovenous malformation. Additionally, five synthetic angiographic datasets with corresponding ground truth segmentation were generated with three different noise levels (low, medium, and high) and also used for comparison. The parameters for each algorithm and subsequent segmentation were optimized using leave-one-out cross evaluation. The Dice coefficient, Matthews correlation coefficient, area under the ROC curve, number of connected components, and true positives were used for comparison. The results of this study suggest that vessel enhancement algorithms do not always lead to more accurate segmentation results compared to segmenting nonenhanced images directly. Multiscale vesselness algorithms, such as MSE, MSF, and MSS proved to be robust to noise, while diffusion-based filters, such as RPM, VED, and HDCS ranked in the top of the list in scenarios with medium or no noise. Filters that assume tubular-shapes, such as MSE, MSF, MSS, OOF, RORPO, and VED show a decrease in accuracy when considering patients with an AVM, because vessels may vary from its tubular-shape in this case. Vessel enhancement algorithms can help to improve the accuracy of the segmentation of the vascular system. However, their contribution to accuracy has to be evaluated as it depends on the specific applications, and in some cases it can lead to a reduction of the overall accuracy. No specific filter was suitable for all tested scenarios. © 2017 American Association of Physicists in Medicine.

Adaptive Statistical Iterative Reconstruction-V: Impact on Image Quality in Ultralow-Dose Coronary Computed Tomography Angiography.

PubMed

Benz, Dominik C; Gräni, Christoph; Mikulicic, Fran; Vontobel, Jan; Fuchs, Tobias A; Possner, Mathias; Clerc, Olivier F; Stehli, Julia; Gaemperli, Oliver; Pazhenkottil, Aju P; Buechel, Ronny R; Kaufmann, Philipp A

The clinical utility of a latest generation iterative reconstruction algorithm (adaptive statistical iterative reconstruction [ASiR-V]) has yet to be elucidated for coronary computed tomography angiography (CCTA). This study evaluates the impact of ASiR-V on signal, noise and image quality in CCTA. Sixty-five patients underwent clinically indicated CCTA on a 256-slice CT scanner using an ultralow-dose protocol. Data sets from each patient were reconstructed at 6 different levels of ASiR-V. Signal intensity was measured by placing a region of interest in the aortic root, LMA, and RCA. Similarly, noise was measured in the aortic root. Image quality was visually assessed by 2 readers. Median radiation dose was 0.49 mSv. Image noise decreased with increasing levels of ASiR-V resulting in a significant increase in signal-to-noise ratio in the RCA and LMA (P < 0.001). Correspondingly, image quality significantly increased with higher levels of ASiR-V (P < 0.001). ASiR-V yields substantial noise reduction and improved image quality enabling introduction of ultralow-dose CCTA.

Emerging Techniques for Dose Optimization in Abdominal CT

PubMed Central

Platt, Joel F.; Goodsitt, Mitchell M.; Al-Hawary, Mahmoud M.; Maturen, Katherine E.; Wasnik, Ashish P.; Pandya, Amit

2014-01-01

Recent advances in computed tomographic (CT) scanning technique such as automated tube current modulation (ATCM), optimized x-ray tube voltage, and better use of iterative image reconstruction have allowed maintenance of good CT image quality with reduced radiation dose. ATCM varies the tube current during scanning to account for differences in patient attenuation, ensuring a more homogeneous image quality, although selection of the appropriate image quality parameter is essential for achieving optimal dose reduction. Reducing the x-ray tube voltage is best suited for evaluating iodinated structures, since the effective energy of the x-ray beam will be closer to the k-edge of iodine, resulting in a higher attenuation for the iodine. The optimal kilovoltage for a CT study should be chosen on the basis of imaging task and patient habitus. The aim of iterative image reconstruction is to identify factors that contribute to noise on CT images with use of statistical models of noise (statistical iterative reconstruction) and selective removal of noise to improve image quality. The degree of noise suppression achieved with statistical iterative reconstruction can be customized to minimize the effect of altered image quality on CT images. Unlike with statistical iterative reconstruction, model-based iterative reconstruction algorithms model both the statistical noise and the physical acquisition process, allowing CT to be performed with further reduction in radiation dose without an increase in image noise or loss of spatial resolution. Understanding these recently developed scanning techniques is essential for optimization of imaging protocols designed to achieve the desired image quality with a reduced dose. © RSNA, 2014 PMID:24428277

Definition of 1992 Technology Aircraft Noise Levels and the Methodology for Assessing Airplane Noise Impact of Component Noise Reduction Concepts

NASA Technical Reports Server (NTRS)

Kumasaka, Henry A.; Martinez, Michael M.; Weir, Donald S.

1996-01-01

This report describes the methodology for assessing the impact of component noise reduction on total airplane system noise. The methodology is intended to be applied to the results of individual study elements of the NASA-Advanced Subsonic Technology (AST) Noise Reduction Program, which will address the development of noise reduction concepts for specific components. Program progress will be assessed in terms of noise reduction achieved, relative to baseline levels representative of 1992 technology airplane/engine design and performance. In this report, the 1992 technology reference levels are defined for assessment models based on four airplane sizes - an average business jet and three commercial transports: a small twin, a medium sized twin, and a large quad. Study results indicate that component changes defined as program final goals for nacelle treatment and engine/airframe source noise reduction would achieve from 6-7 EPNdB reduction of total airplane noise at FAR 36 Stage 3 noise certification conditions for all of the airplane noise assessment models.

Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Solomon, Justin, E-mail: justin.solomon@duke.edu; Samei, Ehsan

2014-09-15

Purpose: Quantum noise properties of CT images are generally assessed using simple geometric phantoms with uniform backgrounds. Such phantoms may be inadequate when assessing nonlinear reconstruction or postprocessing algorithms. The purpose of this study was to design anatomically informed textured phantoms and use the phantoms to assess quantum noise properties across two clinically available reconstruction algorithms, filtered back projection (FBP) and sinogram affirmed iterative reconstruction (SAFIRE). Methods: Two phantoms were designed to represent lung and soft-tissue textures. The lung phantom included intricate vessel-like structures along with embedded nodules (spherical, lobulated, and spiculated). The soft tissue phantom was designed based onmore » a three-dimensional clustered lumpy background with included low-contrast lesions (spherical and anthropomorphic). The phantoms were built using rapid prototyping (3D printing) technology and, along with a uniform phantom of similar size, were imaged on a Siemens SOMATOM Definition Flash CT scanner and reconstructed with FBP and SAFIRE. Fifty repeated acquisitions were acquired for each background type and noise was assessed by estimating pixel-value statistics, such as standard deviation (i.e., noise magnitude), autocorrelation, and noise power spectrum. Noise stationarity was also assessed by examining the spatial distribution of noise magnitude. The noise properties were compared across background types and between the two reconstruction algorithms. Results: In FBP and SAFIRE images, noise was globally nonstationary for all phantoms. In FBP images of all phantoms, and in SAFIRE images of the uniform phantom, noise appeared to be locally stationary (within a reasonably small region of interest). Noise was locally nonstationary in SAFIRE images of the textured phantoms with edge pixels showing higher noise magnitude compared to pixels in more homogenous regions. For pixels in uniform regions, noise magnitude was reduced by an average of 60% in SAFIRE images compared to FBP. However, for edge pixels, noise magnitude ranged from 20% higher to 40% lower in SAFIRE images compared to FBP. SAFIRE images of the lung phantom exhibited distinct regions with varying noise texture (i.e., noise autocorrelation/power spectra). Conclusions: Quantum noise properties observed in uniform phantoms may not be representative of those in actual patients for nonlinear reconstruction algorithms. Anatomical texture should be considered when evaluating the performance of CT systems that use such nonlinear algorithms.« less

Implementation and performance evaluation of acoustic denoising algorithms for UAV

NASA Astrophysics Data System (ADS)

Chowdhury, Ahmed Sony Kamal

Unmanned Aerial Vehicles (UAVs) have become popular alternative for wildlife monitoring and border surveillance applications. Elimination of the UAV's background noise and classifying the target audio signal effectively are still a major challenge. The main goal of this thesis is to remove UAV's background noise by means of acoustic denoising techniques. Existing denoising algorithms, such as Adaptive Least Mean Square (LMS), Wavelet Denoising, Time-Frequency Block Thresholding, and Wiener Filter, were implemented and their performance evaluated. The denoising algorithms were evaluated for average Signal to Noise Ratio (SNR), Segmental SNR (SSNR), Log Likelihood Ratio (LLR), and Log Spectral Distance (LSD) metrics. To evaluate the effectiveness of the denoising algorithms on classification of target audio, we implemented Support Vector Machine (SVM) and Naive Bayes classification algorithms. Simulation results demonstrate that LMS and Discrete Wavelet Transform (DWT) denoising algorithm offered superior performance than other algorithms. Finally, we implemented the LMS and DWT algorithms on a DSP board for hardware evaluation. Experimental results showed that LMS algorithm's performance is robust compared to DWT for various noise types to classify target audio signals.

Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery

NASA Astrophysics Data System (ADS)

Chen, Gang; Metz, Margaret R.; Rizzo, David M.; Dillon, Whalen W.; Meentemeyer, Ross K.

2015-04-01

Forest ecosystems are subject to a variety of disturbances with increasing intensities and frequencies, which may permanently change the trajectories of forest recovery and disrupt the ecosystem services provided by trees. Fire and invasive species, especially exotic disease-causing pathogens and insects, are examples of disturbances that together could pose major threats to forest health. This study examines the impacts of fire and exotic disease (sudden oak death) on forests, with an emphasis on the assessment of post-fire burn severity in a forest where trees have experienced three stages of disease progression pre-fire: early-stage (trees retaining dried foliage and fine twigs), middle-stage (trees losing fine crown fuels), and late-stage (trees falling down). The research was conducted by applying Geographic Object-Based Image Analysis (GEOBIA) to MASTER airborne images that were acquired immediately following the fire for rapid assessment and contained both high-spatial (4 m) and high-spectral (50 bands) resolutions. Although GEOBIA has gradually become a standard tool for analyzing high-spatial resolution imagery, high-spectral resolution data (dozens to hundreds of bands) can dramatically reduce computation efficiency in the process of segmentation and object-based variable extraction, leading to complicated variable selection for succeeding modeling. Hence, we also assessed two widely used band reduction algorithms, PCA (principal component analysis) and MNF (minimum noise fraction), for the delineation of image objects and the subsequent performance of burn severity models using either PCA or MNF derived variables. To increase computation efficiency, only the top 5 PCA and MNF and top 10 PCA and MNF components were evaluated, which accounted for 10% and 20% of the total number of the original 50 spectral bands, respectively. Results show that if no band reduction was applied the models developed for the three stages of disease progression had relatively similar performance, where both spectral responses and texture contributed to burn assessments. However, the application of PCA and MNF introduced much greater variation among models across the three stages. For the early-stage disease progression, neither band reduction algorithms improved or retained the accuracy of burn severity modeling (except for the use of 10 MNF components). Compared to the no-band-reduction scenario, band reduction led to a greater level of overestimation of low-degree burns and underestimation of medium-degree burns, suggesting that the spectral variation removed by PCA and MNF was vital for distinguishing between the spectral reflectance from disease-induced dried crowns (still retaining high structural complexity) and fire ash. For the middle-stage, both algorithms improved the model R2 values by 2-37%, while the late-stage models had comparable or better performance to those using the original 50 spectral bands. This could be explained by the loss of tree crowns enabling better signal penetration, thus leading to reduced spectral variation from canopies. Hence, spectral bands containing a high degree of random noise were correctly removed by the band reduction algorithms. Compared to the middle-stage, the late-stage forest stands were covered by large piles of fallen trees and branches, resulting in higher variability of MASTER imagery. The ability of band reduction to improve the model performance for these late-stage forest stands was reduced, because the valuable spectral variation representing the actual late-stage forest status was partially removed by both algorithms as noise. Our results indicate that PCA and MNF are promising for balancing computation efficiency and the performance of burn severity models in forest stands subject to the middle and late stages of sudden oak death disease progression. Compared to PCA, MNF dramatically reduced image spectral variation, generating larger image objects with less complexity of object shapes. Whereas, PCA-based models delivered superior performance in most evaluated cases suggesting that some key spectral variability contributing to the accuracy of burn severity models in diseased forests may have been removed together with true spectral noise through MNF transformations.

A curvature-based weighted fuzzy c-means algorithm for point clouds de-noising

NASA Astrophysics Data System (ADS)

Cui, Xin; Li, Shipeng; Yan, Xiutian; He, Xinhua

2018-04-01

In order to remove the noise of three-dimensional scattered point cloud and smooth the data without damnify the sharp geometric feature simultaneity, a novel algorithm is proposed in this paper. The feature-preserving weight is added to fuzzy c-means algorithm which invented a curvature weighted fuzzy c-means clustering algorithm. Firstly, the large-scale outliers are removed by the statistics of r radius neighboring points. Then, the algorithm estimates the curvature of the point cloud data by using conicoid parabolic fitting method and calculates the curvature feature value. Finally, the proposed clustering algorithm is adapted to calculate the weighted cluster centers. The cluster centers are regarded as the new points. The experimental results show that this approach is efficient to different scale and intensities of noise in point cloud with a high precision, and perform a feature-preserving nature at the same time. Also it is robust enough to different noise model.

Traffic Noise Ground Attenuation Algorithm Evaluation

NASA Astrophysics Data System (ADS)

Herman, Lloyd Allen

The Federal Highway Administration traffic noise prediction program, STAMINA 2.0, was evaluated for its accuracy. In addition, the ground attenuation algorithm used in the Ontario ORNAMENT method was evaluated to determine its potential to improve these predictions. Field measurements of sound levels were made at 41 sites on I-440 in Nashville, Tennessee in order to both study noise barrier effectiveness and to evaluate STAMINA 2.0 and the performance of the ORNAMENT ground attenuation algorithm. The measurement sites, which contain large variations in terrain, included several cross sections. Further, all sites contain some type of barrier, natural or constructed, which could more fully expose the strength and weaknesses of the ground attenuation algorithms. The noise barrier evaluation was accomplished in accordance with American National Standard Methods for Determination of Insertion Loss of Outdoor Noise Barriers which resulted in an evaluation of this standard. The entire 7.2 mile length of I-440 was modeled using STAMINA 2.0. A multiple run procedure was developed to emulate the results that would be obtained if the ORNAMENT algorithm was incorporated into STAMINA 2.0. Finally, the predicted noise levels based on STAMINA 2.0 and STAMINA with the ORNAMENT ground attenuation algorithm were compared with each other and with the field measurements. It was found that STAMINA 2.0 overpredicted noise levels by an average of over 2 dB for the receivers on I-440, whereas, the STAMINA with ORNAMENT ground attenuation algorithm overpredicted noise levels by an average of less than 0.5 dB. The mean errors for the two predictions were found to be statistically different from each other, and the mean error for the prediction with the ORNAMENT ground attenuation algorithm was not found to be statistically different from zero. The STAMINA 2.0 program predicts little, if any, ground attenuation for receivers at typical first-row distances from highways where noise barriers are used. The ORNAMENT ground attenuation algorithm, which recognizes and better compensates for the presence of obstacles in the propagation path of a sound wave, predicted significant amounts of ground attenuation for most sites.

Automatic spectral imaging protocol selection and iterative reconstruction in abdominal CT with reduced contrast agent dose: initial experience.

PubMed

Lv, Peijie; Liu, Jie; Chai, Yaru; Yan, Xiaopeng; Gao, Jianbo; Dong, Junqiang

2017-01-01

To evaluate the feasibility, image quality, and radiation dose of automatic spectral imaging protocol selection (ASIS) and adaptive statistical iterative reconstruction (ASIR) with reduced contrast agent dose in abdominal multiphase CT. One hundred and sixty patients were randomly divided into two scan protocols (n = 80 each; protocol A, 120 kVp/450 mgI/kg, filtered back projection algorithm (FBP); protocol B, spectral CT imaging with ASIS and 40 to 70 keV monochromatic images generated per 300 mgI/kg, ASIR algorithm. Quantitative parameters (image noise and contrast-to-noise ratios [CNRs]) and qualitative visual parameters (image noise, small structures, organ enhancement, and overall image quality) were compared. Monochromatic images at 50 keV and 60 keV provided similar or lower image noise, but higher contrast and overall image quality as compared with 120-kVp images. Despite the higher image noise, 40-keV images showed similar overall image quality compared to 120-kVp images. Radiation dose did not differ between the two protocols, while contrast agent dose in protocol B was reduced by 33 %. Application of ASIR and ASIS to monochromatic imaging from 40 to 60 keV allowed contrast agent dose reduction with adequate image quality and without increasing radiation dose compared to 120 kVp with FBP. • Automatic spectral imaging protocol selection provides appropriate scan protocols. • Abdominal CT is feasible using spectral imaging and 300 mgI/kg contrast agent. • 50-keV monochromatic images with 50 % ASIR provide optimal image quality.

The ART of representation: Memory reduction and noise tolerance in a neural network vision system

NASA Astrophysics Data System (ADS)

Langley, Christopher S.

The Feature Cerebellar Model Arithmetic Computer (FCMAC) is a multiple-input-single-output neural network that can provide three-degree-of-freedom (3-DOF) pose estimation for a robotic vision system. The FCMAC provides sufficient accuracy to enable a manipulator to grasp an object from an arbitrary pose within its workspace. The network learns an appearance-based representation of an object by storing coarsely quantized feature patterns. As all unique patterns are encoded, the network size grows uncontrollably. A new architecture is introduced herein, which combines the FCMAC with an Adaptive Resonance Theory (ART) network. The ART module categorizes patterns observed during training into a set of prototypes that are used to build the FCMAC. As a result, the network no longer grows without bound, but constrains itself to a user-specified size. Pose estimates remain accurate since the ART layer tends to discard the least relevant information first. The smaller network performs recall faster, and in some cases is better for generalization, resulting in a reduction of error at recall time. The ART-Under-Constraint (ART-C) algorithm is extended to include initial filling with randomly selected patterns (referred to as ART-F). In experiments using a real-world data set, the new network performed equally well using less than one tenth the number of coarse patterns as a regular FCMAC. The FCMAC is also extended to include real-valued input activations. As a result, the network can be tuned to reject a variety of types of noise in the image feature detection. A quantitative analysis of noise tolerance was performed using four synthetic noise algorithms, and a qualitative investigation was made using noisy real-world image data. In validation experiments, the FCMAC system outperformed Radial Basis Function (RBF) networks for the 3-DOF problem, and had accuracy comparable to that of Principal Component Analysis (PCA) and superior to that of Shape Context Matching (SCM), both of which estimate orientation only.

A voting-based star identification algorithm utilizing local and global distribution

NASA Astrophysics Data System (ADS)

Fan, Qiaoyun; Zhong, Xuyang; Sun, Junhua

2018-03-01

A novel star identification algorithm based on voting scheme is presented in this paper. In the proposed algorithm, the global distribution and local distribution of sensor stars are fully utilized, and the stratified voting scheme is adopted to obtain the candidates for sensor stars. The database optimization is employed to reduce its memory requirement and improve the robustness of the proposed algorithm. The simulation shows that the proposed algorithm exhibits 99.81% identification rate with 2-pixel standard deviations of positional noises and 0.322-Mv magnitude noises. Compared with two similar algorithms, the proposed algorithm is more robust towards noise, and the average identification time and required memory is less. Furthermore, the real sky test shows that the proposed algorithm performs well on the real star images.

High-Speed Jet Noise Reduction NASA Perspective

NASA Technical Reports Server (NTRS)

Huff, Dennis L.; Handy, J. (Technical Monitor)

2001-01-01

History shows that the problem of high-speed jet noise reduction is difficult to solve. the good news is that high performance military aircraft noise is dominated by a single source called 'jet noise' (commercial aircraft have several sources). The bad news is that this source has been the subject of research for the past 50 years and progress has been incremental. Major jet noise reduction has been achieved through changing the cycle of the engine to reduce the jet exit velocity. Smaller reductions have been achieved using suppression devices like mixing enhancement and acoustic liners. Significant jet noise reduction without any performance loss is probably not possible! Recent NASA Noise Reduction Research Programs include the High Speed Research Program, Advanced Subsonic Technology Noise Reduction Program, Aerospace Propulsion and Power Program - Fundamental Noise, and Quiet Aircraft Technology Program.

An improved method based on wavelet coefficient correlation to filter noise in Doppler ultrasound blood flow signals

NASA Astrophysics Data System (ADS)

Wan, Renzhi; Zu, Yunxiao; Shao, Lin

2018-04-01

The blood echo signal maintained through Medical ultrasound Doppler devices would always include vascular wall pulsation signal .The traditional method to de-noise wall signal is using high-pass filter, which will also remove the lowfrequency part of the blood flow signal. Some scholars put forward a method based on region selective reduction, which at first estimates of the wall pulsation signals and then removes the wall signal from the mixed signal. Apparently, this method uses the correlation between wavelet coefficients to distinguish blood signal from wall signal, but in fact it is a kind of wavelet threshold de-noising method, whose effect is not so much ideal. In order to maintain a better effect, this paper proposes an improved method based on wavelet coefficient correlation to separate blood signal and wall signal, and simulates the algorithm by computer to verify its validity.

Daniell method for power spectral density estimation in atomic force microscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Labuda, Aleksander

An alternative method for power spectral density (PSD) estimation—the Daniell method—is revisited and compared to the most prevalent method used in the field of atomic force microscopy for quantifying cantilever thermal motion—the Bartlett method. Both methods are shown to underestimate the Q factor of a simple harmonic oscillator (SHO) by a predictable, and therefore correctable, amount in the absence of spurious deterministic noise sources. However, the Bartlett method is much more prone to spectral leakage which can obscure the thermal spectrum in the presence of deterministic noise. By the significant reduction in spectral leakage, the Daniell method leads to amore » more accurate representation of the true PSD and enables clear identification and rejection of deterministic noise peaks. This benefit is especially valuable for the development of automated PSD fitting algorithms for robust and accurate estimation of SHO parameters from a thermal spectrum.« less

Evaluation of piezoceramic actuators for control of aircraft interior noise

NASA Technical Reports Server (NTRS)

Silcox, Richard J.; Lefebvre, Sylvie; Metcalf, Vern L.; Beyer, Todd B.; Fuller, Chris R.

1992-01-01

Results of an experiment to evaluate piezoceramic actuators as the control actuator for active control of interior noise in a large-scale fuselage model are presented. Control was demonstrated for tonal excitation using a time domain least mean squares algorithm. A maximum of four actuator channels and six error signals were used. The actuators were employed for control of noise at frequencies where interior cavity modes were the dominant response and for driven acoustic responses where a structure resonance was dominant. Global reductions of 9 to 12 dB were obtained for the cases examined. The most effective configuration of skin-mounted actuators was found to be a pure in-plane forcing function as opposed to a bending excitation. The frame-mounted actuators were found to be equally effective as the skin-mounted actuators. However, both configurations resulted in local regions of unacceptably high vibration response in the structure.

Critical Propulsion and Noise reduction Technologies for Future Commercial Subsonic Engines. Area of Interest 14.3: Separate Flow Exhaust System Noise

NASA Technical Reports Server (NTRS)

Janardan, B. A.; Hoff, G. E.; Barter, J. W.; Brausch, J. F.; Gliebe, P. R.; Coffin, R. S.; Martens, S.; Delaney, B. R.; Dalton, W. N.; Mengle, V. G.

2000-01-01

This presentation discusses: Project Objectives, Approach and Goal; Baseline Nozzles and Test Cycle Definition; Repeatability and Baseline Nozzle Results; Noise Reduction Concepts; Noise Reduction Tests Configurations of BPR=5 Internal Plug Nozzle adn Acoustic Results; Noise Reduction Test Configurations of BPR=5 External Plug Nozzle and Acoustic Results; and Noise Reduction Tests Configurations of BPR=8 External Plug Nozzle and Acoustic Results.

Critical Low-Noise Technologies Being Developed for Engine Noise Reduction Systems Subproject

NASA Technical Reports Server (NTRS)

Grady, Joseph E.; Civinskas, Kestutis C.

2004-01-01

NASA's previous Advanced Subsonic Technology (AST) Noise Reduction Program delivered the initial technologies for meeting a 10-year goal of a 10-dB reduction in total aircraft system noise. Technology Readiness Levels achieved for the engine-noise-reduction technologies ranged from 4 (rig scale) to 6 (engine demonstration). The current Quiet Aircraft Technology (QAT) project is building on those AST accomplishments to achieve the additional noise reduction needed to meet the Aerospace Technology Enterprise's 10-year goal, again validated through a combination of laboratory rig and engine demonstration tests. In order to meet the Aerospace Technology Enterprise goal for future aircraft of a 50- reduction in the perceived noise level, reductions of 4 dB are needed in both fan and jet noise. The primary objectives of the Engine Noise Reduction Systems (ENRS) subproject are, therefore, to develop technologies to reduce both fan and jet noise by 4 dB, to demonstrate these technologies in engine tests, and to develop and experimentally validate Computational Aero Acoustics (CAA) computer codes that will improve our ability to predict engine noise.

A new Mumford-Shah total variation minimization based model for sparse-view x-ray computed tomography image reconstruction.

PubMed

Chen, Bo; Bian, Zhaoying; Zhou, Xiaohui; Chen, Wensheng; Ma, Jianhua; Liang, Zhengrong

2018-04-12

Total variation (TV) minimization for the sparse-view x-ray computer tomography (CT) reconstruction has been widely explored to reduce radiation dose. However, due to the piecewise constant assumption for the TV model, the reconstructed images often suffer from over-smoothness on the image edges. To mitigate this drawback of TV minimization, we present a Mumford-Shah total variation (MSTV) minimization algorithm in this paper. The presented MSTV model is derived by integrating TV minimization and Mumford-Shah segmentation. Subsequently, a penalized weighted least-squares (PWLS) scheme with MSTV is developed for the sparse-view CT reconstruction. For simplicity, the proposed algorithm is named as 'PWLS-MSTV.' To evaluate the performance of the present PWLS-MSTV algorithm, both qualitative and quantitative studies were conducted by using a digital XCAT phantom and a physical phantom. Experimental results show that the present PWLS-MSTV algorithm has noticeable gains over the existing algorithms in terms of noise reduction, contrast-to-ratio measure and edge-preservation.

Fractal Landscape Algorithms for Environmental Simulations

NASA Astrophysics Data System (ADS)

Mao, H.; Moran, S.

2014-12-01

Natural science and geographical research are now able to take advantage of environmental simulations that more accurately test experimental hypotheses, resulting in deeper understanding. Experiments affected by the natural environment can benefit from 3D landscape simulations capable of simulating a variety of terrains and environmental phenomena. Such simulations can employ random terrain generation algorithms that dynamically simulate environments to test specific models against a variety of factors. Through the use of noise functions such as Perlin noise, Simplex noise, and diamond square algorithms, computers can generate simulations that model a variety of landscapes and ecosystems. This study shows how these algorithms work together to create realistic landscapes. By seeding values into the diamond square algorithm, one can control the shape of landscape. Perlin noise and Simplex noise are also used to simulate moisture and temperature. The smooth gradient created by coherent noise allows more realistic landscapes to be simulated. Terrain generation algorithms can be used in environmental studies and physics simulations. Potential studies that would benefit from simulations include the geophysical impact of flash floods or drought on a particular region and regional impacts on low lying area due to global warming and rising sea levels. Furthermore, terrain generation algorithms also serve as aesthetic tools to display landscapes (Google Earth), and simulate planetary landscapes. Hence, it can be used as a tool to assist science education. Algorithms used to generate these natural phenomena provide scientists a different approach in analyzing our world. The random algorithms used in terrain generation not only contribute to the generating the terrains themselves, but are also capable of simulating weather patterns.

Wavelet tree structure based speckle noise removal for optical coherence tomography

NASA Astrophysics Data System (ADS)

Yuan, Xin; Liu, Xuan; Liu, Yang

2018-02-01

We report a new speckle noise removal algorithm in optical coherence tomography (OCT). Though wavelet domain thresholding algorithms have demonstrated superior advantages in suppressing noise magnitude and preserving image sharpness in OCT, the wavelet tree structure has not been investigated in previous applications. In this work, we propose an adaptive wavelet thresholding algorithm via exploiting the tree structure in wavelet coefficients to remove the speckle noise in OCT images. The threshold for each wavelet band is adaptively selected following a special rule to retain the structure of the image across different wavelet layers. Our results demonstrate that the proposed algorithm outperforms conventional wavelet thresholding, with significant advantages in preserving image features.

Resource sharing on CSMA/CD networks in the presence of noise. M.S. Thesis

NASA Technical Reports Server (NTRS)

Dinschel, Duane Edward

1987-01-01

Resource sharing on carrier sense multiple access with collision detection (CSMA/CD) networks can be accomplished by using window-control algorithms for bus contention. The window-control algorithms are designed to grant permission to transmit to the station with the minimum contention parameter. Proper operation of the window-control algorithm requires that all stations sense the same state of the newtork in each contention slot. Noise causes the state of the network to appear as a collision. False collisions can cause the window-control algorithm to terminate without isolating any stations. A two-phase window-control protocol and approximate recurrence equation with noise as a parameter to improve the performance of the window-control algorithms in the presence of noise are developed. The results are compared through simulation, with the approximate recurrence equation yielding the best overall performance. Noise is even a bigger problem when it is not detected by all stations. In such cases it is possible for the window boundaries of the contending stations to become out of phase. Consequently, it is possible to isolate a station other than the one with the minimum contention parameter. To guarantee proper isolation of the minimum, a broadcast phase must be added after the termination of the algorithm. The protocol required to correct the window-control algorithm when noise is not detected by all stations is discussed.

SU-E-I-89: Assessment of CT Radiation Dose and Image Quality for An Automated Tube Potential Selection Algorithm Using Pediatric Anthropomorphic and ACR Phantoms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mahmood, U; Erdi, Y; Wang, W

Purpose: To assess the impact of General Electrics automated tube potential algorithm, kV assist (kVa) on radiation dose and image quality, with an emphasis on optimizing protocols based on noise texture. Methods: Radiation dose was assessed by inserting optically stimulated luminescence dosimeters (OSLs) throughout the body of a pediatric anthropomorphic phantom (CIRS). The baseline protocol was: 120 kVp, 80 mA, 0.7s rotation time. Image quality was assessed by calculating the contrast to noise ratio (CNR) and noise power spectrum (NPS) from the ACR CT accreditation phantom. CNRs were calculated according to the steps described in ACR CT phantom testing document.more » NPS was determined by taking the 3D FFT of the uniformity section of the ACR phantom. NPS and CNR were evaluated with and without kVa and for all available adaptive iterative statistical reconstruction (ASiR) settings, ranging from 0 to 100%. Each NPS was also evaluated for its peak frequency difference (PFD) with respect to the baseline protocol. Results: For the baseline protocol, CNR was found to decrease from 0.460 ± 0.182 to 0.420 ± 0.057 when kVa was activated. When compared against the baseline protocol, the PFD at ASiR of 40% yielded a decrease in noise magnitude as realized by the increase in CNR = 0.620 ± 0.040. The liver dose decreased by 30% with kVa activation. Conclusion: Application of kVa reduces the liver dose up to 30%. However, reduction in image quality for abdominal scans occurs when using the automated tube voltage selection feature at the baseline protocol. As demonstrated by the CNR and NPS analysis, the texture and magnitude of the noise in reconstructed images at ASiR 40% was found to be the same as our baseline images. We have demonstrated that 30% dose reduction is possible when using 40% ASiR with kVa in pediatric patients.« less

Compensation for the signal processing characteristics of ultrasound B-mode scanners in adaptive speckle reduction.

PubMed

Crawford, D C; Bell, D S; Bamber, J C

1993-01-01

A systematic method to compensate for nonlinear amplification of individual ultrasound B-scanners has been investigated in order to optimise performance of an adaptive speckle reduction (ASR) filter for a wide range of clinical ultrasonic imaging equipment. Three potential methods have been investigated: (1) a method involving an appropriate selection of the speckle recognition feature was successful when the scanner signal processing executes simple logarithmic compressions; (2) an inverse transform (decompression) of the B-mode image was effective in correcting for the measured characteristics of image data compression when the algorithm was implemented in full floating point arithmetic; (3) characterising the behaviour of the statistical speckle recognition feature under conditions of speckle noise was found to be the method of choice for implementation of the adaptive speckle reduction algorithm in limited precision integer arithmetic. In this example, the statistical features of variance and mean were investigated. The third method may be implemented on commercially available fast image processing hardware and is also better suited for transfer into dedicated hardware to facilitate real-time adaptive speckle reduction. A systematic method is described for obtaining ASR calibration data from B-mode images of a speckle producing phantom.

Experimental study of noise reduction for an unstiffened cylindrical model of an airplane fuselage

NASA Astrophysics Data System (ADS)

Willis, C. M.; Daniels, E. F.

1981-12-01

Noise reduction measurements were made for a simplified model of an airplane fuselage consisting of an unstiffened aluminum cylinder 0.5 m in diameter by 1.2 m long with a 1.6-mm-thick wall. Noise reduction was first measured with a reverberant field pink-noise load on the cylinder exterior. Next, noise reduction was measured by using a propeller to provide a more realistic noise load on the cylinder. Structural resonance frequencies and acoustic reverberation times for the cylinder interior volume were also measured. Comparison of data from the relatively simple test using reverberant-field noise with data from the more complex propeller-noise tests indicates some similarity in both the overall noise reduction and the spectral distribution. However, all of the test parameters investigated (propeller speed, blade pitch, and tip clearance) had some effect on the noise-reduction spectra. Thus, the amount of noise reduction achieved appears to be somewhat dependent upon the spectral and spatial characteristics of the flight conditions. Information is also presented on cyclinder resonance frequencies, damping, and characteristics of propeller-noise loads.

A critical review of hearing-aid single-microphone noise-reduction studies in adults and children.

PubMed

Chong, Foong Yen; Jenstad, Lorienne M

2017-10-26

Single-microphone noise reduction (SMNR) is implemented in hearing aids to suppress background noise. The purpose of this article was to provide a critical review of peer-reviewed studies in adults and children with sensorineural hearing loss who were fitted with hearing aids incorporating SMNR. Articles published between 2000 and 2016 were searched in PUBMED and EBSCO databases. Thirty-two articles were included in the final review. Most studies with adult participants showed that SMNR has no effect on speech intelligibility. Positive results were reported for acceptance of background noise, preference, and listening effort. Studies of school-aged children were consistent with the findings of adult studies. No study with infants or young children of under 5 years old was found. Recent studies on noise-reduction systems not yet available in wearable hearing aids have documented benefits of noise reduction on memory for speech processing for older adults. This evidence supports the use of SMNR for adults and school-aged children when the aim is to improve listening comfort or reduce listening effort. Future research should test SMNR with infants and children who are younger than 5 years of age. Further development, testing, and clinical trials should be carried out on algorithms not yet available in wearable hearing aids. Testing higher cognitive level for speech processing and learning of novel sounds or words could show benefits of advanced signal processing features. These approaches should be expanded to other populations such as children and younger adults. Implications for rehabilitation The review provides a quick reference for students and clinicians regarding the efficacy and effectiveness of SMNR in wearable hearing aids. This information is useful during counseling session to build a realistic expectation among hearing aid users. Most studies in the adult population suggest that SMNR may provide some benefits to adult listeners in terms of listening comfort, acceptance of background noise, and release of cognitive load in a complex listening condition. However, it does not improve speech intelligibility. Studies that examined SMNR in the paediatric population suggest that SMNR may benefit older school-aged children, aged between 10 and 12 years old. The evidence supports the use of SMNR for adults and school-aged children when the aim is to improve listening comfort or reduce listening effort.

GIFTS SM EDU Level 1B Algorithms

NASA Technical Reports Server (NTRS)

Tian, Jialin; Gazarik, Michael J.; Reisse, Robert A.; Johnson, David G.

2007-01-01

The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) SensorModule (SM) Engineering Demonstration Unit (EDU) is a high resolution spectral imager designed to measure infrared (IR) radiances using a Fourier transform spectrometer (FTS). The GIFTS instrument employs three focal plane arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The raw interferogram measurements are radiometrically and spectrally calibrated to produce radiance spectra, which are further processed to obtain atmospheric profiles via retrieval algorithms. This paper describes the GIFTS SM EDU Level 1B algorithms involved in the calibration. The GIFTS Level 1B calibration procedures can be subdivided into four blocks. In the first block, the measured raw interferograms are first corrected for the detector nonlinearity distortion, followed by the complex filtering and decimation procedure. In the second block, a phase correction algorithm is applied to the filtered and decimated complex interferograms. The resulting imaginary part of the spectrum contains only the noise component of the uncorrected spectrum. Additional random noise reduction can be accomplished by applying a spectral smoothing routine to the phase-corrected spectrum. The phase correction and spectral smoothing operations are performed on a set of interferogram scans for both ambient and hot blackbody references. To continue with the calibration, we compute the spectral responsivity based on the previous results, from which, the calibrated ambient blackbody (ABB), hot blackbody (HBB), and scene spectra can be obtained. We now can estimate the noise equivalent spectral radiance (NESR) from the calibrated ABB and HBB spectra. The correction schemes that compensate for the fore-optics offsets and off-axis effects are also implemented. In the third block, we developed an efficient method of generating pixel performance assessments. In addition, a random pixel selection scheme is designed based on the pixel performance evaluation. Finally, in the fourth block, the single pixel algorithms are applied to the entire FPA.

Mechanisms of Undersensing by a Noise Detection Algorithm That Utilizes Far-Field Electrograms With Near-Field Bandpass Filtering.

PubMed

Koneru, Jayanthi N; Swerdlow, Charles D; Ploux, Sylvain; Sharma, Parikshit S; Kaszala, Karoly; Tan, Alex Y; Huizar, Jose F; Vijayaraman, Pugazhendi; Kenigsberg, David; Ellenbogen, Kenneth A

2017-02-01

Implantable cardioverter defibrillators (ICDs) must establish a balance between delivering appropriate shocks for ventricular tachyarrhythmias and withholding inappropriate shocks for lead-related oversensing ("noise"). To improve the specificity of ICD therapy, manufacturers have developed proprietary algorithms that detect lead noise. The SecureSense TM RV Lead Noise discrimination (St. Jude Medical, St. Paul, MN, USA) algorithm is designed to differentiate oversensing due to lead failure from ventricular tachyarrhythmias and withhold therapies in the presence of sustained lead-related oversensing. We report 5 patients in whom appropriate ICD therapy was withheld due to the operation of the SecureSense algorithm and explain the mechanism for inhibition of therapy in each case. Limitations of algorithms designed to increase ICD therapy specificity, especially for the SecureSense algorithm, are analyzed. The SecureSense algorithm can withhold appropriate therapies for ventricular arrhythmias due to design and programming limitations. Electrophysiologists should have a thorough understanding of the SecureSense algorithm before routinely programming it and understand the implications for ventricular arrhythmia misclassification. © 2016 Wiley Periodicals, Inc.

Performance of Optimized Actuator and Sensor Arrays in an Active Noise Control System

NASA Technical Reports Server (NTRS)

Palumbo, D. L.; Padula, S. L.; Lyle, K. H.; Cline, J. H.; Cabell, R. H.

1996-01-01

Experiments have been conducted in NASA Langley's Acoustics and Dynamics Laboratory to determine the effectiveness of optimized actuator/sensor architectures and controller algorithms for active control of harmonic interior noise. Tests were conducted in a large scale fuselage model - a composite cylinder which simulates a commuter class aircraft fuselage with three sections of trim panel and a floor. Using an optimization technique based on the component transfer functions, combinations of 4 out of 8 piezoceramic actuators and 8 out of 462 microphone locations were evaluated against predicted performance. A combinatorial optimization technique called tabu search was employed to select the optimum transducer arrays. Three test frequencies represent the cases of a strong acoustic and strong structural response, a weak acoustic and strong structural response and a strong acoustic and weak structural response. Noise reduction was obtained using a Time Averaged/Gradient Descent (TAGD) controller. Results indicate that the optimization technique successfully predicted best and worst case performance. An enhancement of the TAGD control algorithm was also evaluated. The principal components of the actuator/sensor transfer functions were used in the PC-TAGD controller. The principal components are shown to be independent of each other while providing control as effective as the standard TAGD.

a Single-Exposure Dual-Energy Computed Radiography Technique for Improved Nodule Detection and Classification in Chest Imaging

NASA Astrophysics Data System (ADS)

Zink, Frank Edward

The detection and classification of pulmonary nodules is of great interest in chest radiography. Nodules are often indicative of primary cancer, and their detection is particularly important in asymptomatic patients. The ability to classify nodules as calcified or non-calcified is important because calcification is a positive indicator that the nodule is benign. Dual-energy methods offer the potential to improve both the detection and classification of nodules by allowing the formation of material-selective images. Tissue-selective images can improve detection by virtue of the elimination of obscuring rib structure. Bone -selective images are essentially calcium images, allowing classification of the nodule. A dual-energy technique is introduced which uses a computed radiography system to acquire dual-energy chest radiographs in a single-exposure. All aspects of the dual-energy technique are described, with particular emphasis on scatter-correction, beam-hardening correction, and noise-reduction algorithms. The adaptive noise-reduction algorithm employed improves material-selective signal-to-noise ratio by up to a factor of seven with minimal sacrifice in selectivity. A clinical comparison study is described, undertaken to compare the dual-energy technique to conventional chest radiography for the tasks of nodule detection and classification. Observer performance data were collected using the Free Response Observer Characteristic (FROC) method and the bi-normal Alternative FROC (AFROC) performance model. Results of the comparison study, analyzed using two common multiple observer statistical models, showed that the dual-energy technique was superior to conventional chest radiography for detection of nodules at a statistically significant level (p < .05). Discussion of the comparison study emphasizes the unique combination of data collection and analysis techniques employed, as well as the limitations of comparison techniques in the larger context of technology assessment.

An assessment of the effectiveness of a random forest classifier for land-cover classification

NASA Astrophysics Data System (ADS)

Rodriguez-Galiano, V. F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J. P.

2012-01-01

Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level.

The Characterization of Military Aircraft Jet Noise Using Near-Field Acoustical Holography Methods

NASA Astrophysics Data System (ADS)

Wall, Alan Thomas

The noise emissions of jets from full-scale engines installed on military aircraft pose a significant hearing loss risk to military personnel. Noise reduction technologies and the development of operational procedures that minimize noise exposure to personnel are enhanced by the accurate characterization of noise sources within a jet. Hence, more than six decades of research have gone into jet noise measurement and prediction. In the past decade, the noise-source visualization tool near-field acoustical holography (NAH) has been applied to jets. NAH fits a weighted set of expansion wave functions, typically planar, cylindrical, or spherical, to measured sound pressures in the field. NAH measurements were made of a jet from an installed engine on a military aircraft. In the present study, the algorithm of statistically optimized NAH (SONAH) is modified to account for the presence of acoustic reflections from the concrete surface over which the jet was measured. The three dimensional field in the jet vicinity is reconstructed, and information about sources is inferred from reconstructions at the boundary of the turbulent jet flow. Then, a partial field decomposition (PFD) is performed, which represents the total field as the superposition of multiple, independent partial fields. This is the most direct attempt to equate partial fields with independent sources in a jet to date.

Listening effort and perceived clarity for normal-hearing children with the use of digital noise reduction.

PubMed

Gustafson, Samantha; McCreery, Ryan; Hoover, Brenda; Kopun, Judy G; Stelmachowicz, Pat

2014-01-01

The goal of this study was to evaluate how digital noise reduction (DNR) impacts listening effort and judgment of sound clarity in children with normal hearing. It was hypothesized that when two DNR algorithms differing in signal-to-noise ratio (SNR) output are compared, the algorithm that provides the greatest improvement in overall output SNR will reduce listening effort and receive a better clarity rating from child listeners. A secondary goal was to evaluate the relation between the inversion method measurements and listening effort with DNR processing. Twenty-four children with normal hearing (ages 7 to 12 years) participated in a speech recognition task in which consonant-vowel-consonant nonwords were presented in broadband background noise. Test stimuli were recorded through two hearing aids with DNR off and DNR on at 0 dB and +5 dB input SNR. Stimuli were presented to listeners and verbal response time (VRT) and phoneme recognition scores were measured. The underlying assumption was that an increase in VRT reflects an increase in listening effort. Children rated the sound clarity for each condition. The two commercially available HAs were chosen based on: (1) an inversion technique, which was used to quantify the magnitude of change in SNR with the activation of DNR, and (2) a measure of magnitude-squared coherence, which was used to ensure that DNR in both devices preserved the spectrum. One device provided a greater improvement in overall output SNR than the other. Both DNR algorithms resulted in minimal spectral distortion as measured using coherence. For both devices, VRT decreased for the DNR-on condition, suggesting that listening effort decreased with DNR in both devices. Clarity ratings were also better in the DNR-on condition for both devices. The device showing the greatest improvement in output SNR with DNR engaged improved phoneme recognition scores. The magnitude of this improved phoneme recognition was not accurately predicted with measurements of output SNR. Measured output SNR varied in the ability to predict other outcomes. Overall, results suggest that DNR effectively reduces listening effort and improves subjective clarity ratings in children with normal hearing but that these improvements are not necessarily related to the output SNR improvements or preserved speech spectra provided by the DNR.

Listening effort and perceived clarity for normal hearing children with the use of digital noise reduction

PubMed Central

Gustafson, Samantha; McCreery, Ryan; Hoover, Brenda; Kopun, Judy G; Stelmachowicz, Pat

2013-01-01

Objectives The goal of this study was to evaluate how digital noise reduction (DNR) impacts listening effort and judgment of sound clarity in children with normal hearing. It was hypothesized that, when two DNR algorithms differing in signal-to-noise ratio (SNR) output are compared, the algorithm which provides the greatest improvement in overall output SNR will reduce listening effort and receive a better clarity rating from child listeners. A secondary goal was to evaluate the relation between the inversion method measurements and listening effort with DNR processing. Design Twenty-four children with normal hearing (ages 7-12 years) participated in a speech recognition task in which consonant-vowel-consonant nonwords were presented in broadband background noise. Test stimuli were recorded through two hearing aids with DNR-off and DNR-on at 0 dB and +5 dB input SNR. Stimuli were presented to listeners and verbal response time (VRT) and phoneme recognition scores were measured. The underlying assumption was that an increase in VRT reflects an in increase in listening effort. Children rated the sound clarity for each condition. The two commercially available HAs were chosen based on: 1) an inversion technique which was used to quantify the magnitude of change in SNR with the activation of DNR, and 2) a measure of magnitude-squared coherence which was used to ensure that DNR in both devices preserved the spectrum. Results One device provided a greater improvement in overall output SNR than the other. Both DNR algorithms resulted in minimal spectral distortion as measured using coherence. For both devices, VRT decreased for the DNR-on condition suggesting that listening effort decreased with DNR in both devices. Clarity ratings were also better in the DNR-on condition for both devices. The device showing the greatest improvement in output SNR with DNR engaged improved phoneme recognition scores. The magnitude of this improved phoneme recognition was not accurately predicted with measurements of output SNR. Measured output SNR varied in the ability to predict other outcomes. Conclusions Overall, results suggest that DNR effectively reduces listening effort and improves subjective clarity ratings in children with normal hearing but that these improvements are not necessarily related to the output SNR improvements or preserved speech spectra provided by the DNR. PMID:24473240

Emerging Community Noise Reduction Approaches

NASA Technical Reports Server (NTRS)

Envia, Edmane

2012-01-01

An overview of the current NASA research portfolio in the area of aircraft noise reduction is presented. The emphasis of the research described herein is on meeting the aggressive near- and mid-term national goals for reducing aircraft noise emissions, which NASA internal studies have shown to be feasible using noise reduction technologies currently being developed in-house or in partnership with NASA s industry and academic partners. While NASA has an active research effort in airframe noise reduction, this overview focuses on propulsion noise reduction only.

Use of a channelized Hotelling observer to assess CT image quality and optimize dose reduction for iteratively reconstructed images.

PubMed

Favazza, Christopher P; Ferrero, Andrea; Yu, Lifeng; Leng, Shuai; McMillan, Kyle L; McCollough, Cynthia H

2017-07-01

The use of iterative reconstruction (IR) algorithms in CT generally decreases image noise and enables dose reduction. However, the amount of dose reduction possible using IR without sacrificing diagnostic performance is difficult to assess with conventional image quality metrics. Through this investigation, achievable dose reduction using a commercially available IR algorithm without loss of low contrast spatial resolution was determined with a channelized Hotelling observer (CHO) model and used to optimize a clinical abdomen/pelvis exam protocol. A phantom containing 21 low contrast disks-three different contrast levels and seven different diameters-was imaged at different dose levels. Images were created with filtered backprojection (FBP) and IR. The CHO was tasked with detecting the low contrast disks. CHO performance indicated dose could be reduced by 22% to 25% without compromising low contrast detectability (as compared to full-dose FBP images) whereas 50% or more dose reduction significantly reduced detection performance. Importantly, default settings for the scanner and protocol investigated reduced dose by upward of 75%. Subsequently, CHO-based protocol changes to the default protocol yielded images of higher quality and doses more consistent with values from a larger, dose-optimized scanner fleet. CHO assessment provided objective data to successfully optimize a clinical CT acquisition protocol.

Novel cooperative neural fusion algorithms for image restoration and image fusion.

PubMed

Xia, Youshen; Kamel, Mohamed S

2007-02-01

To deal with the problem of restoring degraded images with non-Gaussian noise, this paper proposes a novel cooperative neural fusion regularization (CNFR) algorithm for image restoration. Compared with conventional regularization algorithms for image restoration, the proposed CNFR algorithm can relax need of the optimal regularization parameter to be estimated. Furthermore, to enhance the quality of restored images, this paper presents a cooperative neural fusion (CNF) algorithm for image fusion. Compared with existing signal-level image fusion algorithms, the proposed CNF algorithm can greatly reduce the loss of contrast information under blind Gaussian noise environments. The performance analysis shows that the proposed two neural fusion algorithms can converge globally to the robust and optimal image estimate. Simulation results confirm that in different noise environments, the proposed two neural fusion algorithms can obtain a better image estimate than several well known image restoration and image fusion methods.

Utilisation of polyurethane composit with 50% composition of roystonea regia fiber as noise reduction panel on car hood

NASA Astrophysics Data System (ADS)

Ikhwansyah; Mulia; Gunawan, S.; Lubis, R. D. W.

2018-02-01

The objective is to get the characteristics of noise reduction, noise reduction level, variety of measurement spaces, and knowing the process in making acoustic material of natural fiber becomes noise reduction on a car hood. The process of making noise reduction material used casting method and pressed by using molded press. The composition of noise reduction material consist of 50% roystonea regia by 32 mesh and 50% combined by gypsum and polyurethane. The result shows that the average result of noise reduction at X1- side is 5,7% and X2- side is 3,9%, X1+ side is 0,9% and X2+ side is 6,2%, Z1- side is 8,9% and Z2- side is 10,1%, Z1+ side is 9,7% and Z2+ side is 10,01%. The main conclusion of the study shows that a noise reduction which made of roystonea regia with 32 mesh mixed by matrix of polyurethane and gypsum is appropriate for noise reduction on car hood.

Adaptive Noise Suppression Using Digital Signal Processing

NASA Technical Reports Server (NTRS)

Kozel, David; Nelson, Richard

1996-01-01

A signal to noise ratio dependent adaptive spectral subtraction algorithm is developed to eliminate noise from noise corrupted speech signals. The algorithm determines the signal to noise ratio and adjusts the spectral subtraction proportion appropriately. After spectra subtraction low amplitude signals are squelched. A single microphone is used to obtain both eh noise corrupted speech and the average noise estimate. This is done by determining if the frame of data being sampled is a voiced or unvoiced frame. During unvoice frames an estimate of the noise is obtained. A running average of the noise is used to approximate the expected value of the noise. Applications include the emergency egress vehicle and the crawler transporter.

LLSURE: local linear SURE-based edge-preserving image filtering.

PubMed

Qiu, Tianshuang; Wang, Aiqi; Yu, Nannan; Song, Aimin

2013-01-01

In this paper, we propose a novel approach for performing high-quality edge-preserving image filtering. Based on a local linear model and using the principle of Stein's unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various computer vision applications, including noise reduction, detail smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising.

Error reduction in EMG signal decomposition

PubMed Central

Kline, Joshua C.

2014-01-01

Decomposition of the electromyographic (EMG) signal into constituent action potentials and the identification of individual firing instances of each motor unit in the presence of ambient noise are inherently probabilistic processes, whether performed manually or with automated algorithms. Consequently, they are subject to errors. We set out to classify and reduce these errors by analyzing 1,061 motor-unit action-potential trains (MUAPTs), obtained by decomposing surface EMG (sEMG) signals recorded during human voluntary contractions. Decomposition errors were classified into two general categories: location errors representing variability in the temporal localization of each motor-unit firing instance and identification errors consisting of falsely detected or missed firing instances. To mitigate these errors, we developed an error-reduction algorithm that combines multiple decomposition estimates to determine a more probable estimate of motor-unit firing instances with fewer errors. The performance of the algorithm is governed by a trade-off between the yield of MUAPTs obtained above a given accuracy level and the time required to perform the decomposition. When applied to a set of sEMG signals synthesized from real MUAPTs, the identification error was reduced by an average of 1.78%, improving the accuracy to 97.0%, and the location error was reduced by an average of 1.66 ms. The error-reduction algorithm in this study is not limited to any specific decomposition strategy. Rather, we propose it be used for other decomposition methods, especially when analyzing precise motor-unit firing instances, as occurs when measuring synchronization. PMID:25210159

A research program to reduce interior noise in general aviation airplanes. Influence of depressurization and damping material on the noise reduction characteristics of flat and curved stiffened panels

NASA Technical Reports Server (NTRS)

Navaneethan, R.; Streeter, B.; Koontz, S.; Roskam, J.

1981-01-01

Some 20 x 20 aluminum panels were studied in a frequency range from 20 Hz to 5000 Hz. The noise sources used were a swept sine wave generator and a random noise generator. The effect of noise source was found to be negligible. Increasing the pressure differential across the panel gave better noise reduction below the fundamental resonance frequency due to an increase in stiffness. The largest increase occurred in the first 1 psi pressure differential. The curved, stiffened panel exhibited similar behavior, but with a lower increase of low frequency noise reduction. Depressurization on these panels resulted in decreased noise reduction at higher frequencies. The effect of damping tapes on the overall noise reduction values of the test specimens was small away from the resonance frequency. In the mass-law region, a slight and proportional improvement in noise reduction was observed by adding damping material. Adding sound absorbtion material to a panel with damping material beneficially increased noise reduction at high frequencies.

Objective speech quality assessment and the RPE-LTP coding algorithm in different noise and language conditions.

PubMed

Hansen, J H; Nandkumar, S

1995-01-01

The formulation of reliable signal processing algorithms for speech coding and synthesis require the selection of a prior criterion of performance. Though coding efficiency (bits/second) or computational requirements can be used, a final performance measure must always include speech quality. In this paper, three objective speech quality measures are considered with respect to quality assessment for American English, noisy American English, and noise-free versions of seven languages. The purpose is to determine whether objective quality measures can be used to quantify changes in quality for a given voice coding method, with a known subjective performance level, as background noise or language conditions are changed. The speech coding algorithm chosen is regular-pulse excitation with long-term prediction (RPE-LTP), which has been chosen as the standard voice compression algorithm for the European Digital Mobile Radio system. Three areas are considered for objective quality assessment which include: (i) vocoder performance for American English in a noise-free environment, (ii) speech quality variation for three additive background noise sources, and (iii) noise-free performance for seven languages which include English, Japanese, Finnish, German, Hindi, Spanish, and French. It is suggested that although existing objective quality measures will never replace subjective testing, they can be a useful means of assessing changes in performance, identifying areas for improvement in algorithm design, and augmenting subjective quality tests for voice coding/compression algorithms in noise-free, noisy, and/or non-English applications.

Detection of low-contrast images in film-grain noise.

PubMed

Naderi, F; Sawchuk, A A

1978-09-15

When low contrast photographic images are digitized by a very small aperture, extreme film-grain noise almost completely obliterates the image information. Using a large aperture to average out the noise destroys the fine details of the image. In these situations conventional statistical restoration techniques have little effect, and well chosen heuristic algorithms have yielded better results. In this paper we analyze the noisecheating algorithm of Zweig et al. [J. Opt. Soc. Am. 65, 1347 (1975)] and show that it can be justified by classical maximum-likelihood detection theory. A more general algorithm applicable to a broader class of images is then developed by considering the signal-dependent nature of film-grain noise. Finally, a Bayesian detection algorithm with improved performance is presented.

Relationship between Aircraft Noise Contour Area and Noise Levels at Certification Points

NASA Technical Reports Server (NTRS)

Powell, Clemans A.

2003-01-01

The use of sound exposure level contour area reduction has been proposed as an alternative or supplemental metric of progress and success for the NASA Quiet Aircraft Technology program, which currently uses the average of predicted noise reductions at three community locations. As the program has expanded to include reductions in airframe noise as well as reduction due to optimization of operating procedures for lower noise, there is concern that the three-point methodology may not represent a fair measure of benefit to airport communities. This paper addresses several topics related to this proposal: (1) an analytical basis for a relationship between certification noise levels and noise contour areas for departure operations is developed, (2) the relationship between predicted noise contour area and the noise levels measured or predicted at the certification measurement points is examined for a wide range of commercial and business aircraft, and (3) reductions in contour area for low-noise approach scenarios are predicted and equivalent reductions in source noise are determined.

Multi-scale graph-cut algorithm for efficient water-fat separation.

PubMed

Berglund, Johan; Skorpil, Mikael

2017-09-01

To improve the accuracy and robustness to noise in water-fat separation by unifying the multiscale and graph cut based approaches to B 0 -correction. A previously proposed water-fat separation algorithm that corrects for B 0 field inhomogeneity in 3D by a single quadratic pseudo-Boolean optimization (QPBO) graph cut was incorporated into a multi-scale framework, where field map solutions are propagated from coarse to fine scales for voxels that are not resolved by the graph cut. The accuracy of the single-scale and multi-scale QPBO algorithms was evaluated against benchmark reference datasets. The robustness to noise was evaluated by adding noise to the input data prior to water-fat separation. Both algorithms achieved the highest accuracy when compared with seven previously published methods, while computation times were acceptable for implementation in clinical routine. The multi-scale algorithm was more robust to noise than the single-scale algorithm, while causing only a small increase (+10%) of the reconstruction time. The proposed 3D multi-scale QPBO algorithm offers accurate water-fat separation, robustness to noise, and fast reconstruction. The software implementation is freely available to the research community. Magn Reson Med 78:941-949, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method

NASA Astrophysics Data System (ADS)

Shi, Xiaohui; Huang, Xianwei; Nan, Suqin; Li, Hengxing; Bai, Yanfeng; Fu, Xiquan

2018-04-01

Detector noise has a significantly negative impact on ghost imaging at low light levels, especially for existing recovery algorithm. Based on the characteristics of the additive detector noise, a method named modified compressive sensing ghost imaging is proposed to reduce the background imposed by the randomly distributed detector noise at signal path. Experimental results show that, with an appropriate choice of threshold value, modified compressive sensing ghost imaging algorithm can dramatically enhance the contrast-to-noise ratio of the object reconstruction significantly compared with traditional ghost imaging and compressive sensing ghost imaging methods. The relationship between the contrast-to-noise ratio of the reconstruction image and the intensity ratio (namely, the average signal intensity to average noise intensity ratio) for the three reconstruction algorithms are also discussed. This noise suppression imaging technique will have great applications in remote-sensing and security areas.

Reductions in Multi-Component Jet Noise by Water Injection

NASA Technical Reports Server (NTRS)

Norum, Thomas D.

2004-01-01

An experimental investigation was performed in the NASA Langley Low Speed Aeroacoustics Wind Tunnel to determine the extent of jet exhaust noise reduction that can be obtained using water injection in a hot jet environment. The effects of water parameters such as mass flow rate, injection location, and spray patterns on suppression of dominant noise sources in both subsonic and supersonic jets were determined, and extrapolations to full-scale engine noise reduction were made. Water jets and sprays were injected in to the shear layers of cold and hot circular jets operating at both subsonic and supersonic exhaust conditions. Use of convergent-divergent and convergent nozzles (2.7in. D) allowed for simulations of all major jet noise sources. The experimental results show that water injection clearly disrupts shock noise sources within the jet plume, with large reductions in radiated shock noise. There are smaller reductions in jet mixing noise, resulting in only a small decrease in effective perceived noise level when projections are made to full scale. The fact that the measured noise reduction in the direction upstream of the nozzle was consistently larger than in the noisier downstream direction contributed to keeping effective perceived noise reductions small. Variations in the operation of the water injection system clearly show that injection at the nozzle exit rather than further downstream is required for the largest noise reduction. Noise reduction increased with water pressure as well as with its mass flow, although the type of injector had little effect.

Radiation dose reduction in medical x-ray CT via Fourier-based iterative reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fahimian, Benjamin P.; Zhao Yunzhe; Huang Zhifeng

Purpose: A Fourier-based iterative reconstruction technique, termed Equally Sloped Tomography (EST), is developed in conjunction with advanced mathematical regularization to investigate radiation dose reduction in x-ray CT. The method is experimentally implemented on fan-beam CT and evaluated as a function of imaging dose on a series of image quality phantoms and anonymous pediatric patient data sets. Numerical simulation experiments are also performed to explore the extension of EST to helical cone-beam geometry. Methods: EST is a Fourier based iterative algorithm, which iterates back and forth between real and Fourier space utilizing the algebraically exact pseudopolar fast Fourier transform (PPFFT). Inmore » each iteration, physical constraints and mathematical regularization are applied in real space, while the measured data are enforced in Fourier space. The algorithm is automatically terminated when a proposed termination criterion is met. Experimentally, fan-beam projections were acquired by the Siemens z-flying focal spot technology, and subsequently interleaved and rebinned to a pseudopolar grid. Image quality phantoms were scanned at systematically varied mAs settings, reconstructed by EST and conventional reconstruction methods such as filtered back projection (FBP), and quantified using metrics including resolution, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs). Pediatric data sets were reconstructed at their original acquisition settings and additionally simulated to lower dose settings for comparison and evaluation of the potential for radiation dose reduction. Numerical experiments were conducted to quantify EST and other iterative methods in terms of image quality and computation time. The extension of EST to helical cone-beam CT was implemented by using the advanced single-slice rebinning (ASSR) method. Results: Based on the phantom and pediatric patient fan-beam CT data, it is demonstrated that EST reconstructions with the lowest scanner flux setting of 39 mAs produce comparable image quality, resolution, and contrast relative to FBP with the 140 mAs flux setting. Compared to the algebraic reconstruction technique and the expectation maximization statistical reconstruction algorithm, a significant reduction in computation time is achieved with EST. Finally, numerical experiments on helical cone-beam CT data suggest that the combination of EST and ASSR produces reconstructions with higher image quality and lower noise than the Feldkamp Davis and Kress (FDK) method and the conventional ASSR approach. Conclusions: A Fourier-based iterative method has been applied to the reconstruction of fan-bean CT data with reduced x-ray fluence. This method incorporates advantageous features in both real and Fourier space iterative schemes: using a fast and algebraically exact method to calculate forward projection, enforcing the measured data in Fourier space, and applying physical constraints and flexible regularization in real space. Our results suggest that EST can be utilized for radiation dose reduction in x-ray CT via the readily implementable technique of lowering mAs settings. Numerical experiments further indicate that EST requires less computation time than several other iterative algorithms and can, in principle, be extended to helical cone-beam geometry in combination with the ASSR method.« less

Radiation dose reduction in medical x-ray CT via Fourier-based iterative reconstruction.

PubMed

Fahimian, Benjamin P; Zhao, Yunzhe; Huang, Zhifeng; Fung, Russell; Mao, Yu; Zhu, Chun; Khatonabadi, Maryam; DeMarco, John J; Osher, Stanley J; McNitt-Gray, Michael F; Miao, Jianwei

2013-03-01

A Fourier-based iterative reconstruction technique, termed Equally Sloped Tomography (EST), is developed in conjunction with advanced mathematical regularization to investigate radiation dose reduction in x-ray CT. The method is experimentally implemented on fan-beam CT and evaluated as a function of imaging dose on a series of image quality phantoms and anonymous pediatric patient data sets. Numerical simulation experiments are also performed to explore the extension of EST to helical cone-beam geometry. EST is a Fourier based iterative algorithm, which iterates back and forth between real and Fourier space utilizing the algebraically exact pseudopolar fast Fourier transform (PPFFT). In each iteration, physical constraints and mathematical regularization are applied in real space, while the measured data are enforced in Fourier space. The algorithm is automatically terminated when a proposed termination criterion is met. Experimentally, fan-beam projections were acquired by the Siemens z-flying focal spot technology, and subsequently interleaved and rebinned to a pseudopolar grid. Image quality phantoms were scanned at systematically varied mAs settings, reconstructed by EST and conventional reconstruction methods such as filtered back projection (FBP), and quantified using metrics including resolution, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs). Pediatric data sets were reconstructed at their original acquisition settings and additionally simulated to lower dose settings for comparison and evaluation of the potential for radiation dose reduction. Numerical experiments were conducted to quantify EST and other iterative methods in terms of image quality and computation time. The extension of EST to helical cone-beam CT was implemented by using the advanced single-slice rebinning (ASSR) method. Based on the phantom and pediatric patient fan-beam CT data, it is demonstrated that EST reconstructions with the lowest scanner flux setting of 39 mAs produce comparable image quality, resolution, and contrast relative to FBP with the 140 mAs flux setting. Compared to the algebraic reconstruction technique and the expectation maximization statistical reconstruction algorithm, a significant reduction in computation time is achieved with EST. Finally, numerical experiments on helical cone-beam CT data suggest that the combination of EST and ASSR produces reconstructions with higher image quality and lower noise than the Feldkamp Davis and Kress (FDK) method and the conventional ASSR approach. A Fourier-based iterative method has been applied to the reconstruction of fan-bean CT data with reduced x-ray fluence. This method incorporates advantageous features in both real and Fourier space iterative schemes: using a fast and algebraically exact method to calculate forward projection, enforcing the measured data in Fourier space, and applying physical constraints and flexible regularization in real space. Our results suggest that EST can be utilized for radiation dose reduction in x-ray CT via the readily implementable technique of lowering mAs settings. Numerical experiments further indicate that EST requires less computation time than several other iterative algorithms and can, in principle, be extended to helical cone-beam geometry in combination with the ASSR method.

Radiation dose reduction in medical x-ray CT via Fourier-based iterative reconstruction

PubMed Central

Fahimian, Benjamin P.; Zhao, Yunzhe; Huang, Zhifeng; Fung, Russell; Mao, Yu; Zhu, Chun; Khatonabadi, Maryam; DeMarco, John J.; Osher, Stanley J.; McNitt-Gray, Michael F.; Miao, Jianwei

2013-01-01

Purpose: A Fourier-based iterative reconstruction technique, termed Equally Sloped Tomography (EST), is developed in conjunction with advanced mathematical regularization to investigate radiation dose reduction in x-ray CT. The method is experimentally implemented on fan-beam CT and evaluated as a function of imaging dose on a series of image quality phantoms and anonymous pediatric patient data sets. Numerical simulation experiments are also performed to explore the extension of EST to helical cone-beam geometry. Methods: EST is a Fourier based iterative algorithm, which iterates back and forth between real and Fourier space utilizing the algebraically exact pseudopolar fast Fourier transform (PPFFT). In each iteration, physical constraints and mathematical regularization are applied in real space, while the measured data are enforced in Fourier space. The algorithm is automatically terminated when a proposed termination criterion is met. Experimentally, fan-beam projections were acquired by the Siemens z-flying focal spot technology, and subsequently interleaved and rebinned to a pseudopolar grid. Image quality phantoms were scanned at systematically varied mAs settings, reconstructed by EST and conventional reconstruction methods such as filtered back projection (FBP), and quantified using metrics including resolution, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs). Pediatric data sets were reconstructed at their original acquisition settings and additionally simulated to lower dose settings for comparison and evaluation of the potential for radiation dose reduction. Numerical experiments were conducted to quantify EST and other iterative methods in terms of image quality and computation time. The extension of EST to helical cone-beam CT was implemented by using the advanced single-slice rebinning (ASSR) method. Results: Based on the phantom and pediatric patient fan-beam CT data, it is demonstrated that EST reconstructions with the lowest scanner flux setting of 39 mAs produce comparable image quality, resolution, and contrast relative to FBP with the 140 mAs flux setting. Compared to the algebraic reconstruction technique and the expectation maximization statistical reconstruction algorithm, a significant reduction in computation time is achieved with EST. Finally, numerical experiments on helical cone-beam CT data suggest that the combination of EST and ASSR produces reconstructions with higher image quality and lower noise than the Feldkamp Davis and Kress (FDK) method and the conventional ASSR approach. Conclusions: A Fourier-based iterative method has been applied to the reconstruction of fan-bean CT data with reduced x-ray fluence. This method incorporates advantageous features in both real and Fourier space iterative schemes: using a fast and algebraically exact method to calculate forward projection, enforcing the measured data in Fourier space, and applying physical constraints and flexible regularization in real space. Our results suggest that EST can be utilized for radiation dose reduction in x-ray CT via the readily implementable technique of lowering mAs settings. Numerical experiments further indicate that EST requires less computation time than several other iterative algorithms and can, in principle, be extended to helical cone-beam geometry in combination with the ASSR method. PMID:23464329

Study of image matching algorithm and sub-pixel fitting algorithm in target tracking

NASA Astrophysics Data System (ADS)

Yang, Ming-dong; Jia, Jianjun; Qiang, Jia; Wang, Jian-yu

2015-03-01

Image correlation matching is a tracking method that searched a region most approximate to the target template based on the correlation measure between two images. Because there is no need to segment the image, and the computation of this method is little. Image correlation matching is a basic method of target tracking. This paper mainly studies the image matching algorithm of gray scale image, which precision is at sub-pixel level. The matching algorithm used in this paper is SAD (Sum of Absolute Difference) method. This method excels in real-time systems because of its low computation complexity. The SAD method is introduced firstly and the most frequently used sub-pixel fitting algorithms are introduced at the meantime. These fitting algorithms can't be used in real-time systems because they are too complex. However, target tracking often requires high real-time performance, we put forward a fitting algorithm named paraboloidal fitting algorithm based on the consideration above, this algorithm is simple and realized easily in real-time system. The result of this algorithm is compared with that of surface fitting algorithm through image matching simulation. By comparison, the precision difference between these two algorithms is little, it's less than 0.01pixel. In order to research the influence of target rotation on precision of image matching, the experiment of camera rotation was carried on. The detector used in the camera is a CMOS detector. It is fixed to an arc pendulum table, take pictures when the camera rotated different angles. Choose a subarea in the original picture as the template, and search the best matching spot using image matching algorithm mentioned above. The result shows that the matching error is bigger when the target rotation angle is larger. It's an approximate linear relation. Finally, the influence of noise on matching precision was researched. Gaussian noise and pepper and salt noise were added in the image respectively, and the image was processed by mean filter and median filter, then image matching was processed. The result show that when the noise is little, mean filter and median filter can achieve a good result. But when the noise density of salt and pepper noise is bigger than 0.4, or the variance of Gaussian noise is bigger than 0.0015, the result of image matching will be wrong.

Propulsion Noise Reduction Research in the NASA Advanced Air Transport Technology Project

NASA Technical Reports Server (NTRS)

Van Zante, Dale; Nark, Douglas; Fernandez, Hamilton

2017-01-01

The Aircraft Noise Reduction (ANR) sub-project is focused on the generation, development, and testing of component noise reduction technologies progressing toward the NASA far term noise goals while providing associated near and mid-term benefits. The ANR sub-project has efforts in airframe noise reduction, propulsion (including fan and core) noise reduction, acoustic liner technology, and propulsion airframe aeroacoustics for candidate conventional and unconventional aircraft configurations. The current suite of propulsion specific noise research areas is reviewed along with emerging facility and measurement capabilities. In the longer term, the changes in engine and aircraft configuration will influence the suite of technologies necessary to reduce noise in next generation systems.

Maximum likelihood estimation of signal-to-noise ratio and combiner weight

NASA Technical Reports Server (NTRS)

Kalson, S.; Dolinar, S. J.

1986-01-01

An algorithm for estimating signal to noise ratio and combiner weight parameters for a discrete time series is presented. The algorithm is based upon the joint maximum likelihood estimate of the signal and noise power. The discrete-time series are the sufficient statistics obtained after matched filtering of a biphase modulated signal in additive white Gaussian noise, before maximum likelihood decoding is performed.

Evaluation of the Advanced Subsonic Technology Program Noise Reduction Benefits

NASA Technical Reports Server (NTRS)

Golub, Robert A.; Rawls, John W., Jr.; Russell, James W.

2005-01-01

This report presents a detailed evaluation of the aircraft noise reduction technology concepts developed during the course of the NASA/FAA Advanced Subsonic Technology (AST) Noise Reduction Program. In 1992, NASA and the FAA initiated a cosponsored, multi-year program with the U.S. aircraft industry focused on achieving significant advances in aircraft noise reduction. The program achieved success through a systematic development and validation of noise reduction technology. Using the NASA Aircraft Noise Prediction Program, the noise reduction benefit of the technologies that reached a NASA technology readiness level of 5 or 6 were applied to each of four classes of aircraft which included a large four engine aircraft, a large twin engine aircraft, a small twin engine aircraft and a business jet. Total aircraft noise reductions resulting from the implementation of the appropriate technologies for each class of aircraft are presented and compared to the AST program goals.

Evolutionary Fuzzy Block-Matching-Based Camera Raw Image Denoising.

PubMed

Yang, Chin-Chang; Guo, Shu-Mei; Tsai, Jason Sheng-Hong

2017-09-01

An evolutionary fuzzy block-matching-based image denoising algorithm is proposed to remove noise from a camera raw image. Recently, a variance stabilization transform is widely used to stabilize the noise variance, so that a Gaussian denoising algorithm can be used to remove the signal-dependent noise in camera sensors. However, in the stabilized domain, the existed denoising algorithm may blur too much detail. To provide a better estimate of the noise-free signal, a new block-matching approach is proposed to find similar blocks by the use of a type-2 fuzzy logic system (FLS). Then, these similar blocks are averaged with the weightings which are determined by the FLS. Finally, an efficient differential evolution is used to further improve the performance of the proposed denoising algorithm. The experimental results show that the proposed denoising algorithm effectively improves the performance of image denoising. Furthermore, the average performance of the proposed method is better than those of two state-of-the-art image denoising algorithms in subjective and objective measures.

EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal

NASA Astrophysics Data System (ADS)

Chen, Yong; Wu, Chun-ting; Liu, Huan-lin

2017-07-01

Noise may reduce the demodulation accuracy of fiber Bragg grating (FBG) sensing signal so as to affect the quality of sensing detection. Thus, the recovery of a signal from observed noisy data is necessary. In this paper, a precise self-adaptive algorithm of selecting relevant modes is proposed to remove the noise of signal. Empirical mode decomposition (EMD) is first used to decompose a signal into a set of modes. The pseudo modes cancellation is introduced to identify and eliminate false modes, and then the Mutual Information (MI) of partial modes is calculated. MI is used to estimate the critical point of high and low frequency components. Simulation results show that the proposed algorithm estimates the critical point more accurately than the traditional algorithms for FBG spectral signal. While, compared to the similar algorithms, the signal noise ratio of the signal can be improved more than 10 dB after processing by the proposed algorithm, and correlation coefficient can be increased by 0.5, so it demonstrates better de-noising effect.

A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

PubMed

Torija, Antonio J; Ruiz, Diego P

2015-02-01

The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

[A new peak detection algorithm of Raman spectra].

PubMed

Jiang, Cheng-Zhi; Sun, Qiang; Liu, Ying; Liang, Jing-Qiu; An, Yan; Liu, Bing

2014-01-01

The authors proposed a new Raman peak recognition method named bi-scale correlation algorithm. The algorithm uses the combination of the correlation coefficient and the local signal-to-noise ratio under two scales to achieve Raman peak identification. We compared the performance of the proposed algorithm with that of the traditional continuous wavelet transform method through MATLAB, and then tested the algorithm with real Raman spectra. The results show that the average time for identifying a Raman spectrum is 0.51 s with the algorithm, while it is 0.71 s with the continuous wavelet transform. When the signal-to-noise ratio of Raman peak is greater than or equal to 6 (modern Raman spectrometers feature an excellent signal-to-noise ratio), the recognition accuracy with the algorithm is higher than 99%, while it is less than 84% with the continuous wavelet transform method. The mean and the standard deviations of the peak position identification error of the algorithm are both less than that of the continuous wavelet transform method. Simulation analysis and experimental verification prove that the new algorithm possesses the following advantages: no needs of human intervention, no needs of de-noising and background removal operation, higher recognition speed and higher recognition accuracy. The proposed algorithm is operable in Raman peak identification.

An efficient multiple exposure image fusion in JPEG domain

NASA Astrophysics Data System (ADS)

Hebbalaguppe, Ramya; Kakarala, Ramakrishna

2012-01-01

In this paper, we describe a method to fuse multiple images taken with varying exposure times in the JPEG domain. The proposed algorithm finds its application in HDR image acquisition and image stabilization for hand-held devices like mobile phones, music players with cameras, digital cameras etc. Image acquisition at low light typically results in blurry and noisy images for hand-held camera's. Altering camera settings like ISO sensitivity, exposure times and aperture for low light image capture results in noise amplification, motion blur and reduction of depth-of-field respectively. The purpose of fusing multiple exposures is to combine the sharp details of the shorter exposure images with high signal-to-noise-ratio (SNR) of the longer exposure images. The algorithm requires only a single pass over all images, making it efficient. It comprises of - sigmoidal boosting of shorter exposed images, image fusion, artifact removal and saturation detection. Algorithm does not need more memory than a single JPEG macro block to be kept in memory making it feasible to be implemented as the part of a digital cameras hardware image processing engine. The Artifact removal step reuses the JPEGs built-in frequency analysis and hence benefits from the considerable optimization and design experience that is available for JPEG.

Cook-Levin Theorem Algorithmic-Reducibility/Completeness = Wilson Renormalization-(Semi)-Group Fixed-Points; ``Noise''-Induced Phase-Transitions (NITs) to Accelerate Algorithmics (``NIT-Picking'') REPLACING CRUTCHES!!!: Models: Turing-machine, finite-state-models, finite-automata

NASA Astrophysics Data System (ADS)

Young, Frederic; Siegel, Edward

Cook-Levin theorem theorem algorithmic computational-complexity(C-C) algorithmic-equivalence reducibility/completeness equivalence to renormalization-(semi)-group phase-transitions critical-phenomena statistical-physics universality-classes fixed-points, is exploited via Siegel FUZZYICS =CATEGORYICS = ANALOGYICS =PRAGMATYICS/CATEGORY-SEMANTICS ONTOLOGY COGNITION ANALYTICS-Aristotle ``square-of-opposition'' tabular list-format truth-table matrix analytics predicts and implements ''noise''-induced phase-transitions (NITs) to accelerate versus to decelerate Harel [Algorithmics (1987)]-Sipser[Intro.Thy. Computation(`97)] algorithmic C-C: ''NIT-picking''(!!!), to optimize optimization-problems optimally(OOPO). Versus iso-''noise'' power-spectrum quantitative-only amplitude/magnitude-only variation stochastic-resonance, ''NIT-picking'' is ''noise'' power-spectrum QUALitative-type variation via quantitative critical-exponents variation. Computer-''science''/SEANCE algorithmic C-C models: Turing-machine, finite-state-models, finite-automata,..., discrete-maths graph-theory equivalence to physics Feynman-diagrams are identified as early-days once-workable valid but limiting IMPEDING CRUTCHES(!!!), ONLY IMPEDE latter-days new-insights!!!

Technologies for Aircraft Noise Reduction

NASA Technical Reports Server (NTRS)

Huff, Dennis L.

2006-01-01

Technologies for aircraft noise reduction have been developed by NASA over the past 15 years through the Advanced Subsonic Technology (AST) Noise Reduction Program and the Quiet Aircraft Technology (QAT) project. This presentation summarizes highlights from these programs and anticipated noise reduction benefits for communities surrounding airports. Historical progress in noise reduction and technologies available for future aircraft/engine development are identified. Technologies address aircraft/engine components including fans, exhaust nozzles, landing gear, and flap systems. New "chevron" nozzles have been developed and implemented on several aircraft in production today that provide significant jet noise reduction. New engines using Ultra-High Bypass (UHB) ratios are projected to provide about 10 EPNdB (Effective Perceived Noise Level in decibels) engine noise reduction relative to the average fleet that was flying in 1997. Audio files are embedded in the presentation that estimate the sound levels for a 35,000 pound thrust engine for takeoff and approach power conditions. The predictions are based on actual model scale data that was obtained by NASA. Finally, conceptual pictures are shown that look toward future aircraft/propulsion systems that might be used to obtain further noise reduction.

Noise and Fuel Burn Reduction Potential of an Innovative Subsonic Transport Configuration

NASA Technical Reports Server (NTRS)

Guo, Yueping; Nickol, Craig L.; Thomas, Russell H.

2014-01-01

A study is presented for the noise and fuel burn reduction potential of an innovative double deck concept aircraft with two three-shaft direct-drive turbofan engines. The engines are mounted from the fuselage so that the engine inlet is over the main wing. It is shown that such an aircraft can achieve a cumulative Effective Perceived Noise Level (EPNL) about 28 dB below the current aircraft noise regulations of Stage 4. The combination of high bypass ratio engines and advanced wing design with laminar flow control technologies provide fuel burn reduction and low noise levels simultaneously. For example, the fuselage mounted engine position provides more than 4 EPNLdB of noise reduction by shielding the inlet radiated noise. To identify the potential effect of noise reduction technologies on this concept, parametric studies are presented to reveal the system level benefits of various emerging noise reduction concepts, for both engine and airframe noise reduction. These concepts are discussed both individually to show their respective incremental noise reduction potential and collectively to assess their aggregate effects on the total noise. Through these concepts approximately about 8 dB of additional noise reduction is possible, bringing the cumulative noise level of this aircraft to 36 EPNLdB below Stage 4, if the entire suite of noise reduction technologies would mature to practical application. In a final step, an estimate is made for this same aircraft concept but with higher bypass ratio, geared, turbofan engines. With this geared turbofan propulsion system, the noise is estimated to reach as low as 40-42 dB below Stage 4 with a fuel burn reduction of 43-47% below the 2005 best-in-class aircraft baseline. While just short of the NASA N+2 goals of 42 dB and 50% fuel burn reduction, for a 2025 in service timeframe, this assessment shows that this innovative concept warrants refined study. Furthermore, this design appears to be a viable potential future passenger aircraft, not only in meeting the regulatory requirements, but also in competing with aircraft of different advanced designs within this N+2 timeframe and goal framework.

Effects of venting on wind noise levels measured at the eardrum.

PubMed

Chung, King

2013-01-01

Wind noise can be a nuisance to hearing aid users. With the advent of sophisticated feedback reduction algorithms, people with higher degrees of hearing loss are fit with larger vents than previously allowed, and more people with lesser degrees of hearing loss are fit with open hearing aids. The purpose of this study was to examine the effects of venting on wind noise levels in the ear canal for hearing aids with omnidirectional and directional microphones. Two behind-the-ear hearing aids were programmed when they were worn on a Knowles Electronics Manikin for Acoustic Research. The hearing aid worn on the right ear was programmed to the omnidirectional microphone mode and the one on the left to the directional microphone mode. The hearing aids were adjusted to linear amplification with flat frequency response in an anechoic chamber. Gains below 10 dB were used to avoid output limiting of wind noise levels at low input levels. Wind noise samples were recorded at the eardrum location in a wind tunnel at wind velocities ranging from a gentle to a strong breeze. The hearing aids were coupled to #13 tubings (i.e., open vent), or conventional skeleton earmolds with no vent, pressure vents, or 3mm vents. Polar and spectral characteristics of wind noise were analyzed off-line using MatLab programs. Wind noise levels in the ear canals were mostly predicted by vent-induced frequency response changes in the conventional earmold conditions for both omnidirectional and directional hearing aids. The open vent condition, however, yielded the lowest levels, which could not be entirely predicted by the frequency response changes of the hearing aids. This indicated that a wind-related vent effect permitted an additional amount of sound reduction in the ear canal, which could not be explained by known vent effects. For the microphone location, form factor, and gain settings tested, open fit hearing aids yielded lower noise levels at the eardrum location than conventional behind-the-ear hearing aids.

NASA's Subsonic Jet Transport Noise Reduction Research

NASA Technical Reports Server (NTRS)

Powell, Clemans A.; Preisser, John S.

2000-01-01

Although new jet transport airplanes in today s fleet are considerably quieter than the first jet transports introduced about 40 years ago, airport community noise continues to be an important environmental issue. NASA s Advanced Subsonic Transport (AST) Noise Reduction program was begun in 1994 as a seven-year effort to develop technology to reduce jet transport noise 10 dB relative to 1992 technology. This program provides for reductions in engine source noise, improvements in nacelle acoustic treatments, reductions in the noise generated by the airframe, and improvements in the way airplanes are operated in the airport environs. These noise reduction efforts will terminate at the end of 2001 and it appears that the objective will be met. However, because of an anticipated 3-8% growth in passenger and cargo operations well into the 21st Century and the slow introduction of new the noise reduction technology into the fleet, world aircraft noise impact will remain essentially constant until about 2020 to 2030 and thereafter begin to rise. Therefore NASA has begun planning with the Federal Aviation Administration, industry, universities and environmental interest groups in the USA for a new noise reduction initiative to provide technology for significant further reductions.

Small-Noise Analysis and Symmetrization of Implicit Monte Carlo Samplers

DOE PAGES

Goodman, Jonathan; Lin, Kevin K.; Morzfeld, Matthias

2015-07-06

Implicit samplers are algorithms for producing independent, weighted samples from multivariate probability distributions. These are often applied in Bayesian data assimilation algorithms. We use Laplace asymptotic expansions to analyze two implicit samplers in the small noise regime. Our analysis suggests a symmetrization of the algorithms that leads to improved implicit sampling schemes at a relatively small additional cost. Here, computational experiments confirm the theory and show that symmetrization is effective for small noise sampling problems.

Improvement of Frequency Locking Algorithm for Atomic Frequency Standards

NASA Astrophysics Data System (ADS)

Park, Young-Ho; Kang, Hoonsoo; Heyong Lee, Soo; Eon Park, Sang; Lee, Jong Koo; Lee, Ho Seong; Kwon, Taeg Yong

2010-09-01

The authors describe a novel method of frequency locking algorithm for atomic frequency standards. The new algorithm for locking the microwave frequency to the Ramsey resonance is compared with the old one that had been employed in the cesium atomic beam frequency standards such as NIST-7 and KRISS-1. Numerical simulations for testing the performance of the algorithm show that the new method has a noise filtering performance superior to the old one by a factor of 1.2 for the flicker signal noise and 1.4 for random-walk signal noise. The new algorithm can readily be used to enhance the frequency stability for a digital servo employing the slow square wave frequency modulation.

Identification of noise artifacts in searches for long-duration gravitational-wave transients

NASA Astrophysics Data System (ADS)

Prestegard, Tanner; Thrane, Eric; Christensen, Nelson L.; Coughlin, Michael W.; Hubbert, Ben; Kandhasamy, Shivaraj; MacAyeal, Evan; Mandic, Vuk

2012-05-01

We present an algorithm for the identification of transient noise artifacts (glitches) in cross-correlation searches for long gravitational-wave (GW) transients lasting seconds to weeks. The algorithm utilizes the auto-power in each detector as a discriminator between well-behaved stationary noise (possibly including a GW signal) and non-stationary noise transients. We test the algorithm with both Monte Carlo noise and time-shifted data from the LIGO S5 science run and find that it removes a significant fraction of glitches while keeping the vast majority (99.6%) of the data. We show that this cleaned data can be used to observe GW signals at a significantly lower amplitude than can otherwise be achieved. Using an accretion disk instability signal model, we estimate that the algorithm is accidentally triggered at a rate of less than 10-5% by realistic signals, and less than 3% even for exceptionally loud signals. We conclude that the algorithm is a safe and effective method for cleaning the cross-correlation data used in searches for long GW transients.

Robust optical flow using adaptive Lorentzian filter for image reconstruction under noisy condition

NASA Astrophysics Data System (ADS)

Kesrarat, Darun; Patanavijit, Vorapoj

2017-02-01

In optical flow for motion allocation, the efficient result in Motion Vector (MV) is an important issue. Several noisy conditions may cause the unreliable result in optical flow algorithms. We discover that many classical optical flows algorithms perform better result under noisy condition when combined with modern optimized model. This paper introduces effective robust models of optical flow by using Robust high reliability spatial based optical flow algorithms using the adaptive Lorentzian norm influence function in computation on simple spatial temporal optical flows algorithm. Experiment on our proposed models confirm better noise tolerance in optical flow's MV under noisy condition when they are applied over simple spatial temporal optical flow algorithms as a filtering model in simple frame-to-frame correlation technique. We illustrate the performance of our models by performing an experiment on several typical sequences with differences in movement speed of foreground and background where the experiment sequences are contaminated by the additive white Gaussian noise (AWGN) at different noise decibels (dB). This paper shows very high effectiveness of noise tolerance models that they are indicated by peak signal to noise ratio (PSNR).

Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI.

PubMed

Hu, Changwei; Qu, Xiaobo; Guo, Di; Bao, Lijun; Chen, Zhong

2011-09-01

Undersampling k-space is an effective way to decrease acquisition time for MRI. However, aliasing artifacts introduced by undersampling may blur the edges of magnetic resonance images, which often contain important information for clinical diagnosis. Moreover, k-space data is often contaminated by the noise signals of unknown intensity. To better preserve the edge features while suppressing the aliasing artifacts and noises, we present a new wavelet-based algorithm for undersampled MRI reconstruction. The algorithm solves the image reconstruction as a standard optimization problem including a ℓ(2) data fidelity term and ℓ(1) sparsity regularization term. Rather than manually setting the regularization parameter for the ℓ(1) term, which is directly related to the threshold, an automatic estimated threshold adaptive to noise intensity is introduced in our proposed algorithm. In addition, a prior matrix based on edge correlation in wavelet domain is incorporated into the regularization term. Compared with nonlinear conjugate gradient descent algorithm, iterative shrinkage/thresholding algorithm, fast iterative soft-thresholding algorithm and the iterative thresholding algorithm using exponentially decreasing threshold, the proposed algorithm yields reconstructions with better edge recovery and noise suppression. Copyright © 2011 Elsevier Inc. All rights reserved.

A Bayesian nonparametric approach to dynamical noise reduction

NASA Astrophysics Data System (ADS)

Kaloudis, Konstantinos; Hatjispyros, Spyridon J.

2018-06-01

We propose a Bayesian nonparametric approach for the noise reduction of a given chaotic time series contaminated by dynamical noise, based on Markov Chain Monte Carlo methods. The underlying unknown noise process (possibly) exhibits heavy tailed behavior. We introduce the Dynamic Noise Reduction Replicator model with which we reconstruct the unknown dynamic equations and in parallel we replicate the dynamics under reduced noise level dynamical perturbations. The dynamic noise reduction procedure is demonstrated specifically in the case of polynomial maps. Simulations based on synthetic time series are presented.

Computed tomography imaging with the Adaptive Statistical Iterative Reconstruction (ASIR) algorithm: dependence of image quality on the blending level of reconstruction.

PubMed

Barca, Patrizio; Giannelli, Marco; Fantacci, Maria Evelina; Caramella, Davide

2018-06-01

Computed tomography (CT) is a useful and widely employed imaging technique, which represents the largest source of population exposure to ionizing radiation in industrialized countries. Adaptive Statistical Iterative Reconstruction (ASIR) is an iterative reconstruction algorithm with the potential to allow reduction of radiation exposure while preserving diagnostic information. The aim of this phantom study was to assess the performance of ASIR, in terms of a number of image quality indices, when different reconstruction blending levels are employed. CT images of the Catphan-504 phantom were reconstructed using conventional filtered back-projection (FBP) and ASIR with reconstruction blending levels of 20, 40, 60, 80, and 100%. Noise, noise power spectrum (NPS), contrast-to-noise ratio (CNR) and modulation transfer function (MTF) were estimated for different scanning parameters and contrast objects. Noise decreased and CNR increased non-linearly up to 50 and 100%, respectively, with increasing blending level of reconstruction. Also, ASIR has proven to modify the NPS curve shape. The MTF of ASIR reconstructed images depended on tube load/contrast and decreased with increasing blending level of reconstruction. In particular, for low radiation exposure and low contrast acquisitions, ASIR showed lower performance than FBP, in terms of spatial resolution for all blending levels of reconstruction. CT image quality varies substantially with the blending level of reconstruction. ASIR has the potential to reduce noise whilst maintaining diagnostic information in low radiation exposure CT imaging. Given the opposite variation of CNR and spatial resolution with the blending level of reconstruction, it is recommended to use an optimal value of this parameter for each specific clinical application.

Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image.

PubMed

Kumar, M; Mishra, S K

2017-01-01

The clinical magnetic resonance imaging (MRI) images may get corrupted due to the presence of the mixture of different types of noises such as Rician, Gaussian, impulse, etc. Most of the available filtering algorithms are noise specific, linear, and non-adaptive. There is a need to develop a nonlinear adaptive filter that adapts itself according to the requirement and effectively applied for suppression of mixed noise from different MRI images. In view of this, a novel nonlinear neural network based adaptive filter i.e. functional link artificial neural network (FLANN) whose weights are trained by a recently developed derivative free meta-heuristic technique i.e. teaching learning based optimization (TLBO) is proposed and implemented. The performance of the proposed filter is compared with five other adaptive filters and analyzed by considering quantitative metrics and evaluating the nonparametric statistical test. The convergence curve and computational time are also included for investigating the efficiency of the proposed as well as competitive filters. The simulation outcomes of proposed filter outperform the other adaptive filters. The proposed filter can be hybridized with other evolutionary technique and utilized for removing different noise and artifacts from others medical images more competently.

ERP denoising in multichannel EEG data using contrasts between signal and noise subspaces.

PubMed

Ivannikov, Andriy; Kalyakin, Igor; Hämäläinen, Jarmo; Leppänen, Paavo H T; Ristaniemi, Tapani; Lyytinen, Heikki; Kärkkäinen, Tommi

2009-06-15

In this paper, a new method intended for ERP denoising in multichannel EEG data is discussed. The denoising is done by separating ERP/noise subspaces in multidimensional EEG data by a linear transformation and the following dimension reduction by ignoring noise components during inverse transformation. The separation matrix is found based on the assumption that ERP sources are deterministic for all repetitions of the same type of stimulus within the experiment, while the other noise sources do not obey the determinancy property. A detailed derivation of the technique is given together with the analysis of the results of its application to a real high-density EEG data set. The interpretation of the results and the performance of the proposed method under conditions, when the basic assumptions are violated - e.g. the problem is underdetermined - are also discussed. Moreover, we study how the factors of the number of channels and trials used by the method influence the effectiveness of ERP/noise subspaces separation. In addition, we explore also the impact of different data resampling strategies on the performance of the considered algorithm. The results can help in determining the optimal parameters of the equipment/methods used to elicit and reliably estimate ERPs.

On the signal-to-noise ratio in IUE high-dispersion spectra

NASA Technical Reports Server (NTRS)

Leckrone, David S.; Adelman, Saul J.

1989-01-01

An observational and data reduction technique for fixed pattern noise (FPN) and random noise (RN) in fully extracted IUE high-dispersion spectra is described in detail, along with actual empirical values of signal-to-noise ratio (S/N) achieved. A co-addition procedure, involving SWP and LWR cameras observations of the same spectrum at different positions in the image format, provides a basis to disentangle FPN from RN, allowing each average amplitude, within a given wavelength interval, to be estimated as a function of average flux number. Empirical curves, derived with the noise algorithm, make it possible to estimate the S/N in individual spectra at the wavelengths investigated. The average S/N at the continuum level in well-exposed stellar spectra varies from 10 to 20, for the orders analyzed, depending on position in the spectral format. The co-addition procedure yields an improvement in S/N by factors ranging from 2.3 to 2.9. Direct measurements of S/N in narrow, line-free wavelength intervals of individual and co-added spectra for weak-lined stars yield comparable, or in some cases somewhat higher, S/N values and improvement factors.

DIAL with heterodyne detection including speckle noise: Aircraft/shuttle measurements of O3, H2O, and NH3 with pulsed tunable CO2 lasers

NASA Technical Reports Server (NTRS)

Brockman, P.; Hess, R. V.; Staton, L. D.; Bair, C. H.

1980-01-01

Atmospheric trace constituent measurements with higher vertical resolution than attainable with passive radiometers are discussed. Infrared differential absorption lidar (DIAL), which depends on Mie scattering from aerosols, has special advantages for tropospheric and lower stratospheric applications and has great potential importance for measurements from shuttle and aircraft. Differential absorption lidar data reduction involves comparing large amplitude signals which have small differences. The accuracy of the trace constituent concentration inferred from DIAL measurements depends strongly on the errors in determining the amplitude of the signals. Thus, the commonly used SNR expression (signal divided by noise in the absence of signal) is not adequate to describe DIAL measurement accuracy and must be replaced by an expression which includes the random coherent (speckle) noise within the signal. A comprehensive DIAL computer algorithm is modified to include heterodyne detection and speckle noise. Examples for monitoring vertical distributions of O3, H2O, and NH3 using a ground-, aircraft-, or shuttle-based pulsed tunable CO2 laser DIAL system are given.

Performance appraisal of VAS radiometry for GOES-4, -5 and -6

NASA Technical Reports Server (NTRS)

Chesters, D.; Robinson, W. D.

1983-01-01

The first three VISSR Atmospheric Sounders (VAS) were launched on GOES-4, -5, and -6 in 1980, 1981 and 1983. Postlaunch radiometric performance is assessed for noise, biases, registration and reliability, with special attention to calibration and problems in the data processing chain. The postlaunch performance of the VAS radiometer meets its prelaunch design specifications, particularly those related to image formation and noise reduction. The best instrument is carried on GOES-5, currently operational as GOES-EAST. Single sample noise is lower than expected, especially for the small longwave and large shortwave detectors. Detector to detector offsets are correctable to within the resolution limits of the instrument. Truncation, zero point and droop errors are insignificant. Absolute calibration errors, estimated from HIRS and from radiation transfer calculations, indicate moderate, but stable biases. Relative calibration errors from scanline to scanline are noticeable, but meet sounding requirements for temporarily and spatially averaged sounding fields of view. The VAS instrument is a potentially useful radiometer for mesoscale sounding operations. Image quality is very good. Soundings derived from quality controlled data meet prelaunch requirements when calculated with noise and bias resistant algorithms.

a Universal De-Noising Algorithm for Ground-Based LIDAR Signal

NASA Astrophysics Data System (ADS)

Ma, Xin; Xiang, Chengzhi; Gong, Wei

2016-06-01

Ground-based lidar, working as an effective remote sensing tool, plays an irreplaceable role in the study of atmosphere, since it has the ability to provide the atmospheric vertical profile. However, the appearance of noise in a lidar signal is unavoidable, which leads to difficulties and complexities when searching for more information. Every de-noising method has its own characteristic but with a certain limitation, since the lidar signal will vary with the atmosphere changes. In this paper, a universal de-noising algorithm is proposed to enhance the SNR of a ground-based lidar signal, which is based on signal segmentation and reconstruction. The signal segmentation serving as the keystone of the algorithm, segments the lidar signal into three different parts, which are processed by different de-noising method according to their own characteristics. The signal reconstruction is a relatively simple procedure that is to splice the signal sections end to end. Finally, a series of simulation signal tests and real dual field-of-view lidar signal shows the feasibility of the universal de-noising algorithm.

A qualitative and quantitative analysis of radiation dose and image quality of computed tomography images using adaptive statistical iterative reconstruction.

PubMed

Hussain, Fahad Ahmed; Mail, Noor; Shamy, Abdulrahman M; Suliman, Alghamdi; Saoudi, Abdelhamid

2016-05-08

Image quality is a key issue in radiology, particularly in a clinical setting where it is important to achieve accurate diagnoses while minimizing radiation dose. Some computed tomography (CT) manufacturers have introduced algorithms that claim significant dose reduction. In this study, we assessed CT image quality produced by two reconstruction algorithms provided with GE Healthcare's Discovery 690 Elite positron emission tomography (PET) CT scanner. Image quality was measured for images obtained at various doses with both conventional filtered back-projection (FBP) and adaptive statistical iterative reconstruction (ASIR) algorithms. A stan-dard CT dose index (CTDI) phantom and a pencil ionization chamber were used to measure the CT dose at 120 kVp and an exposure of 260 mAs. Image quality was assessed using two phantoms. CT images of both phantoms were acquired at tube voltage (kV) of 120 with exposures ranging from 25 mAs to 400 mAs. Images were reconstructed using FBP and ASIR ranging from 10% to 100%, then analyzed for noise, low-contrast detectability, contrast-to-noise ratio (CNR), and modulation transfer function (MTF). Noise was 4.6 HU in water phantom images acquired at 260 mAs/FBP 120 kV and 130 mAs/50% ASIR 120 kV. The large objects (fre-quency < 7 lp/cm) retained fairly acceptable image quality at 130 mAs/50% ASIR, compared to 260 mAs/FBP. The application of ASIR for small objects (frequency >7 lp/cm) showed poor visibility compared to FBP at 260 mAs and even worse for images acquired at less than 130 mAs. ASIR blending more than 50% at low dose tends to reduce contrast of small objects (frequency >7 lp/cm). We concluded that dose reduction and ASIR should be applied with close attention if the objects to be detected or diagnosed are small (frequency > 7 lp/cm). Further investigations are required to correlate the small objects (frequency > 7 lp/cm) to patient anatomy and clinical diagnosis.

40 CFR 211.207 - Computation of the noise -reduction rating (NRR).

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 25 2011-07-01 2011-07-01 false Computation of the noise -reduction... (CONTINUED) NOISE ABATEMENT PROGRAMS PRODUCT NOISE LABELING Hearing Protective Devices § 211.207 Computation of the noise -reduction rating (NRR). Calculate the NRR for hearing protective devices by...

Fan Noise Reduction: An Overview

NASA Technical Reports Server (NTRS)

Envia, Edmane

2001-01-01

Fan noise reduction technologies developed as part of the engine noise reduction element of the Advanced Subsonic Technology Program are reviewed. Developments in low-noise fan stage design, swept and leaned outlet guide vanes, active noise control, fan flow management, and scarfed inlet are discussed. In each case, a description of the method is presented and, where available, representative results and general conclusions are discussed. The review concludes with a summary of the accomplishments of the AST-sponsored fan noise reduction research and a few thoughts on future work.

Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis.

PubMed

Jeong, Ji-Wook; Chae, Seung-Hoon; Chae, Eun Young; Kim, Hak Hee; Choi, Young-Wook; Lee, Sooyeul

2016-01-01

We propose computer-aided detection (CADe) algorithm for microcalcification (MC) clusters in reconstructed digital breast tomosynthesis (DBT) images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP) reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D) objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR) enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.

Intelligent estimation of noise and blur variances using ANN for the restoration of ultrasound images.

PubMed

Uddin, Muhammad Shahin; Halder, Kalyan Kumar; Tahtali, Murat; Lambert, Andrew J; Pickering, Mark R; Marchese, Margaret; Stuart, Iain

2016-11-01

Ultrasound (US) imaging is a widely used clinical diagnostic tool in medical imaging techniques. It is a comparatively safe, economical, painless, portable, and noninvasive real-time tool compared to the other imaging modalities. However, the image quality of US imaging is severely affected by the presence of speckle noise and blur during the acquisition process. In order to ensure a high-quality clinical diagnosis, US images must be restored by reducing their speckle noise and blur. In general, speckle noise is modeled as a multiplicative noise following a Rayleigh distribution and blur as a Gaussian function. Hereto, we propose an intelligent estimator based on artificial neural networks (ANNs) to estimate the variances of noise and blur, which, in turn, are used to obtain an image without discernible distortions. A set of statistical features computed from the image and its complex wavelet sub-bands are used as input to the ANN. In the proposed method, we solve the inverse Rayleigh function numerically for speckle reduction and use the Richardson-Lucy algorithm for de-blurring. The performance of this method is compared with that of the traditional methods by applying them to a synthetic, physical phantom and clinical data, which confirms better restoration results by the proposed method.

Noise adaptive wavelet thresholding for speckle noise removal in optical coherence tomography.

PubMed

Zaki, Farzana; Wang, Yahui; Su, Hao; Yuan, Xin; Liu, Xuan

2017-05-01

Optical coherence tomography (OCT) is based on coherence detection of interferometric signals and hence inevitably suffers from speckle noise. To remove speckle noise in OCT images, wavelet domain thresholding has demonstrated significant advantages in suppressing noise magnitude while preserving image sharpness. However, speckle noise in OCT images has different characteristics in different spatial scales, which has not been considered in previous applications of wavelet domain thresholding. In this study, we demonstrate a noise adaptive wavelet thresholding (NAWT) algorithm that exploits the difference of noise characteristics in different wavelet sub-bands. The algorithm is simple, fast, effective and is closely related to the physical origin of speckle noise in OCT image. Our results demonstrate that NAWT outperforms conventional wavelet thresholding.

Two-Microphone Spatial Filtering Improves Speech Reception for Cochlear-Implant Users in Reverberant Conditions With Multiple Noise Sources

PubMed Central

2014-01-01

This study evaluates a spatial-filtering algorithm as a method to improve speech reception for cochlear-implant (CI) users in reverberant environments with multiple noise sources. The algorithm was designed to filter sounds using phase differences between two microphones situated 1 cm apart in a behind-the-ear hearing-aid capsule. Speech reception thresholds (SRTs) were measured using a Coordinate Response Measure for six CI users in 27 listening conditions including each combination of reverberation level (T60 = 0, 270, and 540 ms), number of noise sources (1, 4, and 11), and signal-processing algorithm (omnidirectional response, dipole-directional response, and spatial-filtering algorithm). Noise sources were time-reversed speech segments randomly drawn from the Institute of Electrical and Electronics Engineers sentence recordings. Target speech and noise sources were processed using a room simulation method allowing precise control over reverberation times and sound-source locations. The spatial-filtering algorithm was found to provide improvements in SRTs on the order of 6.5 to 11.0 dB across listening conditions compared with the omnidirectional response. This result indicates that such phase-based spatial filtering can improve speech reception for CI users even in highly reverberant conditions with multiple noise sources. PMID:25330772

Photoplethysmograph signal reconstruction based on a novel hybrid motion artifact detection-reduction approach. Part I: Motion and noise artifact detection.

PubMed

Chong, Jo Woon; Dao, Duy K; Salehizadeh, S M A; McManus, David D; Darling, Chad E; Chon, Ki H; Mendelson, Yitzhak

2014-11-01

Motion and noise artifacts (MNA) are a serious obstacle in utilizing photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a MNA detection method which can provide a clean vs. corrupted decision on each successive PPG segment. For motion artifact detection, we compute four time-domain parameters: (1) standard deviation of peak-to-peak intervals (2) standard deviation of peak-to-peak amplitudes (3) standard deviation of systolic and diastolic interval ratios, and (4) mean standard deviation of pulse shape. We have adopted a support vector machine (SVM) which takes these parameters from clean and corrupted PPG signals and builds a decision boundary to classify them. We apply several distinct features of the PPG data to enhance classification performance. The algorithm we developed was verified on PPG data segments recorded by simulation, laboratory-controlled and walking/stair-climbing experiments, respectively, and we compared several well-established MNA detection methods to our proposed algorithm. All compared detection algorithms were evaluated in terms of motion artifact detection accuracy, heart rate (HR) error, and oxygen saturation (SpO2) error. For laboratory controlled finger, forehead recorded PPG data and daily-activity movement data, our proposed algorithm gives 94.4, 93.4, and 93.7% accuracies, respectively. Significant reductions in HR and SpO2 errors (2.3 bpm and 2.7%) were noted when the artifacts that were identified by SVM-MNA were removed from the original signal than without (17.3 bpm and 5.4%). The accuracy and error values of our proposed method were significantly higher and lower, respectively, than all other detection methods. Another advantage of our method is its ability to provide highly accurate onset and offset detection times of MNAs. This capability is important for an automated approach to signal reconstruction of only those data points that need to be reconstructed, which is the subject of the companion paper to this article. Finally, our MNA detection algorithm is real-time realizable as the computational speed on the 7-s PPG data segment was found to be only 7 ms with a Matlab code.

Applications of fractional lower order S transform time frequency filtering algorithm to machine fault diagnosis

PubMed Central

Wang, Haibin; Zha, Daifeng; Li, Peng; Xie, Huicheng; Mao, Lili

2017-01-01

Stockwell transform(ST) time-frequency representation(ST-TFR) is a time frequency analysis method which combines short time Fourier transform with wavelet transform, and ST time frequency filtering(ST-TFF) method which takes advantage of time-frequency localized spectra can separate the signals from Gaussian noise. The ST-TFR and ST-TFF methods are used to analyze the fault signals, which is reasonable and effective in general Gaussian noise cases. However, it is proved that the mechanical bearing fault signal belongs to Alpha(α) stable distribution process(1 < α < 2) in this paper, even the noise also is α stable distribution in some special cases. The performance of ST-TFR method will degrade under α stable distribution noise environment, following the ST-TFF method fail. Hence, a new fractional lower order ST time frequency representation(FLOST-TFR) method employing fractional lower order moment and ST and inverse FLOST(IFLOST) are proposed in this paper. A new FLOST time frequency filtering(FLOST-TFF) algorithm based on FLOST-TFR method and IFLOST is also proposed, whose simplified method is presented in this paper. The discrete implementation of FLOST-TFF algorithm is deduced, and relevant steps are summarized. Simulation results demonstrate that FLOST-TFR algorithm is obviously better than the existing ST-TFR algorithm under α stable distribution noise, which can work better under Gaussian noise environment, and is robust. The FLOST-TFF method can effectively filter out α stable distribution noise, and restore the original signal. The performance of FLOST-TFF algorithm is better than the ST-TFF method, employing which mixed MSEs are smaller when α and generalized signal noise ratio(GSNR) change. Finally, the FLOST-TFR and FLOST-TFF methods are applied to analyze the outer race fault signal and extract their fault features under α stable distribution noise, where excellent performances can be shown. PMID:28406916

Image denoising based on noise detection

NASA Astrophysics Data System (ADS)

Jiang, Yuanxiang; Yuan, Rui; Sun, Yuqiu; Tian, Jinwen

2018-03-01

Because of the noise points in the images, any operation of denoising would change the original information of non-noise pixel. A noise detection algorithm based on fractional calculus was proposed to denoise in this paper. Convolution of the image was made to gain direction gradient masks firstly. Then, the mean gray was calculated to obtain the gradient detection maps. Logical product was made to acquire noise position image next. Comparisons in the visual effect and evaluation parameters after processing, the results of experiment showed that the denoising algorithms based on noise were better than that of traditional methods in both subjective and objective aspects.

An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images.

PubMed

Liu, Xiao; Shi, Jun; Zhou, Shichong; Lu, Minhua

2014-01-01

The dimensionality reduction is an important step in ultrasound image based computer-aided diagnosis (CAD) for breast cancer. A newly proposed l2,1 regularized correntropy algorithm for robust feature selection (CRFS) has achieved good performance for noise corrupted data. Therefore, it has the potential to reduce the dimensions of ultrasound image features. However, in clinical practice, the collection of labeled instances is usually expensive and time costing, while it is relatively easy to acquire the unlabeled or undetermined instances. Therefore, the semi-supervised learning is very suitable for clinical CAD. The iterated Laplacian regularization (Iter-LR) is a new regularization method, which has been proved to outperform the traditional graph Laplacian regularization in semi-supervised classification and ranking. In this study, to augment the classification accuracy of the breast ultrasound CAD based on texture feature, we propose an Iter-LR-based semi-supervised CRFS (Iter-LR-CRFS) algorithm, and then apply it to reduce the feature dimensions of ultrasound images for breast CAD. We compared the Iter-LR-CRFS with LR-CRFS, original supervised CRFS, and principal component analysis. The experimental results indicate that the proposed Iter-LR-CRFS significantly outperforms all other algorithms.

Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue

NASA Astrophysics Data System (ADS)

Sawyer, Travis W.; Rice, Photini F. S.; Sawyer, David M.; Koevary, Jennifer W.; Barton, Jennifer K.

2018-02-01

Ovarian cancer has the lowest survival rate among all gynecologic cancers due to predominantly late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Optical coherence tomography (OCT) is an emerging technique that provides depthresolved, high-resolution images of biological tissue in real time and demonstrates great potential for imaging of ovarian tissue. Mouse models are crucial to quantitatively assess the diagnostic potential of OCT for ovarian cancer imaging; however, due to small organ size, the ovaries must rst be separated from the image background using the process of segmentation. Manual segmentation is time-intensive, as OCT yields three-dimensional data. Furthermore, speckle noise complicates OCT images, frustrating many processing techniques. While much work has investigated noise-reduction and automated segmentation for retinal OCT imaging, little has considered the application to the ovaries, which exhibit higher variance and inhomogeneity than the retina. To address these challenges, we evaluated a set of algorithms to segment OCT images of mouse ovaries. We examined ve preprocessing techniques and six segmentation algorithms. While all pre-processing methods improve segmentation, Gaussian filtering is most effective, showing an improvement of 32% +/- 1.2%. Of the segmentation algorithms, active contours performs best, segmenting with an accuracy of 0.948 +/- 0.012 compared with manual segmentation (1.0 being identical). Nonetheless, further optimization could lead to maximizing the performance for segmenting OCT images of the ovaries.

An assessment of propeller aircraft noise reduction technology

NASA Technical Reports Server (NTRS)

Metzger, F. Bruce

1995-01-01

This report is a review of the literature regarding propeller airplane far-field noise reduction. Near-field and cabin noise reduction are not specifically addressed. However, some of the approaches used to reduce far-field noise produce beneficial effects in the near-field and in the cabin. The emphasis is on propeller noise reduction but engine exhaust noise reduction by muffling is also addressed since the engine noise becomes a significant part of the aircraft noise signature when propeller noise is reduced. It is concluded that there is a substantial body of information available that can be used as the basis to reduce propeller airplane noise. The reason that this information is not often used in airplane design is the associated weight, cost, and performance penalties. It is recommended that the highest priority be given to research for reducing the penalties associated with lower operating RPM and propeller diameter while increasing the number of blades. Research to reduce engine noise and explore innovative propeller concepts is also recommended.

Optimal signal constellation design for ultra-high-speed optical transport in the presence of nonlinear phase noise.

PubMed

Liu, Tao; Djordjevic, Ivan B

2014-12-29

In this paper, we first describe an optimal signal constellation design algorithm suitable for the coherent optical channels dominated by the linear phase noise. Then, we modify this algorithm to be suitable for the nonlinear phase noise dominated channels. In optimization procedure, the proposed algorithm uses the cumulative log-likelihood function instead of the Euclidian distance. Further, an LDPC coded modulation scheme is proposed to be used in combination with signal constellations obtained by proposed algorithm. Monte Carlo simulations indicate that the LDPC-coded modulation schemes employing the new constellation sets, obtained by our new signal constellation design algorithm, outperform corresponding QAM constellations significantly in terms of transmission distance and have better nonlinearity tolerance.

Data inversion algorithm development for the hologen occultation experiment

NASA Technical Reports Server (NTRS)

Gordley, Larry L.; Mlynczak, Martin G.

1986-01-01

The successful retrieval of atmospheric parameters from radiometric measurement requires not only the ability to do ideal radiometric calculations, but also a detailed understanding of instrument characteristics. Therefore a considerable amount of time was spent in instrument characterization in the form of test data analysis and mathematical formulation. Analyses of solar-to-reference interference (electrical cross-talk), detector nonuniformity, instrument balance error, electronic filter time-constants and noise character were conducted. A second area of effort was the development of techniques for the ideal radiometric calculations required for the Halogen Occultation Experiment (HALOE) data reduction. The computer code for these calculations must be extremely complex and fast. A scheme for meeting these requirements was defined and the algorithms needed form implementation are currently under development. A third area of work included consulting on the implementation of the Emissivity Growth Approximation (EGA) method of absorption calculation into a HALOE broadband radiometer channel retrieval algorithm.

A modified 3D algorithm for road traffic noise attenuation calculations in large urban areas.

PubMed

Wang, Haibo; Cai, Ming; Yao, Yifan

2017-07-01

The primary objective of this study is the development and application of a 3D road traffic noise attenuation calculation algorithm. First, the traditional empirical method does not address problems caused by non-direct occlusion by buildings and the different building heights. In contrast, this study considers the volume ratio of the buildings and the area ratio of the projection of buildings adjacent to the road. The influence of the ground affection is analyzed. The insertion loss due to barriers (infinite length and finite barriers) is also synthesized in the algorithm. Second, the impact of different road segmentation is analyzed. Through the case of Pearl River New Town, it is recommended that 5° is the most appropriate scanning angle as the computational time is acceptable and the average error is approximately 3.1 dB. In addition, the algorithm requires only 1/17 of the time that the beam tracking method requires at the cost of more imprecise calculation results. Finally, the noise calculation for a large urban area with a high density of buildings shows the feasibility of the 3D noise attenuation calculation algorithm. The algorithm is expected to be applied in projects requiring large area noise simulations. Copyright © 2017 Elsevier Ltd. All rights reserved.

An unbiased risk estimator for image denoising in the presence of mixed poisson-gaussian noise.

PubMed

Le Montagner, Yoann; Angelini, Elsa D; Olivo-Marin, Jean-Christophe

2014-03-01

The behavior and performance of denoising algorithms are governed by one or several parameters, whose optimal settings depend on the content of the processed image and the characteristics of the noise, and are generally designed to minimize the mean squared error (MSE) between the denoised image returned by the algorithm and a virtual ground truth. In this paper, we introduce a new Poisson-Gaussian unbiased risk estimator (PG-URE) of the MSE applicable to a mixed Poisson-Gaussian noise model that unifies the widely used Gaussian and Poisson noise models in fluorescence bioimaging applications. We propose a stochastic methodology to evaluate this estimator in the case when little is known about the internal machinery of the considered denoising algorithm, and we analyze both theoretically and empirically the characteristics of the PG-URE estimator. Finally, we evaluate the PG-URE-driven parametrization for three standard denoising algorithms, with and without variance stabilizing transforms, and different characteristics of the Poisson-Gaussian noise mixture.

An improved algorithm of laser spot center detection in strong noise background

NASA Astrophysics Data System (ADS)

Zhang, Le; Wang, Qianqian; Cui, Xutai; Zhao, Yu; Peng, Zhong

2018-01-01

Laser spot center detection is demanded in many applications. The common algorithms for laser spot center detection such as centroid and Hough transform method have poor anti-interference ability and low detection accuracy in the condition of strong background noise. In this paper, firstly, the median filtering was used to remove the noise while preserving the edge details of the image. Secondly, the binarization of the laser facula image was carried out to extract target image from background. Then the morphological filtering was performed to eliminate the noise points inside and outside the spot. At last, the edge of pretreated facula image was extracted and the laser spot center was obtained by using the circle fitting method. In the foundation of the circle fitting algorithm, the improved algorithm added median filtering, morphological filtering and other processing methods. This method could effectively filter background noise through theoretical analysis and experimental verification, which enhanced the anti-interference ability of laser spot center detection and also improved the detection accuracy.

Comparison between beamforming and super resolution imaging algorithms for non-destructive evaluation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fan, Chengguang; Drinkwater, Bruce W.

In this paper the performance of total focusing method is compared with the widely used time-reversal MUSIC super resolution technique. The algorithms are tested with simulated and experimental ultrasonic array data, each containing different noise levels. The simulated time domain signals allow the effects of array geometry, frequency, scatterer location, scatterer size, scatterer separation and random noise to be carefully controlled. The performance of the imaging algorithms is evaluated in terms of resolution and sensitivity to random noise. It is shown that for the low noise situation, time-reversal MUSIC provides enhanced lateral resolution when compared to the total focusing method.more » However, for higher noise levels, the total focusing method shows robustness, whilst the performance of time-reversal MUSIC is significantly degraded.« less

A Novel Pairwise Comparison-Based Method to Determine Radiation Dose Reduction Potentials of Iterative Reconstruction Algorithms, Exemplified Through Circle of Willis Computed Tomography Angiography.

PubMed

Ellmann, Stephan; Kammerer, Ferdinand; Brand, Michael; Allmendinger, Thomas; May, Matthias S; Uder, Michael; Lell, Michael M; Kramer, Manuel

2016-05-01

The aim of this study was to determine the dose reduction potential of iterative reconstruction (IR) algorithms in computed tomography angiography (CTA) of the circle of Willis using a novel method of evaluating the quality of radiation dose-reduced images. This study relied on ReconCT, a proprietary reconstruction software that allows simulating CT scans acquired with reduced radiation dose based on the raw data of true scans. To evaluate the performance of ReconCT in this regard, a phantom study was performed to compare the image noise of true and simulated scans within simulated vessels of a head phantom. That followed, 10 patients scheduled for CTA of the circle of Willis were scanned according to our institute's standard protocol (100 kV, 145 reference mAs). Subsequently, CTA images of these patients were reconstructed as either a full-dose weighted filtered back projection or with radiation dose reductions down to 10% of the full-dose level and Sinogram-Affirmed Iterative Reconstruction (SAFIRE) with either strength 3 or 5. Images were marked with arrows pointing on vessels of different sizes, and image pairs were presented to observers. Five readers assessed image quality with 2-alternative forced choice comparisons. In the phantom study, no significant differences were observed between the noise levels of simulated and true scans in filtered back projection, SAFIRE 3, and SAFIRE 5 reconstructions.The dose reduction potential for patient scans showed a strong dependence on IR strength as well as on the size of the vessel of interest. Thus, the potential radiation dose reductions ranged from 84.4% for the evaluation of great vessels reconstructed with SAFIRE 5 to 40.9% for the evaluation of small vessels reconstructed with SAFIRE 3. This study provides a novel image quality evaluation method based on 2-alternative forced choice comparisons. In CTA of the circle of Willis, higher IR strengths and greater vessel sizes allowed higher degrees of radiation dose reduction.

The subjective importance of noise spectral content

NASA Astrophysics Data System (ADS)

Baxter, Donald; Phillips, Jonathan; Denman, Hugh

2014-01-01

This paper presents secondary Standard Quality Scale (SQS2) rankings in overall quality JNDs for a subjective analysis of the 3 axes of noise, amplitude, spectral content, and noise type, based on the ISO 20462 softcopy ruler protocol. For the initial pilot study, a Python noise simulation model was created to generate the matrix of noise masks for the softcopy ruler base images with different levels of noise, different low pass filter noise bandwidths and different band pass filter center frequencies, and 3 different types of noise: luma only, chroma only, and luma and chroma combined. Based on the lessons learned, the full subjective experiment, involving 27 observers from Google, NVIDIA and STMicroelectronics was modified to incorporate a wider set of base image scenes, and the removal of band pass filtered noise masks to ease observer fatigue. Good correlation was observed with the Aptina subjective noise study. The absence of tone mapping in the noise simulation model visibly reduced the contrast at high levels of noise, due to the clipping of the high levels of noise near black and white. Under the 34-inch viewing distance, no significant difference was found between the luma only noise masks and the combined luma and chroma noise masks. This was not the intuitive expectation. Two of the base images with large uniform areas, `restaurant' and `no parking', were found to be consistently more sensitive to noise than the texture rich scenes. Two key conclusions are (1) there are fundamentally different sensitivities to noise on a flat patch versus noise in real images and (2) magnification of an image accentuates visual noise in a way that is non-representative of typical noise reduction algorithms generating the same output frequency. Analysis of our experimental noise masks applied to a synthetic Macbeth ColorChecker Chart confirmed the color-dependent nature of the visibility of luma and chroma noise.

The Impact of Different Levels of Adaptive Iterative Dose Reduction 3D on Image Quality of 320-Row Coronary CT Angiography: A Clinical Trial

PubMed Central

Feger, Sarah; Rief, Matthias; Zimmermann, Elke; Martus, Peter; Schuijf, Joanne Désirée; Blobel, Jörg; Richter, Felicitas; Dewey, Marc

2015-01-01

Purpose The aim of this study was the systematic image quality evaluation of coronary CT angiography (CTA), reconstructed with the 3 different levels of adaptive iterative dose reduction (AIDR 3D) and compared to filtered back projection (FBP) with quantum denoising software (QDS). Methods Standard-dose CTA raw data of 30 patients with mean radiation dose of 3.2 ± 2.6 mSv were reconstructed using AIDR 3D mild, standard, strong and compared to FBP/QDS. Objective image quality comparison (signal, noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contour sharpness) was performed using 21 measurement points per patient, including measurements in each coronary artery from proximal to distal. Results Objective image quality parameters improved with increasing levels of AIDR 3D. Noise was lowest in AIDR 3D strong (p≤0.001 at 20/21 measurement points; compared with FBP/QDS). Signal and contour sharpness analysis showed no significant difference between the reconstruction algorithms for most measurement points. Best coronary SNR and CNR were achieved with AIDR 3D strong. No loss of SNR or CNR in distal segments was seen with AIDR 3D as compared to FBP. Conclusions On standard-dose coronary CTA images, AIDR 3D strong showed higher objective image quality than FBP/QDS without reducing contour sharpness. Trial Registration Clinicaltrials.gov NCT00967876 PMID:25945924

Technologies for Turbofan Noise Reduction

NASA Technical Reports Server (NTRS)

Huff, Dennis

2005-01-01

An overview presentation of NASA's engine noise research since 1992 is given for subsonic commercial aircraft applications. Highlights are included from the Advanced Subsonic Technology (AST) Noise Reduction Program and the Quiet Aircraft Technology (QAT) project with emphasis on engine source noise reduction. Noise reduction goals for 10 EPNdB by 207 and 20 EPNdB by 2022 are reviewed. Fan and jet noise technologies are highlighted from the AST program including higher bypass ratio propulsion, scarf inlets, forward-swept fans, swept/leaned stators, chevron nozzles, noise prediction methods, and active noise control for fans. Source diagnostic tests for fans and jets that have been completed over the past few years are presented showing how new flow measurement methods such as Particle Image Velocimetry (PIV) have played a key role in understanding turbulence, the noise generation process, and how to improve noise prediction methods. Tests focused on source decomposition have helped identify which engine components need further noise reduction. The role of Computational AeroAcoustics (CAA) for fan noise prediction is presented. Advanced noise reduction methods such as Hershel-Quincke tubes and trailing edge blowing for fan noise that are currently being pursued n the QAT program are also presented. Highlights are shown form engine validation and flight demonstrations that were done in the late 1990's with Pratt & Whitney on their PW4098 engine and Honeywell on their TFE-731-60 engine. Finally, future propulsion configurations currently being studied that show promise towards meeting NASA's long term goal of 20 dB noise reduction are shown including a Dual Fan Engine concept on a Blended Wing Body aircraft.

Improving label-free detection of circulating melanoma cells by photoacoustic flow cytometry

NASA Astrophysics Data System (ADS)

Zhou, Huan; Wang, Qiyan; Pang, Kai; Zhou, Quanyu; Yang, Ping; He, Hao; Wei, Xunbin

2018-02-01

Melanoma is a kind of a malignant tumor of melanocytes with the properties of high mortality and high metastasis rate. The circulating melanoma cells with the high content of melanin can be detected by light absorption to diagnose and treat cancer at an early stage. Compared with conventional detection methods such as in vivo flow cytometry (IVFC) based on fluorescence, the in vivo photoacoustic flow cytometry (PAFC) utilizes melanin cells as biomarkers to collect the photoacoustic (PA) signals without toxic fluorescent dyes labeling in a non-invasive way. The information of target tumor cells is helpful for data analysis and cell counting. However, the raw signals in PAFC system contain numerous noises such as environmental noise, device noise and in vivo motion noise. Conventional denoising algorithms such as wavelet denoising (WD) method and means filter (MF) method are based on the local information to extract the data of clinical interest, which remove the subtle feature and leave many noises. To address the above questions, the nonlocal means (NLM) method based on nonlocal data has been proposed to suppress the noise in PA signals. Extensive experiments on in vivo PA signals from the mice with the injection of B16F10 cells in caudal vein have been conducted. All the results indicate that the NLM method has superior noise reduction performance and subtle information reservation.

DEMON-type algorithms for determination of hydro-acoustic signatures of surface ships and of divers

NASA Astrophysics Data System (ADS)

Slamnoiu, G.; Radu, O.; Rosca, V.; Pascu, C.; Damian, R.; Surdu, G.; Curca, E.; Radulescu, A.

2016-08-01

With the project “System for detection, localization, tracking and identification of risk factors for strategic importance in littoral areas”, developed in the National Programme II, the members of the research consortium intend to develop a functional model for a hydroacoustic passive subsystem for determination of acoustic signatures of targets such as fast boats and autonomous divers. This paper presents some of the results obtained in the area of hydroacoustic signal processing by using DEMON-type algorithms (Detection of Envelope Modulation On Noise). For evaluation of the performance of various algorithm variations we have used both audio recordings of the underwater noise generated by ships and divers in real situations and also simulated noises. We have analysed the results of processing these signals using four DEMON algorithm structures as presented in the reference literature and a fifth DEMON algorithm structure proposed by the authors of this paper. The algorithm proposed by the authors generates similar results to those obtained by applying the traditional algorithms but requires less computing resources than those and at the same time it has proven to be more resilient to random noise influence.

Seismic random noise attenuation method based on empirical mode decomposition of Hausdorff dimension

NASA Astrophysics Data System (ADS)

Yan, Z.; Luan, X.

2017-12-01

Introduction Empirical mode decomposition (EMD) is a noise suppression algorithm by using wave field separation, which is based on the scale differences between effective signal and noise. However, since the complexity of the real seismic wave field results in serious aliasing modes, it is not ideal and effective to denoise with this method alone. Based on the multi-scale decomposition characteristics of the signal EMD algorithm, combining with Hausdorff dimension constraints, we propose a new method for seismic random noise attenuation. First of all, We apply EMD algorithm adaptive decomposition of seismic data and obtain a series of intrinsic mode function (IMF)with different scales. Based on the difference of Hausdorff dimension between effectively signals and random noise, we identify IMF component mixed with random noise. Then we use threshold correlation filtering process to separate the valid signal and random noise effectively. Compared with traditional EMD method, the results show that the new method of seismic random noise attenuation has a better suppression effect. The implementation process The EMD algorithm is used to decompose seismic signals into IMF sets and analyze its spectrum. Since most of the random noise is high frequency noise, the IMF sets can be divided into three categories: the first category is the effective wave composition of the larger scale; the second category is the noise part of the smaller scale; the third category is the IMF component containing random noise. Then, the third kind of IMF component is processed by the Hausdorff dimension algorithm, and the appropriate time window size, initial step and increment amount are selected to calculate the Hausdorff instantaneous dimension of each component. The dimension of the random noise is between 1.0 and 1.05, while the dimension of the effective wave is between 1.05 and 2.0. On the basis of the previous steps, according to the dimension difference between the random noise and effective signal, we extracted the sample points, whose fractal dimension value is less than or equal to 1.05 for the each IMF components, to separate the residual noise. Using the IMF components after dimension filtering processing and the effective wave IMF components after the first selection for reconstruction, we can obtained the results of de-noising.

Structural parameters that influence the noise reduction characteristics of typical general aviation materials

NASA Technical Reports Server (NTRS)

Roskam, J.; Grosveld, F.

1980-01-01

Effect of panel curvature and oblique angle of sound incidence on noise reduction characteristics of an aluminum panel are experimentally investigated. Panel curvature results show significant increase in stiffness with comparable decrease of sound transmission through the panel in the frequency region below the panel/cavity resonance frequency. Noise reduction data have been achieved for aluminum panels with clamped, bonded and riveted edge conditions. These edge conditions are shown to influence noise reduction characteristics of aluminum panels. Experimentally measured noise reduction characteristics of flat aluminum panels with uniaxial and biaxial in-plane stresses are presented and discussed. Results indicate important improvement in noise reduction of these panels in the frequency range below the fundamental panel/cavity resonance frequency.

Adaptive EMG noise reduction in ECG signals using noise level approximation

NASA Astrophysics Data System (ADS)

Marouf, Mohamed; Saranovac, Lazar

2017-12-01

In this paper the usage of noise level approximation for adaptive Electromyogram (EMG) noise reduction in the Electrocardiogram (ECG) signals is introduced. To achieve the adequate adaptiveness, a translation-invariant noise level approximation is employed. The approximation is done in the form of a guiding signal extracted as an estimation of the signal quality vs. EMG noise. The noise reduction framework is based on a bank of low pass filters. So, the adaptive noise reduction is achieved by selecting the appropriate filter with respect to the guiding signal aiming to obtain the best trade-off between the signal distortion caused by filtering and the signal readability. For the evaluation purposes; both real EMG and artificial noises are used. The tested ECG signals are from the MIT-BIH Arrhythmia Database Directory, while both real and artificial records of EMG noise are added and used in the evaluation process. Firstly, comparison with state of the art methods is conducted to verify the performance of the proposed approach in terms of noise cancellation while preserving the QRS complex waves. Additionally, the signal to noise ratio improvement after the adaptive noise reduction is computed and presented for the proposed method. Finally, the impact of adaptive noise reduction method on QRS complexes detection was studied. The tested signals are delineated using a state of the art method, and the QRS detection improvement for different SNR is presented.

Measuring radiation dose in computed tomography using elliptic phantom and free-in-air, and evaluating iterative metal artifact reduction algorithm

NASA Astrophysics Data System (ADS)

Morgan, Ashraf

The need for an accurate and reliable way for measuring patient dose in multi-row detector computed tomography (MDCT) has increased significantly. This research was focusing on the possibility of measuring CT dose in air to estimate Computed Tomography Dose Index (CTDI) for routine quality control purposes. New elliptic CTDI phantom that better represent human geometry was manufactured for investigating the effect of the subject shape on measured CTDI. Monte Carlo simulation was utilized in order to determine the dose distribution in comparison to the traditional cylindrical CTDI phantom. This research also investigated the effect of Siemens health care newly developed iMAR (iterative metal artifact reduction) algorithm, arthroplasty phantom was designed and manufactured that purpose. The design of new phantoms was part of the research as they mimic the human geometry more than the existing CTDI phantom. The standard CTDI phantom is a right cylinder that does not adequately represent the geometry of the majority of the patient population. Any dose reduction algorithm that is used during patient scan will not be utilized when scanning the CTDI phantom, so a better-designed phantom will allow the use of dose reduction algorithms when measuring dose, which leads to better dose estimation and/or better understanding of dose delivery. Doses from a standard CTDI phantom and the newly-designed phantoms were compared to doses measured in air. Iterative reconstruction is a promising technique in MDCT dose reduction and artifacts correction. Iterative reconstruction algorithms have been developed to address specific imaging tasks as is the case with Iterative Metal Artifact Reduction or iMAR which was developed by Siemens and is to be in use with the companys future computed tomography platform. The goal of iMAR is to reduce metal artifact when imaging patients with metal implants and recover CT number of tissues adjacent to the implant. This research evaluated iMAR capability of recovering CT numbers and reducing noise. Also, the use of iMAR should allow using lower tube voltage instead of 140 KVp which is used frequently to image patients with shoulder implants. The evaluations of image quality and dose reduction were carried out using an arthroplasty phantom.

Fast frequency acquisition via adaptive least squares algorithm

NASA Technical Reports Server (NTRS)

Kumar, R.

1986-01-01

A new least squares algorithm is proposed and investigated for fast frequency and phase acquisition of sinusoids in the presence of noise. This algorithm is a special case of more general, adaptive parameter-estimation techniques. The advantages of the algorithms are their conceptual simplicity, flexibility and applicability to general situations. For example, the frequency to be acquired can be time varying, and the noise can be nonGaussian, nonstationary and colored. As the proposed algorithm can be made recursive in the number of observations, it is not necessary to have a priori knowledge of the received signal-to-noise ratio or to specify the measurement time. This would be required for batch processing techniques, such as the fast Fourier transform (FFT). The proposed algorithm improves the frequency estimate on a recursive basis as more and more observations are obtained. When the algorithm is applied in real time, it has the extra advantage that the observations need not be stored. The algorithm also yields a real time confidence measure as to the accuracy of the estimator.

Low-Contrast and Low-Radiation Dose Protocol in Cardiac Computed Tomography: Usefulness of Low Tube Voltage and Knowledge-Based Iterative Model Reconstruction Algorithm.

PubMed

Iyama, Yuji; Nakaura, Takeshi; Yokoyama, Koichi; Kidoh, Masafumi; Harada, Kazunori; Oda, Seitaro; Tokuyasu, Shinichi; Yamashita, Yasuyuki

This study aimed to evaluate the feasibility of a low contrast, low-radiation dose protocol of 80-peak kilovoltage (kVp) with prospective electrocardiography-gated cardiac computed tomography (CT) using knowledge-based iterative model reconstruction (IMR). Thirty patients underwent an 80-kVp prospective electrocardiography-gated cardiac CT with low-contrast agent (222-mg iodine per kilogram of body weight) dose. We also enrolled 30 consecutive patients who were scanned with a 120-kVp cardiac CT with filtered back projection using the standard contrast agent dose (370-mg iodine per kilogram of body weight) as a historical control group. We evaluated the radiation dose for the 2 groups. The 80-kVp images were reconstructed with filtered back projection (protocol A), hybrid iterative reconstruction (HIR, protocol B), and IMR (protocol C). We compared CT numbers, image noise, and contrast-to-noise ratio among 120-kVp protocol, protocol A, protocol B, and protocol C. In addition, we compared the noise reduction rate between HIR and IMR. Two independent readers compared image contrast, image noise, image sharpness, unfamiliar image texture, and overall image quality among the 4 protocols. The estimated effective dose (ED) of the 80-kVp protocol was 74% lower than that of the 120-kVp protocol (1.4 vs 5.4 mSv). The contrast-to-noise ratio of protocol C was significantly higher than that of protocol A. The noise reduction rate of IMR was significantly higher than that of HIR (P < 0.01). There was no significant difference in almost all qualitative image quality between 120-kVp protocol and protocol C except for image contrast. A 80-kVp protocol with IMR yields higher image quality with 74% decreased radiation dose and 40% decreased contrast agent dose as compared with a 120-kVp protocol, while decreasing more image noise compared with the 80-kVp protocol with HIR.

Aircraft Noise Reduction Subproject Overview

NASA Technical Reports Server (NTRS)

Fernandez, Hamilton; Nark, Douglas M.; Van Zante, Dale E.

2016-01-01

The material presents highlights of propulsion and airframe noise research being completed for the Advanced Air Transport Technology Project. The basis of noise reduction plans along with representative work for the airframe, propulsion, and propulsion-airframe integration is discussed for the Aircraft Noise reduction Subproject.

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

NASA Astrophysics Data System (ADS)

Mishra, Puneet; Singla, Sunil Kumar

2013-01-01

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

Experimental study of stochastic noise propagation in SPECT images reconstructed using the conjugate gradient algorithm.

PubMed

Mariano-Goulart, D; Fourcade, M; Bernon, J L; Rossi, M; Zanca, M

2003-01-01

Thanks to an experimental study based on simulated and physical phantoms, the propagation of the stochastic noise in slices reconstructed using the conjugate gradient algorithm has been analysed versus iterations. After a first increase corresponding to the reconstruction of the signal, the noise stabilises before increasing linearly with iterations. The level of the plateau as well as the slope of the subsequent linear increase depends on the noise in the projection data.

Impact of Noise Reduction Algorithm in Cochlear Implant Processing on Music Enjoyment.

PubMed

Kohlberg, Gavriel D; Mancuso, Dean M; Griffin, Brianna M; Spitzer, Jaclyn B; Lalwani, Anil K

2016-06-01

Noise reduction algorithm (NRA) in speech processing strategy has positive impact on speech perception among cochlear implant (CI) listeners. We sought to evaluate the effect of NRA on music enjoyment. Prospective analysis of music enjoyment. Academic medical center. Normal-hearing (NH) adults (N = 16) and CI listeners (N = 9). Subjective rating of music excerpts. NH and CI listeners evaluated country music piece on three enjoyment modalities: pleasantness, musicality, and naturalness. Participants listened to the original version and 20 modified, less complex versions created by including subsets of musical instruments from the original song. NH participants listened to the segments through CI simulation and CI listeners listened to the segments with their usual speech processing strategy, with and without NRA. Decreasing the number of instruments was significantly associated with increase in the pleasantness and naturalness in both NH and CI subjects (p < 0.05). However, there was no difference in music enjoyment with or without NRA for either NH listeners with CI simulation or CI listeners across all three modalities of pleasantness, musicality, and naturalness (p > 0.05): this was true for the original and the modified music segments with one to three instruments (p > 0.05). NRA does not affect music enjoyment in CI listener or NH individual with CI simulation. This suggests that strategies to enhance speech processing will not necessarily have a positive impact on music enjoyment. However, reducing the complexity of music shows promise in enhancing music enjoyment and should be further explored.

An improved affine projection algorithm for active noise cancellation

NASA Astrophysics Data System (ADS)

Zhang, Congyan; Wang, Mingjiang; Han, Yufei; Sun, Yunzhuo

2017-08-01

Affine projection algorithm is a signal reuse algorithm, and it has a good convergence rate compared to other traditional adaptive filtering algorithm. There are two factors that affect the performance of the algorithm, which are step factor and the projection length. In the paper, we propose a new variable step size affine projection algorithm (VSS-APA). It dynamically changes the step size according to certain rules, so that it can get smaller steady-state error and faster convergence speed. Simulation results can prove that its performance is superior to the traditional affine projection algorithm and in the active noise control (ANC) applications, the new algorithm can get very good results.

Recursive Algorithms for Real-Time Digital CR-RCn Pulse Shaping

NASA Astrophysics Data System (ADS)

Nakhostin, M.

2011-10-01

This paper reports on recursive algorithms for real-time implementation of CR-(RC)n filters in digital nuclear spectroscopy systems. The algorithms are derived by calculating the Z-transfer function of the filters for filter orders up to n=4 . The performances of the filters are compared with the performance of the conventional digital trapezoidal filter using a noise generator which separately generates pure series, 1/f and parallel noise. The results of our study enable one to select the optimum digital filter for different noise and rate conditions.

SU-F-T-407: Artifact Reduction with Dual Energy Or IMAR: Who’s Winning?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elder, E; Schreibmann, E; Dhabaan, A

2016-06-15

Purpose: The purpose of this abstract was to evaluate the performance of commercial strategies for artifact reduction in radiation oncology settings. The iterative metal artifact reduction (Siemens iMAR) algorithm and monoenergetic virtual datasets reconstructed from dual energy scans are compared side-by-side in their ability to image in the presence of metal inserts. Methods: A CIRS ATOM Dosimetry Verification Phantom was scanned with and without a metal insert on a SOMATOM Definition AS dual energy scanner. Images with the metal insert were reconstructed with (a) a tradition single energy CT scan with the iMAR option implemented, using different artifact reduction settingsmore » and (b) a monoenergetic scan calculated from dual energy scans by recovering differences in the energy-dependence of the attenuation coefficients of different materials and then creating a virtual monoenergetic scan from these coefficients. The iMAR and monoenergetic scans were then compared with the metal-free scan to assess changes in HU numbers and noise within a region around the metal insert. Results: Both the iMAR and dual energy scans reduced artifacts produced by the metal insert. However the iMAR results are dependent of the selected algorithm settings, with a mean HU difference ranging from 0.65 to 90.40 for different options. The mean differences without the iMAR correction were 38.74. When using the dual energy scan, the mean differences were 4.53, that is however attributed to increased noise and not artifacts, as the dual energy scan had the lowest skewness (2.52) compared to the iMAR scans (ranging from 3.90 to 4.88) and the lowest kurtosis (5.72 for dual energy, range of 18.19 to 27.36 for iMAR). Conclusion: Both approaches accurately recovered HU numbers, however the dual energy method provided smaller residual artifacts.« less

ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform.

PubMed

El B'charri, Oussama; Latif, Rachid; Elmansouri, Khalifa; Abenaou, Abdenbi; Jenkal, Wissam

2017-02-07

Since the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician's correct decision on patients. The dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet. In this work, we shall provide a comprehensive study on the impact of the choice of threshold algorithm, threshold value, and the appropriate wavelet decomposition level to evaluate the ECG signal de-noising performance. A set of simulations is performed on both synthetic and real ECG signals to achieve the promised results. First, the synthetic ECG signal is used to observe the algorithm response. The evaluation results of synthetic ECG signal corrupted by various types of noise has showed that the modified unified threshold and wavelet hyperbolic threshold de-noising method is better in realistic and colored noises. The tuned threshold is then used on real ECG signals from the MIT-BIH database. The results has shown that the proposed method achieves higher performance than the ordinary dual tree wavelet transform into all kinds of noise removal from ECG signal. The simulation results indicate that the algorithm is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal. Moreover, the algorithm is quite simple and can be used in real time ECG monitoring.

The effect of oblique angle of sound incidence, realistic edge conditions, curvature and in-plane panel stresses on the noise reduction characteristics of general aviation type panels

NASA Technical Reports Server (NTRS)

Grosveld, F.; Lameris, J.; Dunn, D.

1979-01-01

Experiments and a theoretical analysis were conducted to predict the noise reduction of inclined and curved panels. These predictions are compared to the experimental results with reasonable agreement between theory and experiment for panels under an oblique angle of sound incidence. Theoretical as well as experimental results indicate a big increase in noise reduction when a flat test panel is curved. Further curving the panel slightly decreases the noise reduction. Riveted flat panels are shown to give a higher noise reduction in the stiffness-controlled frequency region, while bonded panels are superior in this region when the test panel is curved. Experimentally measured noise reduction characteristics of flat aluminum panels with uniaxial in-plane stresses are presented and discussed. These test results indicate an important improvement in the noise reduction of these panels in the frequency range below the fundamental panel/cavity frequency.

Noise reduction for model counterrotation propeller at cruise by reducing aft-propeller diameter

NASA Technical Reports Server (NTRS)

Dittmar, James H.; Stang, David B.

1987-01-01

The forward propeller of a model counterrotation propeller was tested with its original aft propeller and with a reduced diameter aft propeller. Noise reductions with the reduced diameter aft propeller were measured at simulated cruise conditions. Reductions were as large as 7.5 dB for the aft-propeller passing tone and 15 dB in the harmonics at specific angles. The interaction tones, mostly the first, were reduced probably because the reduced-diameter aft-propeller blades no longer interacted with the forward propeller tip vortex. The total noise (sum of primary and interaction noise) at each harmonic was significantly reduced. The chief noise reduction at each harmonic came from reduced aft-propeller-alone noise, with the interaction tones contributing little to the totals at cruise. Total cruise noise reductions were as much as 3 dB at given angles for the blade passing tone and 10 dB for some of the harmonics. These reductions would measurably improve the fuselage interior noise levels and represent a definite cruise noise benefit from using a reduced diameter aft propeller.

The slow-scale linear noise approximation: an accurate, reduced stochastic description of biochemical networks under timescale separation conditions

PubMed Central

2012-01-01

Background It is well known that the deterministic dynamics of biochemical reaction networks can be more easily studied if timescale separation conditions are invoked (the quasi-steady-state assumption). In this case the deterministic dynamics of a large network of elementary reactions are well described by the dynamics of a smaller network of effective reactions. Each of the latter represents a group of elementary reactions in the large network and has associated with it an effective macroscopic rate law. A popular method to achieve model reduction in the presence of intrinsic noise consists of using the effective macroscopic rate laws to heuristically deduce effective probabilities for the effective reactions which then enables simulation via the stochastic simulation algorithm (SSA). The validity of this heuristic SSA method is a priori doubtful because the reaction probabilities for the SSA have only been rigorously derived from microscopic physics arguments for elementary reactions. Results We here obtain, by rigorous means and in closed-form, a reduced linear Langevin equation description of the stochastic dynamics of monostable biochemical networks in conditions characterized by small intrinsic noise and timescale separation. The slow-scale linear noise approximation (ssLNA), as the new method is called, is used to calculate the intrinsic noise statistics of enzyme and gene networks. The results agree very well with SSA simulations of the non-reduced network of elementary reactions. In contrast the conventional heuristic SSA is shown to overestimate the size of noise for Michaelis-Menten kinetics, considerably under-estimate the size of noise for Hill-type kinetics and in some cases even miss the prediction of noise-induced oscillations. Conclusions A new general method, the ssLNA, is derived and shown to correctly describe the statistics of intrinsic noise about the macroscopic concentrations under timescale separation conditions. The ssLNA provides a simple and accurate means of performing stochastic model reduction and hence it is expected to be of widespread utility in studying the dynamics of large noisy reaction networks, as is common in computational and systems biology. PMID:22583770

Improved liver R2* mapping by pixel-wise curve fitting with adaptive neighborhood regularization.

PubMed

Wang, Changqing; Zhang, Xinyuan; Liu, Xiaoyun; He, Taigang; Chen, Wufan; Feng, Qianjin; Feng, Yanqiu

2018-08-01

To improve liver R2* mapping by incorporating adaptive neighborhood regularization into pixel-wise curve fitting. Magnetic resonance imaging R2* mapping remains challenging because of the serial images with low signal-to-noise ratio. In this study, we proposed to exploit the neighboring pixels as regularization terms and adaptively determine the regularization parameters according to the interpixel signal similarity. The proposed algorithm, called the pixel-wise curve fitting with adaptive neighborhood regularization (PCANR), was compared with the conventional nonlinear least squares (NLS) and nonlocal means filter-based NLS algorithms on simulated, phantom, and in vivo data. Visually, the PCANR algorithm generates R2* maps with significantly reduced noise and well-preserved tiny structures. Quantitatively, the PCANR algorithm produces R2* maps with lower root mean square errors at varying R2* values and signal-to-noise-ratio levels compared with the NLS and nonlocal means filter-based NLS algorithms. For the high R2* values under low signal-to-noise-ratio levels, the PCANR algorithm outperforms the NLS and nonlocal means filter-based NLS algorithms in the accuracy and precision, in terms of mean and standard deviation of R2* measurements in selected region of interests, respectively. The PCANR algorithm can reduce the effect of noise on liver R2* mapping, and the improved measurement precision will benefit the assessment of hepatic iron in clinical practice. Magn Reson Med 80:792-801, 2018. © 2018 International Society for Magnetic Resonance in Medicine. © 2018 International Society for Magnetic Resonance in Medicine.

An Out-of-Core GPU based dimensionality reduction algorithm for Big Mass Spectrometry Data and its application in bottom-up Proteomics.

PubMed

Awan, Muaaz Gul; Saeed, Fahad

2017-08-01

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and computational operations inside GPU. These novel data structures include Binary Spectra and Quantized Indexed Spectra (QIS) . The former helps in communicating essential information between CPU and GPU using minimum amount of data while latter enables us to store and process complex 3-D data structure into a 1-D array structure while maintaining the integrity of MS data. Our proposed algorithm also takes into account the limited memory of GPUs and switches between in-core and out-of-core modes based upon the size of input data. G-MSR achieves a peak speed-up of 386x over its sequential counterpart and is shown to process over a million spectra in just 32 seconds. The code for this algorithm is available as a GPL open-source at GitHub at the following link: https://github.com/pcdslab/G-MSR.

WE-FG-207B-04: Noise Suppression for Energy-Resolved CT Via Variance Weighted Non-Local Filtration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harms, J; Zhu, L

Purpose: The photon starvation problem is exacerbated in energy-resolved CT, since the detected photons are shared by multiple energy channels. Using pixel similarity-based non-local filtration, we aim to produce accurate and high-resolution energy-resolved CT images with significantly reduced noise. Methods: Averaging CT images reconstructed from different energy channels reduces noise at the price of losing spectral information, while conventional denoising techniques inevitably degrade image resolution. Inspired by the fact that CT images of the same object at different energies share the same structures, we aim to reduce noise of energy-resolved CT by averaging only pixels of similar materials - amore » non-local filtration technique. For each CT image, an empirical exponential model is used to calculate the material similarity between two pixels based on their CT values and the similarity values are organized in a matrix form. A final similarity matrix is generated by averaging these similarity matrices, with weights inversely proportional to the estimated total noise variance in the sinogram of different energy channels. Noise suppression is achieved for each energy channel via multiplying the image vector by the similarity matrix. Results: Multiple scans on a tabletop CT system are used to simulate 6-channel energy-resolved CT, with energies ranging from 75 to 125 kVp. On a low-dose acquisition at 15 mA of the Catphan©600 phantom, our method achieves the same image spatial resolution as a high-dose scan at 80 mA with a noise standard deviation (STD) lower by a factor of >2. Compared with another non-local noise suppression algorithm (ndiNLM), the proposed algorithms obtains images with substantially improved resolution at the same level of noise reduction. Conclusion: We propose a noise-suppression method for energy-resolved CT. Our method takes full advantage of the additional structural information provided by energy-resolved CT and preserves image values at each energy level. Research reported in this publication was supported by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R21EB019597. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.« less

The effects of digital signal processing features on children's speech recognition and loudness perception.

PubMed

Crukley, Jeffery; Scollie, Susan D

2014-03-01

The purpose of this study was to determine the effects of hearing instruments set to Desired Sensation Level version 5 (DSL v5) hearing instrument prescription algorithm targets and equipped with directional microphones and digital noise reduction (DNR) on children's sentence recognition in noise performance and loudness perception in a classroom environment. Ten children (ages 8-17 years) with stable, congenital sensorineural hearing losses participated in the study. Participants were fitted bilaterally with behind-the-ear hearing instruments set to DSL v5 prescriptive targets. Sentence recognition in noise was evaluated using the Bamford-Kowal-Bench Speech in Noise Test (Niquette et al., 2003). Loudness perception was evaluated using a modified version of the Contour Test of Loudness Perception (Cox, Alexander, Taylor, & Gray, 1997). Children's sentence recognition in noise performance was significantly better when using directional microphones alone or in combination with DNR than when using omnidirectional microphones alone or in combination with DNR. Children's loudness ratings for sounds above 72 dB SPL were lowest when fitted with the DSL v5 Noise prescription combined with directional microphones. DNR use showed no effect on loudness ratings. Use of the DSL v5 Noise prescription with a directional microphone improved sentence recognition in noise performance and reduced loudness perception ratings for loud sounds relative to a typical clinical reference fitting with the DSL v5 Quiet prescription with no digital signal processing features enabled. Potential clinical strategies are discussed.

Development of a voltage-dependent current noise algorithm for conductance-based stochastic modelling of auditory nerve fibres.

PubMed

Badenhorst, Werner; Hanekom, Tania; Hanekom, Johan J

2016-12-01

This study presents the development of an alternative noise current term and novel voltage-dependent current noise algorithm for conductance-based stochastic auditory nerve fibre (ANF) models. ANFs are known to have significant variance in threshold stimulus which affects temporal characteristics such as latency. This variance is primarily caused by the stochastic behaviour or microscopic fluctuations of the node of Ranvier's voltage-dependent sodium channels of which the intensity is a function of membrane voltage. Though easy to implement and low in computational cost, existing current noise models have two deficiencies: it is independent of membrane voltage, and it is unable to inherently determine the noise intensity required to produce in vivo measured discharge probability functions. The proposed algorithm overcomes these deficiencies while maintaining its low computational cost and ease of implementation compared to other conductance and Markovian-based stochastic models. The algorithm is applied to a Hodgkin-Huxley-based compartmental cat ANF model and validated via comparison of the threshold probability and latency distributions to measured cat ANF data. Simulation results show the algorithm's adherence to in vivo stochastic fibre characteristics such as an exponential relationship between the membrane noise and transmembrane voltage, a negative linear relationship between the log of the relative spread of the discharge probability and the log of the fibre diameter and a decrease in latency with an increase in stimulus intensity.

Noise-cancellation-based nonuniformity correction algorithm for infrared focal-plane arrays.

PubMed

Godoy, Sebastián E; Pezoa, Jorge E; Torres, Sergio N

2008-10-10

The spatial fixed-pattern noise (FPN) inherently generated in infrared (IR) imaging systems compromises severely the quality of the acquired imagery, even making such images inappropriate for some applications. The FPN refers to the inability of the photodetectors in the focal-plane array to render a uniform output image when a uniform-intensity scene is being imaged. We present a noise-cancellation-based algorithm that compensates for the additive component of the FPN. The proposed method relies on the assumption that a source of noise correlated to the additive FPN is available to the IR camera. An important feature of the algorithm is that all the calculations are reduced to a simple equation, which allows for the bias compensation of the raw imagery. The algorithm performance is tested using real IR image sequences and is compared to some classical methodologies. (c) 2008 Optical Society of America

Airport noise impact reduction through operations

NASA Technical Reports Server (NTRS)

Deloach, R.

1981-01-01

The airport-noise levels and annoyance model (ALAMO) developed at NASA Langley Research Center is comprised of a system of computer programs which is capable of quantifying airport community noise impact in terms of noise level, population distribution, and human subjective response to noise. The ALAMO can be used to compare the noise impact of an airport's current operating scenario with the noise impact which would result from some proposed change in airport operations. The relative effectiveness of number of noise-impact reduction alternatives is assessed for a major midwest airport. Significant reductions in noise impact are predicted for certain noise abatement strategies while others are shown to result in relatively little noise relief.

Improving Cancer Detection and Dose Efficiency in Dedicated Breast Cancer CT

DTIC Science & Technology

2010-02-01

source trajectory and data truncation, which can however be solved with the back-projection filtration ( BPF ) algorithm [6,7]. I have used the BPF ...high to low radiation dose levels. I have investigated noise properties in images reconstructed by use of FDK and BPF algorithms at different noise...analytic algorithms such as the FDK and BPF algorithms are applied to sparse-view data, the reconstruction images will contain artifacts such as streak

The Research on Denoising of SAR Image Based on Improved K-SVD Algorithm

NASA Astrophysics Data System (ADS)

Tan, Linglong; Li, Changkai; Wang, Yueqin

2018-04-01

SAR images often receive noise interference in the process of acquisition and transmission, which can greatly reduce the quality of images and cause great difficulties for image processing. The existing complete DCT dictionary algorithm is fast in processing speed, but its denoising effect is poor. In this paper, the problem of poor denoising, proposed K-SVD (K-means and singular value decomposition) algorithm is applied to the image noise suppression. Firstly, the sparse dictionary structure is introduced in detail. The dictionary has a compact representation and can effectively train the image signal. Then, the sparse dictionary is trained by K-SVD algorithm according to the sparse representation of the dictionary. The algorithm has more advantages in high dimensional data processing. Experimental results show that the proposed algorithm can remove the speckle noise more effectively than the complete DCT dictionary and retain the edge details better.

Alpha image reconstruction (AIR): A new iterative CT image reconstruction approach using voxel-wise alpha blending

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hofmann, Christian; Sawall, Stefan; Knaup, Michael

2014-06-15

Purpose: Iterative image reconstruction gains more and more interest in clinical routine, as it promises to reduce image noise (and thereby patient dose), to reduce artifacts, or to improve spatial resolution. Among vendors and researchers, however, there is no consensus of how to best achieve these aims. The general approach is to incorporatea priori knowledge into iterative image reconstruction, for example, by adding additional constraints to the cost function, which penalize variations between neighboring voxels. However, this approach to regularization in general poses a resolution noise trade-off because the stronger the regularization, and thus the noise reduction, the stronger themore » loss of spatial resolution and thus loss of anatomical detail. The authors propose a method which tries to improve this trade-off. The proposed reconstruction algorithm is called alpha image reconstruction (AIR). One starts with generating basis images, which emphasize certain desired image properties, like high resolution or low noise. The AIR algorithm reconstructs voxel-specific weighting coefficients that are applied to combine the basis images. By combining the desired properties of each basis image, one can generate an image with lower noise and maintained high contrast resolution thus improving the resolution noise trade-off. Methods: All simulations and reconstructions are performed in native fan-beam geometry. A water phantom with resolution bar patterns and low contrast disks is simulated. A filtered backprojection (FBP) reconstruction with a Ram-Lak kernel is used as a reference reconstruction. The results of AIR are compared against the FBP results and against a penalized weighted least squares reconstruction which uses total variation as regularization. The simulations are based on the geometry of the Siemens Somatom Definition Flash scanner. To quantitatively assess image quality, the authors analyze line profiles through resolution patterns to define a contrast factor for contrast-resolution plots. Furthermore, the authors calculate the contrast-to-noise ratio with the low contrast disks and the authors compare the agreement of the reconstructions with the ground truth by calculating the normalized cross-correlation and the root-mean-square deviation. To evaluate the clinical performance of the proposed method, the authors reconstruct patient data acquired with a Somatom Definition Flash dual source CT scanner (Siemens Healthcare, Forchheim, Germany). Results: The results of the simulation study show that among the compared algorithms AIR achieves the highest resolution and the highest agreement with the ground truth. Compared to the reference FBP reconstruction AIR is able to reduce the relative pixel noise by up to 50% and at the same time achieve a higher resolution by maintaining the edge information from the basis images. These results can be confirmed with the patient data. Conclusions: To evaluate the AIR algorithm simulated and measured patient data of a state-of-the-art clinical CT system were processed. It is shown, that generating CT images through the reconstruction of weighting coefficients has the potential to improve the resolution noise trade-off and thus to improve the dose usage in clinical CT.« less

Image quality comparison of two adaptive statistical iterative reconstruction (ASiR, ASiR-V) algorithms and filtered back projection in routine liver CT.

PubMed

Chen, Li-Hong; Jin, Chao; Li, Jian-Ying; Wang, Ge-Liang; Jia, Yong-Jun; Duan, Hai-Feng; Pan, Ning; Guo, Jianxin

2018-06-06

To compare image quality of two adaptive statistical iterative reconstruction (ASiR and ASiR-V) algorithms using objective and subjective metrics for routine liver CT, with the conventional filtered back projection (FBP) reconstructions as reference standards. This institutional review board-approved study included 52 patients with clinically suspected hepatic metastases. Patients were divided equally into ASiR and ASiR-V groups with same scan parameters. Images were reconstructed with ASiR and ASiR-V from 0 (FBP) to 100% blending percentages at 10% interval in its respective group. Mean and standard deviation of CT numbers for liver parenchyma were recorded. Two experienced radiologists reviewed all images for image quality blindly and independently. Data were statistically analyzed. There was no difference in CT dose index between ASiR and ASiR-V groups. As the percentage of ASiR and ASiR-V increased from 10 to 100% , image noise reduced by 8.6 -57.9% and 8.9-81.6%, respectively, compared with FBP. There was substantial interobserver agreement in image quality assessment for ASiR and ASiR-V images. Compared with FBP reconstruction, subjective image quality scores of ASiR and ASiR-V improved significantly as percentage increased from 10 to 80% for ASiR (peaked at 50% with 32.2% noise reduction) and from 10 to 90% (peaked at 60% with 51.5% noise reduction) for ASiR-V. Both ASiR and ASiR-V improved the objective and subjective image quality for routine liver CT compared with FBP. ASiR-V provided further image quality improvement with higher acceptable percentage than ASiR, and ASiR-V60% had the highest image quality score. Advances in knowledge: (1) Both ASiR and ASiR-V significantly reduce image noise compared with conventional FBP reconstruction. (2) ASiR-V with 60 blending percentage provides the highest image quality score in routine liver CT.

Hybrid Wing Body Aircraft System Noise Assessment with Propulsion Airframe Aeroacoustic Experiments

NASA Technical Reports Server (NTRS)

Thomas, Russell H.; Burley, Casey L.; Olson, Erik D.

2010-01-01

A system noise assessment of a hybrid wing body configuration was performed using NASA s best available aircraft models, engine model, and system noise assessment method. A propulsion airframe aeroacoustic effects experimental database for key noise sources and interaction effects was used to provide data directly in the noise assessment where prediction methods are inadequate. NASA engine and aircraft system models were created to define the hybrid wing body aircraft concept as a twin engine aircraft with a 7500 nautical mile mission. The engines were modeled as existing technology high bypass ratio turbofans. The baseline hybrid wing body aircraft was assessed at 22 dB cumulative below the FAA Stage 4 certification level. To determine the potential for noise reduction with relatively near term technologies, seven other configurations were assessed beginning with moving the engines two fan nozzle diameters upstream of the trailing edge and then adding technologies for reduction of the highest noise sources. Aft radiated noise was expected to be the most challenging to reduce and, therefore, the experimental database focused on jet nozzle and pylon configurations that could reduce jet noise through a combination of source reduction and shielding effectiveness. The best configuration for reduction of jet noise used state-of-the-art technology chevrons with a pylon above the engine in the crown position. This configuration resulted in jet source noise reduction, favorable azimuthal directivity, and noise source relocation upstream where it is more effectively shielded by the limited airframe surface, and additional fan noise attenuation from acoustic liner on the crown pylon internal surfaces. Vertical and elevon surfaces were also assessed to add shielding area. The elevon deflection above the trailing edge showed some small additional noise reduction whereas vertical surfaces resulted in a slight noise increase. With the effects of the configurations from the database included, the best available noise reduction was 40 dB cumulative. Projected effects from additional technologies were assessed for an advanced noise reduction configuration including landing gear fairings and advanced pylon and chevron nozzles. Incorporating the three additional technology improvements, an aircraft noise is projected of 42.4 dB cumulative below the Stage 4 level.

Complexity reduction in the H.264/AVC using highly adaptive fast mode decision based on macroblock motion activity

NASA Astrophysics Data System (ADS)

Abdellah, Skoudarli; Mokhtar, Nibouche; Amina, Serir

2015-11-01

The H.264/AVC video coding standard is used in a wide range of applications from video conferencing to high-definition television according to its high compression efficiency. This efficiency is mainly acquired from the newly allowed prediction schemes including variable block modes. However, these schemes require a high complexity to select the optimal mode. Consequently, complexity reduction in the H.264/AVC encoder has recently become a very challenging task in the video compression domain, especially when implementing the encoder in real-time applications. Fast mode decision algorithms play an important role in reducing the overall complexity of the encoder. In this paper, we propose an adaptive fast intermode algorithm based on motion activity, temporal stationarity, and spatial homogeneity. This algorithm predicts the motion activity of the current macroblock from its neighboring blocks and identifies temporal stationary regions and spatially homogeneous regions using adaptive threshold values based on content video features. Extensive experimental work has been done in high profile, and results show that the proposed source-coding algorithm effectively reduces the computational complexity by 53.18% on average compared with the reference software encoder, while maintaining the high-coding efficiency of H.264/AVC by incurring only 0.097 dB in total peak signal-to-noise ratio and 0.228% increment on the total bit rate.

Variance-reduced simulation of lattice discrete-time Markov chains with applications in reaction networks

NASA Astrophysics Data System (ADS)

Maginnis, P. A.; West, M.; Dullerud, G. E.

2016-10-01

We propose an algorithm to accelerate Monte Carlo simulation for a broad class of stochastic processes. Specifically, the class of countable-state, discrete-time Markov chains driven by additive Poisson noise, or lattice discrete-time Markov chains. In particular, this class includes simulation of reaction networks via the tau-leaping algorithm. To produce the speedup, we simulate pairs of fair-draw trajectories that are negatively correlated. Thus, when averaged, these paths produce an unbiased Monte Carlo estimator that has reduced variance and, therefore, reduced error. Numerical results for three example systems included in this work demonstrate two to four orders of magnitude reduction of mean-square error. The numerical examples were chosen to illustrate different application areas and levels of system complexity. The areas are: gene expression (affine state-dependent rates), aerosol particle coagulation with emission and human immunodeficiency virus infection (both with nonlinear state-dependent rates). Our algorithm views the system dynamics as a ;black-box;, i.e., we only require control of pseudorandom number generator inputs. As a result, typical codes can be retrofitted with our algorithm using only minor changes. We prove several analytical results. Among these, we characterize the relationship of covariances between paths in the general nonlinear state-dependent intensity rates case, and we prove variance reduction of mean estimators in the special case of affine intensity rates.

An l1-TV Algorithm for Deconvolution with Salt and Pepper Noise

DTIC Science & Technology

2009-04-01

deblurring in the presence of impulsive noise ,” Int. J. Comput. Vision, vol. 70, no. 3, pp. 279–298, Dec. 2006. [13] A. E. Beaton and J. W. Tukey, “The...AN 1-TV ALGORITHM FOR DECONVOLUTIONWITH SALT AND PEPPER NOISE Brendt Wohlberg∗ T-7 Mathematical Modeling and Analysis Los Alamos National Laboratory...and pepper noise , but the extension of this formulation to more general prob- lems, such as deconvolution, has received little attention. We consider

Restored low-dose digital breast tomosynthesis: a perception study

NASA Astrophysics Data System (ADS)

Borges, Lucas R.; Bakic, Predrag R.; Maidment, Andrew D. A.; Vieira, Marcelo A. C.

2018-03-01

This work investigates the perception of noise from restored low-dose digital breast tomosynthesis (DBT) images. First, low-dose DBT projections were generated using a dose reduction simulation algorithm. A dataset of clinical images from the Hospital of the University of Pennsylvania was used for this purpose. Low-dose projections were then denoised with a denoising pipeline developed specifically for DBT images. Denoised and noisy projections were combined to generate images with signal-to-noise ratio comparable to the full-dose images. The quality of restored low-dose and full-dose projections were first compared in terms of an objective no-reference image quality metric previously validated for mammography. In the second analysis, regions of interest (ROIs) were selected from reconstructed full-dose and restored low-dose slices, and were displayed side-by-side on a high-resolution medical display. Five medical physics specialists were asked to choose the image containing less noise and less blur using a 2-AFC experiment. The objective metric shows that, after the proposed image restoration framework was applied, images with as little as 60% of the AEC dose yielded similar quality indices when compared to images acquired with the full-dose. In the 2-AFC experiments results showed that when the denoising framework was used, 30% reduction in dose was possible without any perceived difference in noise or blur. Note that this study evaluated the observers perception to noise and blur and does not claim that the dose of DBT examinations can be reduced with no harm to the detection of cancer. Future work is necessary to make any claims regarding detection, localization and characterization of lesions.

Implementation of noise budgets for civil airports

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bishop, D.E.

1982-01-01

An increasing number of airports are faced with the need for establishing a lid on the noise from aircraft operations and for developing programs for reducing airport noise on a year-to-year basis. As an example, the California Airport Noise Standard acts to impose such programs on a number of airports in California. Any airport faced with the need to establish a quantitative reduction of noise obviously wants to achieve this reduction with the least impact on numbers of operations and reduction in air transportation services to the community. A reduction in noise and an increase in operations usually can bemore » achieved only by encouraging use of the quietest aircraft available and, further adding incentives for operating procedures that minimize noise. One approach in administering airport noise reduction is to adopt an airport noise budget. As used in this paper, the noise budget concept implies that quantitative limits on the noise environment and on the noise contributions by major airport users will be established. Having methods for enforcing compliance with the airport budget for those airport users that exceed their budget will be established. Thus, the noise budget provides airport management, and major airport users, with quantitative measures for defining noise goals, and actual progress in achieving such goals.« less

Noise spatial nonuniformity and the impact of statistical image reconstruction in CT myocardial perfusion imaging.

PubMed

Lauzier, Pascal Theriault; Tang, Jie; Speidel, Michael A; Chen, Guang-Hong

2012-07-01

To achieve high temporal resolution in CT myocardial perfusion imaging (MPI), images are often reconstructed using filtered backprojection (FBP) algorithms from data acquired within a short-scan angular range. However, the variation in the central angle from one time frame to the next in gated short scans has been shown to create detrimental partial scan artifacts when performing quantitative MPI measurements. This study has two main purposes. (1) To demonstrate the existence of a distinct detrimental effect in short-scan FBP, i.e., the introduction of a nonuniform spatial image noise distribution; this nonuniformity can lead to unexpectedly high image noise and streaking artifacts, which may affect CT MPI quantification. (2) To demonstrate that statistical image reconstruction (SIR) algorithms can be a potential solution to address the nonuniform spatial noise distribution problem and can also lead to radiation dose reduction in the context of CT MPI. Projection datasets from a numerically simulated perfusion phantom and an in vivo animal myocardial perfusion CT scan were used in this study. In the numerical phantom, multiple realizations of Poisson noise were added to projection data at each time frame to investigate the spatial distribution of noise. Images from all datasets were reconstructed using both FBP and SIR reconstruction algorithms. To quantify the spatial distribution of noise, the mean and standard deviation were measured in several regions of interest (ROIs) and analyzed across time frames. In the in vivo study, two low-dose scans at tube currents of 25 and 50 mA were reconstructed using FBP and SIR. Quantitative perfusion metrics, namely, the normalized upslope (NUS), myocardial blood volume (MBV), and first moment transit time (FMT), were measured for two ROIs and compared to reference values obtained from a high-dose scan performed at 500 mA. Images reconstructed using FBP showed a highly nonuniform spatial distribution of noise. This spatial nonuniformity led to large fluctuations in the temporal direction. In the numerical phantom study, the level of noise was shown to vary by as much as 87% within a given image, and as much as 110% between different time frames for a ROI far from isocenter. The spatially nonuniform noise pattern was shown to correlate with the source trajectory and the object structure. In contrast, images reconstructed using SIR showed a highly uniform spatial distribution of noise, leading to smaller unexpected noise fluctuations in the temporal direction when a short scan angular range was used. In the numerical phantom study, the noise varied by less than 37% within a given image, and by less than 20% between different time frames. Also, the noise standard deviation in SIR images was on average half of that of FBP images. In the in vivo studies, the deviation observed between quantitative perfusion metrics measured from low-dose scans and high-dose scans was mitigated when SIR was used instead of FBP to reconstruct images. (1) Images reconstructed using FBP suffered from nonuniform spatial noise levels. This nonuniformity is another manifestation of the detrimental effects caused by short-scan reconstruction in CT MPI. (2) Images reconstructed using SIR had a much lower and more uniform noise level and thus can be used as a potential solution to address the FBP nonuniformity. (3) Given the improvement in the accuracy of the perfusion metrics when using SIR, it may be desirable to use a statistical reconstruction framework to perform low-dose dynamic CT MPI.

Noise spatial nonuniformity and the impact of statistical image reconstruction in CT myocardial perfusion imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lauzier, Pascal Theriault; Tang Jie; Speidel, Michael A.

Purpose: To achieve high temporal resolution in CT myocardial perfusion imaging (MPI), images are often reconstructed using filtered backprojection (FBP) algorithms from data acquired within a short-scan angular range. However, the variation in the central angle from one time frame to the next in gated short scans has been shown to create detrimental partial scan artifacts when performing quantitative MPI measurements. This study has two main purposes. (1) To demonstrate the existence of a distinct detrimental effect in short-scan FBP, i.e., the introduction of a nonuniform spatial image noise distribution; this nonuniformity can lead to unexpectedly high image noise andmore » streaking artifacts, which may affect CT MPI quantification. (2) To demonstrate that statistical image reconstruction (SIR) algorithms can be a potential solution to address the nonuniform spatial noise distribution problem and can also lead to radiation dose reduction in the context of CT MPI. Methods: Projection datasets from a numerically simulated perfusion phantom and an in vivo animal myocardial perfusion CT scan were used in this study. In the numerical phantom, multiple realizations of Poisson noise were added to projection data at each time frame to investigate the spatial distribution of noise. Images from all datasets were reconstructed using both FBP and SIR reconstruction algorithms. To quantify the spatial distribution of noise, the mean and standard deviation were measured in several regions of interest (ROIs) and analyzed across time frames. In the in vivo study, two low-dose scans at tube currents of 25 and 50 mA were reconstructed using FBP and SIR. Quantitative perfusion metrics, namely, the normalized upslope (NUS), myocardial blood volume (MBV), and first moment transit time (FMT), were measured for two ROIs and compared to reference values obtained from a high-dose scan performed at 500 mA. Results: Images reconstructed using FBP showed a highly nonuniform spatial distribution of noise. This spatial nonuniformity led to large fluctuations in the temporal direction. In the numerical phantom study, the level of noise was shown to vary by as much as 87% within a given image, and as much as 110% between different time frames for a ROI far from isocenter. The spatially nonuniform noise pattern was shown to correlate with the source trajectory and the object structure. In contrast, images reconstructed using SIR showed a highly uniform spatial distribution of noise, leading to smaller unexpected noise fluctuations in the temporal direction when a short scan angular range was used. In the numerical phantom study, the noise varied by less than 37% within a given image, and by less than 20% between different time frames. Also, the noise standard deviation in SIR images was on average half of that of FBP images. In the in vivo studies, the deviation observed between quantitative perfusion metrics measured from low-dose scans and high-dose scans was mitigated when SIR was used instead of FBP to reconstruct images. Conclusions: (1) Images reconstructed using FBP suffered from nonuniform spatial noise levels. This nonuniformity is another manifestation of the detrimental effects caused by short-scan reconstruction in CT MPI. (2) Images reconstructed using SIR had a much lower and more uniform noise level and thus can be used as a potential solution to address the FBP nonuniformity. (3) Given the improvement in the accuracy of the perfusion metrics when using SIR, it may be desirable to use a statistical reconstruction framework to perform low-dose dynamic CT MPI.« less

Noise spatial nonuniformity and the impact of statistical image reconstruction in CT myocardial perfusion imaging

PubMed Central

Lauzier, Pascal Thériault; Tang, Jie; Speidel, Michael A.; Chen, Guang-Hong

2012-01-01

Purpose: To achieve high temporal resolution in CT myocardial perfusion imaging (MPI), images are often reconstructed using filtered backprojection (FBP) algorithms from data acquired within a short-scan angular range. However, the variation in the central angle from one time frame to the next in gated short scans has been shown to create detrimental partial scan artifacts when performing quantitative MPI measurements. This study has two main purposes. (1) To demonstrate the existence of a distinct detrimental effect in short-scan FBP, i.e., the introduction of a nonuniform spatial image noise distribution; this nonuniformity can lead to unexpectedly high image noise and streaking artifacts, which may affect CT MPI quantification. (2) To demonstrate that statistical image reconstruction (SIR) algorithms can be a potential solution to address the nonuniform spatial noise distribution problem and can also lead to radiation dose reduction in the context of CT MPI. Methods: Projection datasets from a numerically simulated perfusion phantom and an in vivo animal myocardial perfusion CT scan were used in this study. In the numerical phantom, multiple realizations of Poisson noise were added to projection data at each time frame to investigate the spatial distribution of noise. Images from all datasets were reconstructed using both FBP and SIR reconstruction algorithms. To quantify the spatial distribution of noise, the mean and standard deviation were measured in several regions of interest (ROIs) and analyzed across time frames. In the in vivo study, two low-dose scans at tube currents of 25 and 50 mA were reconstructed using FBP and SIR. Quantitative perfusion metrics, namely, the normalized upslope (NUS), myocardial blood volume (MBV), and first moment transit time (FMT), were measured for two ROIs and compared to reference values obtained from a high-dose scan performed at 500 mA. Results: Images reconstructed using FBP showed a highly nonuniform spatial distribution of noise. This spatial nonuniformity led to large fluctuations in the temporal direction. In the numerical phantom study, the level of noise was shown to vary by as much as 87% within a given image, and as much as 110% between different time frames for a ROI far from isocenter. The spatially nonuniform noise pattern was shown to correlate with the source trajectory and the object structure. In contrast, images reconstructed using SIR showed a highly uniform spatial distribution of noise, leading to smaller unexpected noise fluctuations in the temporal direction when a short scan angular range was used. In the numerical phantom study, the noise varied by less than 37% within a given image, and by less than 20% between different time frames. Also, the noise standard deviation in SIR images was on average half of that of FBP images. In the in vivo studies, the deviation observed between quantitative perfusion metrics measured from low-dose scans and high-dose scans was mitigated when SIR was used instead of FBP to reconstruct images. Conclusions: (1) Images reconstructed using FBP suffered from nonuniform spatial noise levels. This nonuniformity is another manifestation of the detrimental effects caused by short-scan reconstruction in CT MPI. (2) Images reconstructed using SIR had a much lower and more uniform noise level and thus can be used as a potential solution to address the FBP nonuniformity. (3) Given the improvement in the accuracy of the perfusion metrics when using SIR, it may be desirable to use a statistical reconstruction framework to perform low-dose dynamic CT MPI. PMID:22830741

Calibrated Noise Measurements with Induced Receiver Gain Fluctuations

NASA Technical Reports Server (NTRS)

Racette, Paul; Walker, David; Gu, Dazhen; Rajola, Marco; Spevacek, Ashly

2011-01-01

The lack of well-developed techniques for modeling changing statistical moments in our observations has stymied the application of stochastic process theory in science and engineering. These limitations were encountered when modeling the performance of radiometer calibration architectures and algorithms in the presence of non stationary receiver fluctuations. Analyses of measured signals have traditionally been limited to a single measurement series. Whereas in a radiometer that samples a set of noise references, the data collection can be treated as an ensemble set of measurements of the receiver state. Noise Assisted Data Analysis is a growing field of study with significant potential for aiding the understanding and modeling of non stationary processes. Typically, NADA entails adding noise to a signal to produce an ensemble set on which statistical analysis is performed. Alternatively as in radiometric measurements, mixing a signal with calibrated noise provides, through the calibration process, the means to detect deviations from the stationary assumption and thereby a measurement tool to characterize the signal's non stationary properties. Data sets comprised of calibrated noise measurements have been limited to those collected with naturally occurring fluctuations in the radiometer receiver. To examine the application of NADA using calibrated noise, a Receiver Gain Modulation Circuit (RGMC) was designed and built to modulate the gain of a radiometer receiver using an external signal. In 2010, an RGMC was installed and operated at the National Institute of Standards and Techniques (NIST) using their Noise Figure Radiometer (NFRad) and national standard noise references. The data collected is the first known set of calibrated noise measurements from a receiver with an externally modulated gain. As an initial step, sinusoidal and step-function signals were used to modulate the receiver gain, to evaluate the circuit characteristics and to study the performance of a variety of calibration algorithms. The receiver noise temperature and time-bandwidth product of the NFRad are calculated from the data. Statistical analysis using temporal-dependent calibration algorithms reveals that the natural occurring fluctuations in the receiver are stationary over long intervals (100s of seconds); however the receiver exhibits local non stationarity over the interval over which one set of reference measurements are collected. A variety of calibration algorithms have been applied to the data to assess algorithms' performance with the gain fluctuation signals. This presentation will describe the RGMC, experiment design and a comparative analysis of calibration algorithms.

Impact of Azimuthally Controlled Fluidic Chevrons on Jet Noise

NASA Technical Reports Server (NTRS)

Henderson, Brenda S.; Norum, Thomas D.

2008-01-01

The impact of azimuthally controlled air injection on broadband shock noise and mixing noise for single and dual stream jets was investigated. The single stream experiments focused on noise reduction for low supersonic jet exhausts. Dual stream experiments included high subsonic core and fan conditions and supersonic fan conditions with transonic core conditions. For the dual stream experiments, air was injected into the core stream. Significant reductions in broadband shock noise were achieved in a single jet with an injection mass flow equal to 1.2% of the core mass flow. Injection near the pylon produced greater broadband shock noise reductions than injection at other locations around the nozzle periphery. Air injection into the core stream did not result in broadband shock noise reduction in dual stream jets. Fluidic injection resulted in some mixing noise reductions for both the single and dual stream jets. For subsonic fan and core conditions, the lowest noise levels were obtained when injecting on the side of the nozzle closest to the microphone axis.

Recent Progress in Engine Noise Reduction Technologies

NASA Technical Reports Server (NTRS)

Huff, Dennis; Gliebe, Philip

2003-01-01

Highlights from NASA-funded research over the past ten years for aircraft engine noise reduction are presented showing overall technical plans, accomplishments, and selected applications to turbofan engines. The work was sponsored by NASA's Advanced Subsonic Technology (AST) Noise Reduction Program. Emphasis is given to only the engine noise reduction research and significant accomplishments that were investigated at Technology Readiness Levels ranging from 4 to 6. The Engine Noise Reduction sub-element was divided into four work areas: source noise prediction, model scale tests, engine validation, and active noise control. Highlights from each area include technologies for higher bypass ratio turbofans, scarf inlets, forward-swept fans, swept and leaned stators, chevron/tabbed nozzles, advanced noise prediction analyses, and active noise control for fans. Finally, an industry perspective is given from General Electric Aircraft Engines showing how these technologies are being applied to commercial products. This publication contains only presentation vu-graphs from an invited lecture given at the 41st AIAA Aerospace Sciences Meeting, January 6-9, 2003.

Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images

NASA Astrophysics Data System (ADS)

Wu, Yuanfeng; Gao, Lianru; Zhang, Bing; Zhao, Haina; Li, Jun

2014-01-01

We present a parallel implementation of the optimized maximum noise fraction (G-OMNF) transform algorithm for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). The proposed approach explored the algorithm data-level concurrency and optimized the computing flow. We first defined a three-dimensional grid, in which each thread calculates a sub-block data to easily facilitate the spatial and spectral neighborhood data searches in noise estimation, which is one of the most important steps involved in OMNF. Then, we optimized the processing flow and computed the noise covariance matrix before computing the image covariance matrix to reduce the original hyperspectral image data transmission. These optimization strategies can greatly improve the computing efficiency and can be applied to other feature extraction algorithms. The proposed parallel feature extraction algorithm was implemented on an Nvidia Tesla GPU using the compute unified device architecture and basic linear algebra subroutines library. Through the experiments on several real hyperspectral images, our GPU parallel implementation provides a significant speedup of the algorithm compared with the CPU implementation, especially for highly data parallelizable and arithmetically intensive algorithm parts, such as noise estimation. In order to further evaluate the effectiveness of G-OMNF, we used two different applications: spectral unmixing and classification for evaluation. Considering the sensor scanning rate and the data acquisition time, the proposed parallel implementation met the on-board real-time feature extraction.

Image reconstruction algorithms for electrical capacitance tomography based on ROF model using new numerical techniques

NASA Astrophysics Data System (ADS)

Chen, Jiaoxuan; Zhang, Maomao; Liu, Yinyan; Chen, Jiaoliao; Li, Yi

2017-03-01

Electrical capacitance tomography (ECT) is a promising technique applied in many fields. However, the solutions for ECT are not unique and highly sensitive to the measurement noise. To remain a good shape of reconstructed object and endure a noisy data, a Rudin-Osher-Fatemi (ROF) model with total variation regularization is applied to image reconstruction in ECT. Two numerical methods, which are simplified augmented Lagrangian (SAL) and accelerated alternating direction method of multipliers (AADMM), are innovatively introduced to try to solve the above mentioned problems in ECT. The effect of the parameters and the number of iterations for different algorithms, and the noise level in capacitance data are discussed. Both simulation and experimental tests were carried out to validate the feasibility of the proposed algorithms, compared to the Landweber iteration (LI) algorithm. The results show that the SAL and AADMM algorithms can handle a high level of noise and the AADMM algorithm outperforms other algorithms in identifying the object from its background.

Selection of floating-point or fixed-point for adaptive noise canceller in somatosensory evoked potential measurement.

PubMed

Shen, Chongfei; Liu, Hongtao; Xie, Xb; Luk, Keith Dk; Hu, Yong

2007-01-01

Adaptive noise canceller (ANC) has been used to improve signal to noise ratio (SNR) of somsatosensory evoked potential (SEP). In order to efficiently apply the ANC in hardware system, fixed-point algorithm based ANC can achieve fast, cost-efficient construction, and low-power consumption in FPGA design. However, it is still questionable whether the SNR improvement performance by fixed-point algorithm is as good as that by floating-point algorithm. This study is to compare the outputs of ANC by floating-point and fixed-point algorithm ANC when it was applied to SEP signals. The selection of step-size parameter (micro) was found different in fixed-point algorithm from floating-point algorithm. In this simulation study, the outputs of fixed-point ANC showed higher distortion from real SEP signals than that of floating-point ANC. However, the difference would be decreased with increasing micro value. In the optimal selection of micro, fixed-point ANC can get as good results as floating-point algorithm.

Nonlocal variational model and filter algorithm to remove multiplicative noise

NASA Astrophysics Data System (ADS)

Chen, Dai-Qiang; Zhang, Hui; Cheng, Li-Zhi

2010-07-01

The nonlocal (NL) means filter proposed by Buades, Coll, and Morel (SIAM Multiscale Model. Simul. 4(2), 490-530, 2005), which makes full use of the redundancy information in images, has shown to be very efficient for image denoising with Gauss noise added. On the basis of the NL method and a striver to minimize the conditional mean-square error, we design a NL means filter to remove multiplicative noise, and combining the NL filter to regularity method, we propose a NL total variational (TV) model and present a fast iterated algorithm for it. Experiments demonstrate that our algorithm is better than TV method; it is superior in preserving small structures and textures and can obtain an improvement in peak signal-to-noise ratio.

Convergence analyses on on-line weight noise injection-based training algorithms for MLPs.

PubMed

Sum, John; Leung, Chi-Sing; Ho, Kevin

2012-11-01

Injecting weight noise during training is a simple technique that has been proposed for almost two decades. However, little is known about its convergence behavior. This paper studies the convergence of two weight noise injection-based training algorithms, multiplicative weight noise injection with weight decay and additive weight noise injection with weight decay. We consider that they are applied to multilayer perceptrons either with linear or sigmoid output nodes. Let w(t) be the weight vector, let V(w) be the corresponding objective function of the training algorithm, let α >; 0 be the weight decay constant, and let μ(t) be the step size. We show that if μ(t)→ 0, then with probability one E[||w(t)||2(2)] is bound and lim(t) → ∞ ||w(t)||2 exists. Based on these two properties, we show that if μ(t)→ 0, Σtμ(t)=∞, and Σtμ(t)(2) <; ∞, then with probability one these algorithms converge. Moreover, w(t) converges with probability one to a point where ∇wV(w)=0.

Mean-variance analysis of block-iterative reconstruction algorithms modeling 3D detector response in SPECT

NASA Astrophysics Data System (ADS)

Lalush, D. S.; Tsui, B. M. W.

1998-06-01

We study the statistical convergence properties of two fast iterative reconstruction algorithms, the rescaled block-iterative (RBI) and ordered subset (OS) EM algorithms, in the context of cardiac SPECT with 3D detector response modeling. The Monte Carlo method was used to generate nearly noise-free projection data modeling the effects of attenuation, detector response, and scatter from the MCAT phantom. One thousand noise realizations were generated with an average count level approximating a typical T1-201 cardiac study. Each noise realization was reconstructed using the RBI and OS algorithms for cases with and without detector response modeling. For each iteration up to twenty, we generated mean and variance images, as well as covariance images for six specific locations. Both OS and RBI converged in the mean to results that were close to the noise-free ML-EM result using the same projection model. When detector response was not modeled in the reconstruction, RBI exhibited considerably lower noise variance than OS for the same resolution. When 3D detector response was modeled, the RBI-EM provided a small improvement in the tradeoff between noise level and resolution recovery, primarily in the axial direction, while OS required about half the number of iterations of RBI to reach the same resolution. We conclude that OS is faster than RBI, but may be sensitive to errors in the projection model. Both OS-EM and RBI-EM are effective alternatives to the EVIL-EM algorithm, but noise level and speed of convergence depend on the projection model used.

Iterative image-domain decomposition for dual-energy CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niu, Tianye; Dong, Xue; Petrongolo, Michael

2014-04-15

Purpose: Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its capability of material decomposition. Direct decomposition via matrix inversion suffers from significant degradation of image signal-to-noise ratios, which reduces clinical values of DECT. Existing denoising algorithms achieve suboptimal performance since they suppress image noise either before or after the decomposition and do not fully explore the noise statistical properties of the decomposition process. In this work, the authors propose an iterative image-domain decomposition method for noise suppression in DECT, using the full variance-covariance matrix of the decomposed images. Methods: The proposed algorithm ismore » formulated in the form of least-square estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, the authors include the inverse of the estimated variance-covariance matrix of the decomposed images as the penalty weight in the least-square term. The regularization term enforces the image smoothness by calculating the square sum of neighboring pixel value differences. To retain the boundary sharpness of the decomposed images, the authors detect the edges in the CT images before decomposition. These edge pixels have small weights in the calculation of the regularization term. Distinct from the existing denoising algorithms applied on the images before or after decomposition, the method has an iterative process for noise suppression, with decomposition performed in each iteration. The authors implement the proposed algorithm using a standard conjugate gradient algorithm. The method performance is evaluated using an evaluation phantom (Catphan©600) and an anthropomorphic head phantom. The results are compared with those generated using direct matrix inversion with no noise suppression, a denoising method applied on the decomposed images, and an existing algorithm with similar formulation as the proposed method but with an edge-preserving regularization term. Results: On the Catphan phantom, the method maintains the same spatial resolution on the decomposed images as that of the CT images before decomposition (8 pairs/cm) while significantly reducing their noise standard deviation. Compared to that obtained by the direct matrix inversion, the noise standard deviation in the images decomposed by the proposed algorithm is reduced by over 98%. Without considering the noise correlation properties in the formulation, the denoising scheme degrades the spatial resolution to 6 pairs/cm for the same level of noise suppression. Compared to the edge-preserving algorithm, the method achieves better low-contrast detectability. A quantitative study is performed on the contrast-rod slice of Catphan phantom. The proposed method achieves lower electron density measurement error as compared to that by the direct matrix inversion, and significantly reduces the error variation by over 97%. On the head phantom, the method reduces the noise standard deviation of decomposed images by over 97% without blurring the sinus structures. Conclusions: The authors propose an iterative image-domain decomposition method for DECT. The method combines noise suppression and material decomposition into an iterative process and achieves both goals simultaneously. By exploring the full variance-covariance properties of the decomposed images and utilizing the edge predetection, the proposed algorithm shows superior performance on noise suppression with high image spatial resolution and low-contrast detectability.« less

Noise reduction in supersonic jets by nozzle fluidic inserts

NASA Astrophysics Data System (ADS)

Morris, Philip J.; McLaughlin, Dennis K.; Kuo, Ching-Wen

2013-08-01

Professor Philip Doak spent a very productive time as a consultant to the Lockheed-Georgia Company in the early 1970s. The focus of the overall research project was the prediction and reduction of noise from supersonic jets. Now, 40 years on, the present paper describes an innovative methodology and device for the reduction of supersonic jet noise. The goal is the development of a practical active noise reduction technique for low bypass ratio turbofan engines. This method introduces fluidic inserts installed in the divergent wall of a CD nozzle to replace hard-wall corrugation seals, which have been demonstrated to be effective by Seiner (2005) [1]. By altering the configuration and operating conditions of the fluidic inserts, active noise reduction for both mixing and shock noise has been obtained. Substantial noise reductions have been achieved for mixing noise in the maximum noise emission direction and in the forward arc for broadband shock-associated noise. To achieve these reductions (on the order of greater than 4 and 2 dB for the two main components respectively), practically achievable levels of injection mass flow rates have been used. The total injected mass flow rates are less than 4% of the core mass flow rate and the effective operating injection pressure ratio has been maintained at or below the same level as the nozzle pressure ratio of the core flow.

Comparison of Tone Mode Measurements for a Forward Swept and Baseline Rotor Fan

NASA Technical Reports Server (NTRS)

Heidelberg, Laurence J.

2003-01-01

A forward swept fan, designated the Quite High Speed Fan (QHSF), was tested in the NASA Glenn 9- by 15-foot Low Speed Wind Tunnel to investigate its noise reduction relative to a baseline fan of the same aerodynamic performance. The design objective of the QHSF was a 6 dB reduction in Effective Perceived Noise Level relative to the baseline fan at the takeoff condition. The design noise reduction was to be a result of lower levels of multiple pure tone noise due to the forward swept rotor, and lower rotor/stator interaction tone noise from a leaned stator. Although the design 6 dB reduction was observed in far-field measurements, the induct mode measurements revealed the reasons for goals. All of the noise reduction was from the blade passing tone and its harmonics and most of this was unexpectedly from rotor/strut interaction modes. The reason for large differences in rotor/strut noise sources could not be determined with certainty. The reductions in the multiple pure tone noise for the forward swept rotor were not observed. this reduction were not the ones related to the design

Segmentation of pomegranate MR images using spatial fuzzy c-means (SFCM) algorithm

NASA Astrophysics Data System (ADS)

Moradi, Ghobad; Shamsi, Mousa; Sedaaghi, M. H.; Alsharif, M. R.

2011-10-01

Segmentation is one of the fundamental issues of image processing and machine vision. It plays a prominent role in a variety of image processing applications. In this paper, one of the most important applications of image processing in MRI segmentation of pomegranate is explored. Pomegranate is a fruit with pharmacological properties such as being anti-viral and anti-cancer. Having a high quality product in hand would be critical factor in its marketing. The internal quality of the product is comprehensively important in the sorting process. The determination of qualitative features cannot be manually made. Therefore, the segmentation of the internal structures of the fruit needs to be performed as accurately as possible in presence of noise. Fuzzy c-means (FCM) algorithm is noise-sensitive and pixels with noise are classified inversely. As a solution, in this paper, the spatial FCM algorithm in pomegranate MR images' segmentation is proposed. The algorithm is performed with setting the spatial neighborhood information in FCM and modification of fuzzy membership function for each class. The segmentation algorithm results on the original and the corrupted Pomegranate MR images by Gaussian, Salt Pepper and Speckle noises show that the SFCM algorithm operates much more significantly than FCM algorithm. Also, after diverse steps of qualitative and quantitative analysis, we have concluded that the SFCM algorithm with 5×5 window size is better than the other windows.

On the precision of automated activation time estimation

NASA Technical Reports Server (NTRS)

Kaplan, D. T.; Smith, J. M.; Rosenbaum, D. S.; Cohen, R. J.

1988-01-01

We examined how the assignment of local activation times in epicardial and endocardial electrograms is affected by sampling rate, ambient signal-to-noise ratio, and sinx/x waveform interpolation. Algorithms used for the estimation of fiducial point locations included dV/dtmax, and a matched filter detection algorithm. Test signals included epicardial and endocardial electrograms overlying both normal and infarcted regions of dog myocardium. Signal-to-noise levels were adjusted by combining known data sets with white noise "colored" to match the spectral characteristics of experimentally recorded noise. For typical signal-to-noise ratios and sampling rates, the template-matching algorithm provided the greatest precision in reproducibly estimating fiducial point location, and sinx/x interpolation allowed for an additional significant improvement. With few restrictions, combining these two techniques may allow for use of digitization rates below the Nyquist rate without significant loss of precision.

Rotorcraft noise

NASA Technical Reports Server (NTRS)

Huston, R. J. (Compiler)

1982-01-01

The establishment of a realistic plan for NASA and the U.S. helicopter industry to develop a design-for-noise methodology, including plans for the identification and development of promising noise reduction technology was discussed. Topics included: noise reduction techniques, scaling laws, empirical noise prediction, psychoacoustics, and methods of developing and validing noise prediction methods.

Digital Detection and Processing of Multiple Quadrature Harmonics for EPR Spectroscopy

PubMed Central

Ahmad, R.; Som, S.; Kesselring, E.; Kuppusamy, P.; Zweier, J.L.; Potter, L.C.

2010-01-01

A quadrature digital receiver and associated signal estimation procedure are reported for L-band electron paramagnetic resonance (EPR) spectroscopy. The approach provides simultaneous acquisition and joint processing of multiple harmonics in both in-phase and out-of-phase channels. The digital receiver, based on a high-speed dual-channel analog-to-digital converter, allows direct digital down-conversion with heterodyne processing using digital capture of the microwave reference signal. Thus, the receiver avoids noise and nonlinearity associated with analog mixers. Also, the architecture allows for low-Q anti-alias filtering and does not require the sampling frequency to be time-locked to the microwave reference. A noise model applicable for arbitrary contributions of oscillator phase noise is presented, and a corresponding maximum-likelihood estimator of unknown parameters is also reported. The signal processing is applicable for Lorentzian lineshape under nonsaturating conditions. The estimation is carried out using a convergent iterative algorithm capable of jointly processing the in-phase and out-of-phase data in the presence of phase noise and unknown microwave phase. Cramér-Rao bound analysis and simulation results demonstrate a significant reduction in linewidth estimation error using quadrature detection, for both low and high values of phase noise. EPR spectroscopic data are also reported for illustration. PMID:20971667

Digital detection and processing of multiple quadrature harmonics for EPR spectroscopy.

PubMed

Ahmad, R; Som, S; Kesselring, E; Kuppusamy, P; Zweier, J L; Potter, L C

2010-12-01

A quadrature digital receiver and associated signal estimation procedure are reported for L-band electron paramagnetic resonance (EPR) spectroscopy. The approach provides simultaneous acquisition and joint processing of multiple harmonics in both in-phase and out-of-phase channels. The digital receiver, based on a high-speed dual-channel analog-to-digital converter, allows direct digital down-conversion with heterodyne processing using digital capture of the microwave reference signal. Thus, the receiver avoids noise and nonlinearity associated with analog mixers. Also, the architecture allows for low-Q anti-alias filtering and does not require the sampling frequency to be time-locked to the microwave reference. A noise model applicable for arbitrary contributions of oscillator phase noise is presented, and a corresponding maximum-likelihood estimator of unknown parameters is also reported. The signal processing is applicable for Lorentzian lineshape under nonsaturating conditions. The estimation is carried out using a convergent iterative algorithm capable of jointly processing the in-phase and out-of-phase data in the presence of phase noise and unknown microwave phase. Cramér-Rao bound analysis and simulation results demonstrate a significant reduction in linewidth estimation error using quadrature detection, for both low and high values of phase noise. EPR spectroscopic data are also reported for illustration. Copyright © 2010 Elsevier Inc. All rights reserved.

Wavelet median denoising of ultrasound images

NASA Astrophysics Data System (ADS)

Macey, Katherine E.; Page, Wyatt H.

2002-05-01

Ultrasound images are contaminated with both additive and multiplicative noise, which is modeled by Gaussian and speckle noise respectively. Distinguishing small features such as fallopian tubes in the female genital tract in the noisy environment is problematic. A new method for noise reduction, Wavelet Median Denoising, is presented. Wavelet Median Denoising consists of performing a standard noise reduction technique, median filtering, in the wavelet domain. The new method is tested on 126 images, comprised of 9 original images each with 14 levels of Gaussian or speckle noise. Results for both separable and non-separable wavelets are evaluated, relative to soft-thresholding in the wavelet domain, using the signal-to-noise ratio and subjective assessment. The performance of Wavelet Median Denoising is comparable to that of soft-thresholding. Both methods are more successful in removing Gaussian noise than speckle noise. Wavelet Median Denoising outperforms soft-thresholding for a larger number of cases of speckle noise reduction than of Gaussian noise reduction. Noise reduction is more successful using non-separable wavelets than separable wavelets. When both methods are applied to ultrasound images obtained from a phantom of the female genital tract a small improvement is seen; however, a substantial improvement is required prior to clinical use.

Noise, Turbulence, and Thrust of Subsonic Free Jets from Lobed Nozzles

NASA Technical Reports Server (NTRS)

Zaman, K. B. M. Q.; Wang, F. Y.

2002-01-01

A study of noise benefit, vis-a-vis thrust penalty, and its correlation to turbulence intensities was conducted for free jets issuing from lobed nozzles. Four convergent nozzles with constant exit area were used in the experiments. Three of these were of rectangular lobed configuration having six, ten and fourteen lobes; the fourth was a circular nozzle. Increasing the number of lobes resulted in a progressive reduction in the turbulence intensities as well as in the overall radiated noise. The noise reduction was pronounced at the low frequency end of the spectrum. However, there was an increase in the high frequency noise that rendered the overall benefit less attractive when compared on a scaled-up A-weighted basis. A reduction in noise was accompanied by a commensurate reduction in the turbulent kinetic energy in the flow field. As expected, increasing the number of lobes involved progressive reduction in the thrust coefficient. Among the cases studied, the six-lobed nozzle had the optimum reduction in turbulence and noise with the least thrust penalty.

A new algorithm for ECG interference removal from single channel EMG recording.

PubMed

Yazdani, Shayan; Azghani, Mahmood Reza; Sedaaghi, Mohammad Hossein

2017-09-01

This paper presents a new method to remove electrocardiogram (ECG) interference from electromyogram (EMG). This interference occurs during the EMG acquisition from trunk muscles. The proposed algorithm employs progressive image denoising (PID) algorithm and ensembles empirical mode decomposition (EEMD) to remove this type of interference. PID is a very recent method that is being used for denoising digital images mixed with white Gaussian noise. It detects white Gaussian noise by deterministic annealing. To the best of our knowledge, PID has never been used before, in the case of EMG and ECG separation or in other 1D signal denoising applications. We have used it according to this fact that amplitude of the EMG signal can be modeled as white Gaussian noise using a filter with time-variant properties. The proposed algorithm has been compared to the other well-known methods such as HPF, EEMD-ICA, Wavelet-ICA and PID. The results show that the proposed algorithm outperforms the others, on the basis of three evaluation criteria used in this paper: Normalized mean square error, Signal to noise ratio and Pearson correlation.

Complex-based OCT angiography algorithm recovers microvascular information better than amplitude- or phase-based algorithms in phase-stable systems

NASA Astrophysics Data System (ADS)

Xu, Jingjiang; Song, Shaozhen; Li, Yuandong; Wang, Ruikang K.

2018-01-01

Optical coherence tomography angiography (OCTA) is increasingly becoming a popular inspection tool for biomedical imaging applications. By exploring the amplitude, phase and complex information available in OCT signals, numerous algorithms have been proposed that contrast functional vessel networks within microcirculatory tissue beds. However, it is not clear which algorithm delivers optimal imaging performance. Here, we investigate systematically how amplitude and phase information have an impact on the OCTA imaging performance, to establish the relationship of amplitude and phase stability with OCT signal-to-noise ratio (SNR), time interval and particle dynamics. With either repeated A-scan or repeated B-scan imaging protocols, the amplitude noise increases with the increase of OCT SNR; however, the phase noise does the opposite, i.e. it increases with the decrease of OCT SNR. Coupled with experimental measurements, we utilize a simple Monte Carlo (MC) model to simulate the performance of amplitude-, phase- and complex-based algorithms for OCTA imaging, the results of which suggest that complex-based algorithms deliver the best performance when the phase noise is  <  ~40 mrad. We also conduct a series of in vivo vascular imaging in animal models and human retina to verify the findings from the MC model through assessing the OCTA performance metrics of vessel connectivity, image SNR and contrast-to-noise ratio. We show that for all the metrics assessed, the complex-based algorithm delivers better performance than either the amplitude- or phase-based algorithms for both the repeated A-scan and the B-scan imaging protocols, which agrees well with the conclusion drawn from the MC simulations.

Complex-based OCT angiography algorithm recovers microvascular information better than amplitude- or phase-based algorithms in phase-stable systems.

PubMed

Xu, Jingjiang; Song, Shaozhen; Li, Yuandong; Wang, Ruikang K

2017-12-19

Optical coherence tomography angiography (OCTA) is increasingly becoming a popular inspection tool for biomedical imaging applications. By exploring the amplitude, phase and complex information available in OCT signals, numerous algorithms have been proposed that contrast functional vessel networks within microcirculatory tissue beds. However, it is not clear which algorithm delivers optimal imaging performance. Here, we investigate systematically how amplitude and phase information have an impact on the OCTA imaging performance, to establish the relationship of amplitude and phase stability with OCT signal-to-noise ratio (SNR), time interval and particle dynamics. With either repeated A-scan or repeated B-scan imaging protocols, the amplitude noise increases with the increase of OCT SNR; however, the phase noise does the opposite, i.e. it increases with the decrease of OCT SNR. Coupled with experimental measurements, we utilize a simple Monte Carlo (MC) model to simulate the performance of amplitude-, phase- and complex-based algorithms for OCTA imaging, the results of which suggest that complex-based algorithms deliver the best performance when the phase noise is  <  ~40 mrad. We also conduct a series of in vivo vascular imaging in animal models and human retina to verify the findings from the MC model through assessing the OCTA performance metrics of vessel connectivity, image SNR and contrast-to-noise ratio. We show that for all the metrics assessed, the complex-based algorithm delivers better performance than either the amplitude- or phase-based algorithms for both the repeated A-scan and the B-scan imaging protocols, which agrees well with the conclusion drawn from the MC simulations.

Characteristics of noise-canceling headphones to reduce the hearing hazard for MP3 users.

PubMed

Liang, Maojin; Zhao, Fei; French, David; Zheng, Yiqing

2012-06-01

Three pairs of headphones [standard iPod ear buds and two noise-canceling headphones (NCHs)] were chosen to investigate frequency characteristics of noise reduction, together with their attenuation effects on preferred listening levels (PLLs) in the presence of various types of background noise. Twenty-six subjects with normal hearing chose their PLLs in quiet, street noise, and subway noise using the three headphones and with the noise-canceling system on/off. Both sets of NCHs reduced noise levels at mid- and high-frequencies. Further noise reductions occurred in low frequencies with the noise canceling system switched on. In street noise, both NCHs had similar noise reduction effects. In subway noise, better noise reduction effects were found in the expensive NCH and with noise-canceling on. A two way repeated measures analysis of variance showed that both listening conditions and headphone styles were significant influencing factors on the PLLs. Subjects tended to increase their PLLs as the background noise level increased. Compared with ear buds, PLLs obtained from NCHs-on in the presence of background noise were reduced up to 4 dB. Therefore, proper selection and use of NCHs appears beneficial in reducing the risk of hearing damage caused by high music listening levels in the presence of background noise.

Application of based on improved wavelet algorithm in fiber temperature sensor

NASA Astrophysics Data System (ADS)

Qi, Hui; Tang, Wenjuan

2018-03-01

It is crucial point that accurate temperature in distributed optical fiber temperature sensor. In order to solve the problem of temperature measurement error due to weak Raman scattering signal and strong noise in system, a new based on improved wavelet algorithm is presented. On the basis of the traditional modulus maxima wavelet algorithm, signal correlation is considered to improve the ability to capture signals and noise, meanwhile, combined with wavelet decomposition scale adaptive method to eliminate signal loss or noise not filtered due to mismatch scale. Superiority of algorithm filtering is compared with others by Matlab. At last, the 3km distributed optical fiber temperature sensing system is used for verification. Experimental results show that accuracy of temperature generally increased by 0.5233.

Digitally enhanced homodyne interferometry.

PubMed

Sutton, Andrew J; Gerberding, Oliver; Heinzel, Gerhard; Shaddock, Daniel A

2012-09-24

We present two variations of a novel interferometry technique capable of simultaneously measuring multiple targets with high sensitivity. The technique performs a homodyne phase measurement by application of a four point phase shifting algorithm, with pseudo-random switching between points to allow multiplexed measurement based upon propagation delay alone. By multiplexing measurements and shifting complexity into signal processing, both variants realise significant complexity reductions over comparable methods. The first variant performs a typical coherent detection with a dedicated reference field and achieves a displacement noise floor 0.8 pm/√Hz above 50 Hz. The second allows for removal of the dedicated reference, resulting in further simplifications and improved low frequency performance with a 1 pm/√Hz noise floor measured down to 20 Hz. These results represent the most sensitive measurement performed using this style of interferometry whilst simultaneously reducing the electro-optic footprint.

A theoretical investigation of noise reduction through the cylindrical fuselage of a twin-engine, propeller-driven aircraft

NASA Technical Reports Server (NTRS)

Bhat, R. B.; Mixson, J. S.

1978-01-01

Interior noise in the fuselage of a twin-engine, propeller-driven aircraft with two propellers rotating in opposite directions is studied analytically. The fuselage was modeled as a stiffened cylindrical shell with simply supported ends, and the effects of stringers and frames were averaged over the shell surface. An approximate mathematical model of the propeller noise excitation was formulated which includes some of the propeller noise characteristics such as sweeping pressure waves around the sidewalls due to propeller rotation and the localized nature of the excitation with the highest levels near the propeller plane. Results are presented in the form of noise reduction, which is the difference between the levels of external and interior noise. The influence of propeller noise characteristics on the noise reduction was studied. The results indicate that the sweep velocity of the excitation around the fuselage sidewalls is critical to noise reduction.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leng, Shuai; Yu, Lifeng; Wang, Jia

Purpose: Our purpose was to reduce image noise in spectral CT by exploiting data redundancies in the energy domain to allow flexible selection of the number, width, and location of the energy bins. Methods: Using a variety of spectral CT imaging methods, conventional filtered backprojection (FBP) reconstructions were performed and resulting images were compared to those processed using a Local HighlY constrained backPRojection Reconstruction (HYPR-LR) algorithm. The mean and standard deviation of CT numbers were measured within regions of interest (ROIs), and results were compared between FBP and HYPR-LR. For these comparisons, the following spectral CT imaging methods were used:(i)more » numerical simulations based on a photon-counting, detector-based CT system, (ii) a photon-counting, detector-based micro CT system using rubidium and potassium chloride solutions, (iii) a commercial CT system equipped with integrating detectors utilizing tube potentials of 80, 100, 120, and 140 kV, and (iv) a clinical dual-energy CT examination. The effects of tube energy and energy bin width were evaluated appropriate to each CT system. Results: The mean CT number in each ROI was unchanged between FBP and HYPR-LR images for each of the spectral CT imaging scenarios, irrespective of bin width or tube potential. However, image noise, as represented by the standard deviation of CT numbers in each ROI, was reduced by 36%-76%. In all scenarios, image noise after HYPR-LR algorithm was similar to that of composite images, which used all available photons. No difference in spatial resolution was observed between HYPR-LR processing and FBP. Dual energy patient data processed using HYPR-LR demonstrated reduced noise in the individual, low- and high-energy images, as well as in the material-specific basis images. Conclusions: Noise reduction can be accomplished for spectral CT by exploiting data redundancies in the energy domain. HYPR-LR is a robust method for reducing image noise in a variety of spectral CT imaging systems without losing spatial resolution or CT number accuracy. This method improves the flexibility to select energy bins in the manner that optimizes material identification and separation without paying the penalty of increased image noise or its corollary, increased patient dose.« less

Toward artifact-free data in Hadamard transform-based double multiplexing of ion mobility-Orbitrap mass spectrometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tummalacherla, Meghasyam; Garimella, Sandilya V. B.; Prost, Spencer A.

The integration of ion mobility spectrometry (IMS) with trap-based mass spectrometer (MS) such as Orbitrap using the dual gate approach suffers from low duty cycle. Efforts to improve the duty cycle involve the utilization of Hadamard transform based double multiplexing which significantly improve the signal to noise ratio and duty cycle of the ion mobility – Orbitrap mass spectrometry (IM – Orbitrap MS) platform. However, significant fluctuations in ion intensity and the temporal shifts in the encoded data give rise to artifacts and noise in the demultiplexed data which significantly reduce the data quality and negate the benefits of multiplexing.more » We propose a new approach that identifies the true IM peaks and helps in eliminating the artifacts in the demultiplexed data leading to a decrease in false positives in subsequent data processing. The algorithm takes an analytical approach to first identify the position of the IM peak in the temporal domain, and then demultiplex and identify the true data making it easier for subsequent data processing. After the application of the algorithm, the quality of the IM-Orbitrap MS measurements was greatly improved because of the reduction in artifacts.« less

Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation.

PubMed

Zana, F; Klein, J C

2001-01-01

This paper presents an algorithm based on mathematical morphology and curvature evaluation for the detection of vessel-like patterns in a noisy environment. Such patterns are very common in medical images. Vessel detection is interesting for the computation of parameters related to blood flow. Its tree-like geometry makes it a usable feature for registration between images that can be of a different nature. In order to define vessel-like patterns, segmentation is performed with respect to a precise model. We define a vessel as a bright pattern, piece-wise connected, and locally linear, mathematical morphology is very well adapted to this description, however other patterns fit such a morphological description. In order to differentiate vessels from analogous background patterns, a cross-curvature evaluation is performed. They are separated out as they have a specific Gaussian-like profile whose curvature varies smoothly along the vessel. The detection algorithm that derives directly from this modeling is based on four steps: (1) noise reduction; (2) linear pattern with Gaussian-like profile improvement; (3) cross-curvature evaluation; (4) linear filtering. We present its theoretical background and illustrate it on real images of various natures, then evaluate its robustness and its accuracy with respect to noise.

Motion artifact removal algorithm by ICA for e-bra: a women ECG measurement system

NASA Astrophysics Data System (ADS)

Kwon, Hyeokjun; Oh, Sechang; Varadan, Vijay K.

2013-04-01

Wearable ECG(ElectroCardioGram) measurement systems have increasingly been developing for people who suffer from CVD(CardioVascular Disease) and have very active lifestyles. Especially, in the case of female CVD patients, several abnormal CVD symptoms are accompanied with CVDs. Therefore, monitoring women's ECG signal is a significant diagnostic method to prevent from sudden heart attack. The E-bra ECG measurement system from our previous work provides more convenient option for women than Holter monitor system. The e-bra system was developed with a motion artifact removal algorithm by using an adaptive filter with LMS(least mean square) and a wandering noise baseline detection algorithm. In this paper, ICA(independent component analysis) algorithms are suggested to remove motion artifact factor for the e-bra system. Firstly, the ICA algorithms are developed with two kinds of statistical theories: Kurtosis, Endropy and evaluated by performing simulations with a ECG signal created by sgolayfilt function of MATLAB, a noise signal including 0.4Hz, 1.1Hz and 1.9Hz, and a weighed vector W estimated by kurtosis or entropy. A correlation value is shown as the degree of similarity between the created ECG signal and the estimated new ECG signal. In the real time E-Bra system, two pseudo signals are extracted by multiplying with a random weighted vector W, the measured ECG signal from E-bra system, and the noise component signal by noise extraction algorithm from our previous work. The suggested ICA algorithm basing on kurtosis or entropy is used to estimate the new ECG signal Y without noise component.

Development of gradient descent adaptive algorithms to remove common mode artifact for improvement of cardiovascular signal quality.

PubMed

Ciaccio, Edward J; Micheli-Tzanakou, Evangelia

2007-07-01

Common-mode noise degrades cardiovascular signal quality and diminishes measurement accuracy. Filtering to remove noise components in the frequency domain often distorts the signal. Two adaptive noise canceling (ANC) algorithms were tested to adjust weighted reference signals for optimal subtraction from a primary signal. Update of weight w was based upon the gradient term of the steepest descent equation: [see text], where the error epsilon is the difference between primary and weighted reference signals. nabla was estimated from Deltaepsilon(2) and Deltaw without using a variable Deltaw in the denominator which can cause instability. The Parallel Comparison (PC) algorithm computed Deltaepsilon(2) using fixed finite differences +/- Deltaw in parallel during each discrete time k. The ALOPEX algorithm computed Deltaepsilon(2)x Deltaw from time k to k + 1 to estimate nabla, with a random number added to account for Deltaepsilon(2) . Deltaw--> 0 near the optimal weighting. Using simulated data, both algorithms stably converged to the optimal weighting within 50-2000 discrete sample points k even with a SNR = 1:8 and weights which were initialized far from the optimal. Using a sharply pulsatile cardiac electrogram signal with added noise so that the SNR = 1:5, both algorithms exhibited stable convergence within 100 ms (100 sample points). Fourier spectral analysis revealed minimal distortion when comparing the signal without added noise to the ANC restored signal. ANC algorithms based upon difference calculations can rapidly and stably converge to the optimal weighting in simulated and real cardiovascular data. Signal quality is restored with minimal distortion, increasing the accuracy of biophysical measurement.

Measurement of pattern roughness and local size variation using CD-SEM: current status

NASA Astrophysics Data System (ADS)

Fukuda, Hiroshi; Kawasaki, Takahiro; Kawada, Hiroki; Sakai, Kei; Kato, Takashi; Yamaguchi, Satoru; Ikota, Masami; Momonoi, Yoshinori

2018-03-01

Measurement of line edge roughness (LER) is discussed from four aspects: edge detection, PSD prediction, sampling strategy, and noise mitigation, and general guidelines and practical solutions for LER measurement today are introduced. Advanced edge detection algorithms such as wave-matching method are shown effective for robustly detecting edges from low SNR images, while conventional algorithm with weak filtering is still effective in suppressing SEM noise and aliasing. Advanced PSD prediction method such as multi-taper method is effective in suppressing sampling noise within a line edge to analyze, while number of lines is still required for suppressing line to line variation. Two types of SEM noise mitigation methods, "apparent noise floor" subtraction method and LER-noise decomposition using regression analysis are verified to successfully mitigate SEM noise from PSD curves. These results are extended to LCDU measurement to clarify the impact of SEM noise and sampling noise on LCDU.

Scatter and beam hardening reduction in industrial computed tomography using photon counting detectors

NASA Astrophysics Data System (ADS)

Schumacher, David; Sharma, Ravi; Grager, Jan-Carl; Schrapp, Michael

2018-07-01

Photon counting detectors (PCD) offer new possibilities for x-ray micro computed tomography (CT) in the field of non-destructive testing. For large and/or dense objects with high atomic numbers the problem of scattered radiation and beam hardening severely influences the image quality. This work shows that using an energy discriminating PCD based on CdTe allows to address these problems by intrinsically reducing both the influence of scattering and beam hardening. Based on 2D-radiographic measurements it is shown that by energy thresholding the influence of scattered radiation can be reduced by up to in case of a PCD compared to a conventional energy-integrating detector (EID). To demonstrate the capabilities of a PCD in reducing beam hardening, cupping artefacts are analyzed quantitatively. The PCD results show that the higher the energy threshold is set, the lower the cupping effect emerges. But since numerous beam hardening correction algorithms exist, the results of the PCD are compared to EID results corrected by common techniques. Nevertheless, the highest energy thresholds yield lower cupping artefacts than any of the applied correction algorithms. As an example of a potential industrial CT application, a turbine blade is investigated by CT. The inner structure of the turbine blade allows for comparing the image quality between PCD and EID in terms of absolute contrast, as well as normalized signal-to-noise and contrast-to-noise ratio. Where the absolute contrast can be improved by raising the energy thresholds of the PCD, it is found that due to lower statistics the normalized contrast-to-noise-ratio could not be improved compared to the EID. These results might change to the contrary when discarding pre-filtering of the x-ray spectra and thus allowing more low-energy photons to reach the detectors. Despite still being in the early phase in technological progress, PCDs already allow to improve CT image quality compared to conventional detectors in terms of scatter and beam hardening reduction.

High frame rate imaging based photometry. Photometric reduction of data from electron-multiplying charge coupled devices (EMCCDs)

NASA Astrophysics Data System (ADS)

Harpsøe, K. B. W.; Jørgensen, U. G.; Andersen, M. I.; Grundahl, F.

2012-06-01

Context. The EMCCD is a type of CCD that delivers fast readout times and negligible readout noise, making it an ideal detector for high frame rate applications which improve resolution, like lucky imaging or shift-and-add. This improvement in resolution can potentially improve the photometry of faint stars in extremely crowded fields significantly by alleviating crowding. Alleviating crowding is a prerequisite for observing gravitational microlensing in main sequence stars towards the galactic bulge. However, the photometric stability of this device has not been assessed. The EMCCD has sources of noise not found in conventional CCDs, and new methods for handling these must be developed. Aims: We aim to investigate how the normal photometric reduction steps from conventional CCDs should be adjusted to be applicable to EMCCD data. One complication is that a bias frame cannot be obtained conventionally, as the output from an EMCCD is not normally distributed. Also, the readout process generates spurious charges in any CCD, but in EMCCD data, these charges are visible as opposed to the conventional CCD. Furthermore we aim to eliminate the photon waste associated with lucky imaging by combining this method with shift-and-add. Methods: A simple probabilistic model for the dark output of an EMCCD is developed. Fitting this model with the expectation-maximization algorithm allows us to estimate the bias, readout noise, amplification, and spurious charge rate per pixel and thus correct for these phenomena. To investigate the stability of the photometry, corrected frames of a crowded field are reduced with a point spread function (PSF) fitting photometry package, where a lucky image is used as a reference. Results: We find that it is possible to develop an algorithm that elegantly reduces EMCCD data and produces stable photometry at the 1% level in an extremely crowded field. Based on observation with the Danish 1.54 m telescope at ESO La Silla Observatory.

Effects of quantum noise in 4D-CT on deformable image registration and derived ventilation data

NASA Astrophysics Data System (ADS)

Latifi, Kujtim; Huang, Tzung-Chi; Feygelman, Vladimir; Budzevich, Mikalai M.; Moros, Eduardo G.; Dilling, Thomas J.; Stevens, Craig W.; van Elmpt, Wouter; Dekker, Andre; Zhang, Geoffrey G.

2013-11-01

Quantum noise is common in CT images and is a persistent problem in accurate ventilation imaging using 4D-CT and deformable image registration (DIR). This study focuses on the effects of noise in 4D-CT on DIR and thereby derived ventilation data. A total of six sets of 4D-CT data with landmarks delineated in different phases, called point-validated pixel-based breathing thorax models (POPI), were used in this study. The DIR algorithms, including diffeomorphic morphons (DM), diffeomorphic demons (DD), optical flow and B-spline, were used to register the inspiration phase to the expiration phase. The DIR deformation matrices (DIRDM) were used to map the landmarks. Target registration errors (TRE) were calculated as the distance errors between the delineated and the mapped landmarks. Noise of Gaussian distribution with different standard deviations (SD), from 0 to 200 Hounsfield Units (HU) in amplitude, was added to the POPI models to simulate different levels of quantum noise. Ventilation data were calculated using the ΔV algorithm which calculates the volume change geometrically based on the DIRDM. The ventilation images with different added noise levels were compared using Dice similarity coefficient (DSC). The root mean square (RMS) values of the landmark TRE over the six POPI models for the four DIR algorithms were stable when the noise level was low (SD <150 HU) and increased with added noise when the level is higher. The most accurate DIR was DD with a mean RMS of 1.5 ± 0.5 mm with no added noise and 1.8 ± 0.5 mm with noise (SD = 200 HU). The DSC values between the ventilation images with and without added noise decreased with the noise level, even when the noise level was relatively low. The DIR algorithm most robust with respect to noise was DM, with mean DSC = 0.89 ± 0.01 and 0.66 ± 0.02 for the top 50% ventilation volumes, as compared between 0 added noise and SD = 30 and 200 HU, respectively. Although the landmark TRE were stable with low noise, the differences between ventilation images increased with noise level, even when the noise was low, indicating ventilation imaging from 4D-CT was sensitive to image noise. Therefore, high quality 4D-CT is essential for accurate ventilation images.

An investigation of the effects of measurement noise in the use of instantaneous angular speed for machine diagnosis

NASA Astrophysics Data System (ADS)

Gu, Fengshou; Yesilyurt, Isa; Li, Yuhua; Harris, Georgina; Ball, Andrew

2006-08-01

In order to discriminate small changes for early fault diagnosis of rotating machines, condition monitoring demands that the measurement of instantaneous angular speed (IAS) of the machines be as accurate as possible. This paper develops the theoretical basis and practical implementation of IAS data acquisition and IAS estimation when noise influence is included. IAS data is modelled as a frequency modulated signal of which the signal-to-noise ratio can be improved by using a high-resolution encoder. From this signal model and analysis, optimal configurations for IAS data collection are addressed for high accuracy IAS measurement. Simultaneously, a method based on analytic signal concept and fast Fourier transform is also developed for efficient and accurate estimation of IAS. Finally, a fault diagnosis is carried out on an electric induction motor driving system using IAS measurement. The diagnosis results show that using a high-resolution encoder and a long data stream can achieve noise reduction by more than 10 dB in the frequency range of interest, validating the model and algorithm developed. Moreover, the results demonstrate that IAS measurement outperforms conventional vibration in diagnosis of incipient faults of motor rotor bar defects and shaft misalignment.

A 256×256 low-light-level CMOS imaging sensor with digital CDS

NASA Astrophysics Data System (ADS)

Zou, Mei; Chen, Nan; Zhong, Shengyou; Li, Zhengfen; Zhang, Jicun; Yao, Li-bin

2016-10-01

In order to achieve high sensitivity for low-light-level CMOS image sensors (CIS), a capacitive transimpedance amplifier (CTIA) pixel circuit with a small integration capacitor is used. As the pixel and the column area are highly constrained, it is difficult to achieve analog correlated double sampling (CDS) to remove the noise for low-light-level CIS. So a digital CDS is adopted, which realizes the subtraction algorithm between the reset signal and pixel signal off-chip. The pixel reset noise and part of the column fixed-pattern noise (FPN) can be greatly reduced. A 256×256 CIS with CTIA array and digital CDS is implemented in the 0.35μm CMOS technology. The chip size is 7.7mm×6.75mm, and the pixel size is 15μm×15μm with a fill factor of 20.6%. The measured pixel noise is 24LSB with digital CDS in RMS value at dark condition, which shows 7.8× reduction compared to the image sensor without digital CDS. Running at 7fps, this low-light-level CIS can capture recognizable images with the illumination down to 0.1lux.

Development of a two wheeled self balancing robot with speech recognition and navigation algorithm

NASA Astrophysics Data System (ADS)

Rahman, Md. Muhaimin; Ashik-E-Rasul, Haq, Nowab. Md. Aminul; Hassan, Mehedi; Hasib, Irfan Mohammad Al; Hassan, K. M. Rafidh

2016-07-01

This paper is aimed to discuss modeling, construction and development of navigation algorithm of a two wheeled self balancing mobile robot in an enclosure. In this paper, we have discussed the design of two of the main controller algorithms, namely PID algorithms, on the robot model. Simulation is performed in the SIMULINK environment. The controller is developed primarily for self-balancing of the robot and also it's positioning. As for the navigation in an enclosure, template matching algorithm is proposed for precise measurement of the robot position. The navigation system needs to be calibrated before navigation process starts. Almost all of the earlier template matching algorithms that can be found in the open literature can only trace the robot. But the proposed algorithm here can also locate the position of other objects in an enclosure, like furniture, tables etc. This will enable the robot to know the exact location of every stationary object in the enclosure. Moreover, some additional features, such as Speech Recognition and Object Detection, are added. For Object Detection, the single board Computer Raspberry Pi is used. The system is programmed to analyze images captured via the camera, which are then processed through background subtraction, followed by active noise reduction.

Jet Noise Reduction by Microjets - A Parametric Study

NASA Technical Reports Server (NTRS)

Zaman, K. B. M. Q.

2010-01-01

The effect of injecting tiny secondary jets (microjets ) on the radiated noise from a subsonic primary jet is studied experimentally. The microjets are injected on to the primary jet near the nozzle exit with variable port geometry, working fluid and driving pressure. A clear noise reduction is observed that improves with increasing jet pressure. It is found that smaller diameter ports with higher driving pressure, but involving less thrust and mass fraction, can produce better noise reduction. A collection of data from the present as well as past experiments is examined in an attempt to correlate the noise reduction with the operating parameters. The results indicate that turbulent mixing noise reduction, as monitored by OASPL at a shallow angle, correlates with the ratio of jet to primary jet driving pressures normalized by the ratio of corresponding diameters (p d /pjD). With gaseous injection, the spectral amplitudes decrease at lower frequencies while an increase is noted at higher frequencies. It is apparent that this amplitude crossover is at least partly due to shock-associated noise from the underexpanded jets themselves. Such crossover is not seen with water injection since the flow in that case is incompressible and there is no shock-associated noise. Centerline velocity data show that larger noise reduction is accompanied by faster jet decay as well as significant reduction in turbulence intensities. While a physical understanding of the dependence of noise reduction on p d /pjD remains unclear, given this correlation, an analysis explains the observed dependence of the effect on various other parameters.

Notched audiograms and noise exposure history in older adults.

PubMed

Nondahl, David M; Shi, Xiaoyu; Cruickshanks, Karen J; Dalton, Dayna S; Tweed, Ted S; Wiley, Terry L; Carmichael, Lakeesha L

2009-12-01

Using data from a population-based cohort study, we compared four published algorithms for identifying notched audiograms and compared their resulting classifications with noise exposure history. Four algorithms: (1) , (2) , (3) , and (4) were used to identify notched audiograms. Audiometric evaluations were collected as a part of the 10-yr follow-up examinations of the Epidemiology of Hearing Loss Study, in Beaver Dam, WI (2003-2005, N = 2395). Detailed noise exposure histories were collected by interview at the baseline examination (1993-1995) and updated at subsequent visits. An extensive history of occupational noise exposure, participation in noisy hobbies, and firearm usage was used to evaluate consistency of the notch classifications with the history of noise exposure. The prevalence of notched audiograms varied greatly by definition (31.7, 25.9, 47.2, and 11.7% for methods 1, 2, 3, and 4, respectively). In this cohort, a history of noise exposure was common (56.2% for occupational noise, 71.7% for noisy hobbies, 13.4% for firearms, and 81.2% for any of these three sources). Among participants with a notched audiogram, almost one-third did not have a history of occupational noise exposure (31.4, 33.0, 32.5, and 28.1% for methods 1, 2, 3, and 4, respectively), and approximately 11% did not have a history of exposure to any of the three sources of noise (11.5, 13.6, 10.3, and 7.6%). Discordance was greater in women than in men. These results suggest that there is a poor agreement across existing algorithms for audiometric notches. In addition, notches can occur in the absence of a positive noise history. In the absence of an objective consensus definition of a notched audiogram and in light of the degree of discordance in women between noise history and notches by each of these algorithms, researchers should be cautious about classifying noise-induced hearing loss by notched audiograms.

SU-E-J-110: A Novel Level Set Active Contour Algorithm for Multimodality Joint Segmentation/Registration Using the Jensen-Rényi Divergence.

PubMed

Markel, D; Naqa, I El; Freeman, C; Vallières, M

2012-06-01

To present a novel joint segmentation/registration for multimodality image-guided and adaptive radiotherapy. A major challenge to this framework is the sensitivity of many segmentation or registration algorithms to noise. Presented is a level set active contour based on the Jensen-Renyi (JR) divergence to achieve improved noise robustness in a multi-modality imaging space. To present a novel joint segmentation/registration for multimodality image-guided and adaptive radiotherapy. A major challenge to this framework is the sensitivity of many segmentation or registration algorithms to noise. Presented is a level set active contour based on the Jensen-Renyi (JR) divergence to achieve improved noise robustness in a multi-modality imaging space. It was found that JR divergence when used for segmentation has an improved robustness to noise compared to using mutual information, or other entropy-based metrics. The MI metric failed at around 2/3 the noise power than the JR divergence. The JR divergence metric is useful for the task of joint segmentation/registration of multimodality images and shows improved results compared entropy based metric. The algorithm can be easily modified to incorporate non-intensity based images, which would allow applications into multi-modality and texture analysis. © 2012 American Association of Physicists in Medicine.

Robust radio interferometric calibration using the t-distribution

NASA Astrophysics Data System (ADS)

Kazemi, S.; Yatawatta, S.

2013-10-01

A major stage of radio interferometric data processing is calibration or the estimation of systematic errors in the data and the correction for such errors. A stochastic error (noise) model is assumed, and in most cases, this underlying model is assumed to be Gaussian. However, outliers in the data due to interference or due to errors in the sky model would have adverse effects on processing based on a Gaussian noise model. Most of the shortcomings of calibration such as the loss in flux or coherence, and the appearance of spurious sources, could be attributed to the deviations of the underlying noise model. In this paper, we propose to improve the robustness of calibration by using a noise model based on Student's t-distribution. Student's t-noise is a special case of Gaussian noise when the variance is unknown. Unlike Gaussian-noise-model-based calibration, traditional least-squares minimization would not directly extend to a case when we have a Student's t-noise model. Therefore, we use a variant of the expectation-maximization algorithm, called the expectation-conditional maximization either algorithm, when we have a Student's t-noise model and use the Levenberg-Marquardt algorithm in the maximization step. We give simulation results to show the robustness of the proposed calibration method as opposed to traditional Gaussian-noise-model-based calibration, especially in preserving the flux of weaker sources that are not included in the calibration model.

Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm

NASA Astrophysics Data System (ADS)

Mozaffarzadeh, Moein; Mahloojifar, Ali; Orooji, Mahdi; Kratkiewicz, Karl; Adabi, Saba; Nasiriavanaki, Mohammadreza

2018-02-01

In photoacoustic imaging, delay-and-sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely delay-multiply-and-sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a beamformer is introduced using minimum variance (MV) adaptive beamforming combined with DMAS, so-called minimum variance-based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45 mm MVB-DMAS results in about 31, 18, and 8 dB sidelobes reduction compared to DAS, MV, and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96%, 94%, and 45% and signal-to-noise ratio about 89%, 15%, and 35% compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33 mm of the experimental images, MVB-DMAS results in about 20 dB sidelobes reduction in comparison with other beamformers.

A Trainable Hearing Aid Algorithm Reflecting Individual Preferences for Degree of Noise-Suppression, Input Sound Level, and Listening Situation.

PubMed

Yoon, Sung Hoon; Nam, Kyoung Won; Yook, Sunhyun; Cho, Baek Hwan; Jang, Dong Pyo; Hong, Sung Hwa; Kim, In Young

2017-03-01

In an effort to improve hearing aid users' satisfaction, recent studies on trainable hearing aids have attempted to implement one or two environmental factors into training. However, it would be more beneficial to train the device based on the owner's personal preferences in a more expanded environmental acoustic conditions. Our study aimed at developing a trainable hearing aid algorithm that can reflect the user's individual preferences in a more extensive environmental acoustic conditions (ambient sound level, listening situation, and degree of noise suppression) and evaluated the perceptual benefit of the proposed algorithm. Ten normal hearing subjects participated in this study. Each subjects trained the algorithm to their personal preference and the trained data was used to record test sounds in three different settings to be utilized to evaluate the perceptual benefit of the proposed algorithm by performing the Comparison Mean Opinion Score test. Statistical analysis revealed that of the 10 subjects, four showed significant differences in amplification constant settings between the noise-only and speech-in-noise situation ( P <0.05) and one subject also showed significant difference between the speech-only and speech-in-noise situation ( P <0.05). Additionally, every subject preferred different β settings for beamforming in all different input sound levels. The positive findings from this study suggested that the proposed algorithm has potential to improve hearing aid users' personal satisfaction under various ambient situations.

An Effective Post-Filtering Framework for 3-D PET Image Denoising Based on Noise and Sensitivity Characteristics

NASA Astrophysics Data System (ADS)

Kim, Ji Hye; Ahn, Il Jun; Nam, Woo Hyun; Ra, Jong Beom

2015-02-01

Positron emission tomography (PET) images usually suffer from a noticeable amount of statistical noise. In order to reduce this noise, a post-filtering process is usually adopted. However, the performance of this approach is limited because the denoising process is mostly performed on the basis of the Gaussian random noise. It has been reported that in a PET image reconstructed by the expectation-maximization (EM), the noise variance of each voxel depends on its mean value, unlike in the case of Gaussian noise. In addition, we observe that the variance also varies with the spatial sensitivity distribution in a PET system, which reflects both the solid angle determined by a given scanner geometry and the attenuation information of a scanned object. Thus, if a post-filtering process based on the Gaussian random noise is applied to PET images without consideration of the noise characteristics along with the spatial sensitivity distribution, the spatially variant non-Gaussian noise cannot be reduced effectively. In the proposed framework, to effectively reduce the noise in PET images reconstructed by the 3-D ordinary Poisson ordered subset EM (3-D OP-OSEM), we first denormalize an image according to the sensitivity of each voxel so that the voxel mean value can represent its statistical properties reliably. Based on our observation that each noisy denormalized voxel has a linear relationship between the mean and variance, we try to convert this non-Gaussian noise image to a Gaussian noise image. We then apply a block matching 4-D algorithm that is optimized for noise reduction of the Gaussian noise image, and reconvert and renormalize the result to obtain a final denoised image. Using simulated phantom data and clinical patient data, we demonstrate that the proposed framework can effectively suppress the noise over the whole region of a PET image while minimizing degradation of the image resolution.

Analysis of image thresholding segmentation algorithms based on swarm intelligence

NASA Astrophysics Data System (ADS)

Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo

2013-03-01

Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.

Blade-Mounted Flap Control for BVI Noise Reduction Proof-of-Concept Test

NASA Technical Reports Server (NTRS)

Dawson, Seth; Hassan, Ahmed; Straub, Friedrich; Tadghighi, Hormoz

1995-01-01

This report describes a wind tunnel test of the McDonnell Douglas Helicopter Systems (MDHS) Active Flap Model Rotor at the NASA Langley 14- by 22-Foot Subsonic Tunnel. The test demonstrated that BVI noise reductions and vibration reductions were possible with the use of an active flap. Aerodynamic results supported the acoustic data trends, showing a reduction in the strength of the tip vortex with the deflection of the flap. Acoustic results showed that the flap deployment, depending on the peak deflection angle and azimuthal shift in its deployment schedule, can produce BVI noise reductions as much as 6 dB on the advancing and retreating sides. The noise reduction was accompanied by an increase in low frequency harmonic noise and high frequency broadband noise. A brief assessment of the effect of the flap on vibration showed that significant reductions were possible. The greatest vibration reductions (as much as 76%) were found in the four per rev pitching moment at the hub. Performance improvement cam results were inconclusive, as the improvements were predicted to be smaller than the resolution of the rotor balance.

Motion-based nonuniformity correction in DoFP polarimeters

NASA Astrophysics Data System (ADS)

Kumar, Rakesh; Tyo, J. Scott; Ratliff, Bradley M.

2007-09-01

Division of Focal Plane polarimeters (DoFP) operate by integrating an array of micropolarizer elements with a focal plane array. These devices have been investigated for over a decade, and example systems have been built in all regions of the optical spectrum. DoFP devices have the distinct advantage that they are mechanically rugged, inherently temporally synchronized, and optically aligned. They have the concomitant disadvantage that each pixel in the FPA has a different instantaneous field of view (IFOV), meaning that the polarization component measurements that go into estimating the Stokes vector across the image come from four different points in the field. In addition to IFOV errors, microgrid camera systems operating in the LWIR have the additional problem that FPA nonuniformity (NU) noise can be quite severe. The spatial differencing nature of a DoFP system exacerbates the residual NU noise that is remaining after calibration, and is often the largest source of false polarization signatures away from regions where IFOV error dominates. We have recently presented a scene based algorithm that uses frame-to-frame motion to compensate for NU noise in unpolarized IR imagers. In this paper, we have extended that algorithm so that it can be used to compensate for NU noise on a DoFP polarimeter. Furthermore, the additional information provided by the scene motion can be used to significantly reduce the IFOV error. We have found a reduction of IFOV error by a factor of 10 if the scene motion is known exactly. Performance is reduced when the motion must be estimated from the scene, but still shows a marked improvement over static DoFP images.

Radiation dose reduction in abdominal computed tomography during the late hepatic arterial phase using a model-based iterative reconstruction algorithm: how low can we go?

PubMed

Husarik, Daniela B; Marin, Daniele; Samei, Ehsan; Richard, Samuel; Chen, Baiyu; Jaffe, Tracy A; Bashir, Mustafa R; Nelson, Rendon C

2012-08-01

The aim of this study was to compare the image quality of abdominal computed tomography scans in an anthropomorphic phantom acquired at different radiation dose levels where each raw data set is reconstructed with both a standard convolution filtered back projection (FBP) and a full model-based iterative reconstruction (MBIR) algorithm. An anthropomorphic phantom in 3 sizes was used with a custom-built liver insert simulating late hepatic arterial enhancement and containing hypervascular liver lesions of various sizes. Imaging was performed on a 64-section multidetector-row computed tomography scanner (Discovery CT750 HD; GE Healthcare, Waukesha, WI) at 3 different tube voltages for each patient size and 5 incrementally decreasing tube current-time products for each tube voltage. Quantitative analysis consisted of contrast-to-noise ratio calculations and image noise assessment. Qualitative image analysis was performed by 3 independent radiologists rating subjective image quality and lesion conspicuity. Contrast-to-noise ratio was significantly higher and mean image noise was significantly lower on MBIR images than on FBP images in all patient sizes, at all tube voltage settings, and all radiation dose levels (P < 0.05). Overall image quality and lesion conspicuity were rated higher for MBIR images compared with FBP images at all radiation dose levels. Image quality and lesion conspicuity on 25% to 50% dose MBIR images were rated equal to full-dose FBP images. This phantom study suggests that depending on patient size, clinically acceptable image quality of the liver in the late hepatic arterial phase can be achieved with MBIR at approximately 50% lower radiation dose compared with FBP.

Wind fence enclosures for infrasonic wind noise reduction.

PubMed

Abbott, JohnPaul; Raspet, Richard; Webster, Jeremy

2015-03-01

A large porous wind fence enclosure has been built and tested to optimize wind noise reduction at infrasonic frequencies between 0.01 and 10 Hz to develop a technology that is simple and cost effective and improves upon the limitations of spatial filter arrays for detecting nuclear explosions, wind turbine infrasound, and other sources of infrasound. Wind noise is reduced by minimizing the sum of the wind noise generated by the turbulence and velocity gradients inside the fence and by the area-averaging the decorrelated pressure fluctuations generated at the surface of the fence. The effects of varying the enclosure porosity, top condition, bottom gap, height, and diameter and adding a secondary windscreen were investigated. The wind fence enclosure achieved best reductions when the surface porosity was between 40% and 55% and was supplemented by a secondary windscreen. The most effective wind fence enclosure tested in this study achieved wind noise reductions of 20-27 dB over the 2-4 Hz frequency band, a minimum of 5 dB noise reduction for frequencies from 0.1 to 20 Hz, constant 3-6 dB noise reduction for frequencies with turbulence wavelengths larger than the fence, and sufficient wind noise reduction at high wind speeds (3-6 m/s) to detect microbaroms.

An algorithm that improves speech intelligibility in noise for normal-hearing listeners.

PubMed

Kim, Gibak; Lu, Yang; Hu, Yi; Loizou, Philipos C

2009-09-01

Traditional noise-suppression algorithms have been shown to improve speech quality, but not speech intelligibility. Motivated by prior intelligibility studies of speech synthesized using the ideal binary mask, an algorithm is proposed that decomposes the input signal into time-frequency (T-F) units and makes binary decisions, based on a Bayesian classifier, as to whether each T-F unit is dominated by the target or the masker. Speech corrupted at low signal-to-noise ratio (SNR) levels (-5 and 0 dB) using different types of maskers is synthesized by this algorithm and presented to normal-hearing listeners for identification. Results indicated substantial improvements in intelligibility (over 60% points in -5 dB babble) over that attained by human listeners with unprocessed stimuli. The findings from this study suggest that algorithms that can estimate reliably the SNR in each T-F unit can improve speech intelligibility.

Survey of techniques for reduction of wind turbine blade trailing edge noise.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barone, Matthew Franklin

2011-08-01

Aerodynamic noise from wind turbine rotors leads to constraints in both rotor design and turbine siting. The primary source of aerodynamic noise on wind turbine rotors is the interaction of turbulent boundary layers on the blades with the blade trailing edges. This report surveys concepts that have been proposed for trailing edge noise reduction, with emphasis on concepts that have been tested at either sub-scale or full-scale. These concepts include trailing edge serrations, low-noise airfoil designs, trailing edge brushes, and porous trailing edges. The demonstrated noise reductions of these concepts are cited, along with their impacts on aerodynamic performance. Anmore » assessment is made of future research opportunities in trailing edge noise reduction for wind turbine rotors.« less

A noise resistant symmetric key cryptosystem based on S8 S-boxes and chaotic maps

NASA Astrophysics Data System (ADS)

Hussain, Iqtadar; Anees, Amir; Aslam, Muhammad; Ahmed, Rehan; Siddiqui, Nasir

2018-04-01

In this manuscript, we have proposed an encryption algorithm to encrypt any digital data. The proposed algorithm is primarily based on the substitution-permutation in which the substitution process is performed by the S 8 Substitution boxes. The proposed algorithm incorporates three different chaotic maps. We have analysed the behaviour of chaos by secure communication in great length, and accordingly, we have applied those chaotic sequences in the proposed encryption algorithm. The simulation and statistical results revealed that the proposed encryption scheme is secure against different attacks. Moreover, the encryption scheme can tolerate the channel noise as well; if the encrypted data is corrupted by the unauthenticated user or by the channel noise, the decryption can still be successfully done with some distortion. The overall results confirmed that the presented work has good cryptographic features, low computational complexity and resistant to the channel noise which makes it suitable for low profile mobile applications.

Operational algorithm for ice-water classification on dual-polarized RADARSAT-2 images

NASA Astrophysics Data System (ADS)

Zakhvatkina, Natalia; Korosov, Anton; Muckenhuber, Stefan; Sandven, Stein; Babiker, Mohamed

2017-01-01

Synthetic Aperture Radar (SAR) data from RADARSAT-2 (RS2) in dual-polarization mode provide additional information for discriminating sea ice and open water compared to single-polarization data. We have developed an automatic algorithm based on dual-polarized RS2 SAR images to distinguish open water (rough and calm) and sea ice. Several technical issues inherent in RS2 data were solved in the pre-processing stage, including thermal noise reduction in HV polarization and correction of angular backscatter dependency in HH polarization. Texture features were explored and used in addition to supervised image classification based on the support vector machines (SVM) approach. The study was conducted in the ice-covered area between Greenland and Franz Josef Land. The algorithm has been trained using 24 RS2 scenes acquired in winter months in 2011 and 2012, and the results were validated against manually derived ice charts of the Norwegian Meteorological Institute. The algorithm was applied on a total of 2705 RS2 scenes obtained from 2013 to 2015, and the validation results showed that the average classification accuracy was 91 ± 4 %.

Experimental determination of pore shapes using phase retrieval from q -space NMR diffraction

NASA Astrophysics Data System (ADS)

Demberg, Kerstin; Laun, Frederik Bernd; Bertleff, Marco; Bachert, Peter; Kuder, Tristan Anselm

2018-05-01

This paper presents an approach to solving the phase problem in nuclear magnetic resonance (NMR) diffusion pore imaging, a method that allows imaging the shape of arbitrary closed pores filled with an NMR-detectable medium for investigation of the microstructure of biological tissue and porous materials. Classical q -space imaging composed of two short diffusion-encoding gradient pulses yields, analogously to diffraction experiments, the modulus squared of the Fourier transform of the pore image which entails an inversion problem: An unambiguous reconstruction of the pore image requires both magnitude and phase. Here the phase information is recovered from the Fourier modulus by applying a phase retrieval algorithm. This allows omitting experimentally challenging phase measurements using specialized temporal gradient profiles. A combination of the hybrid input-output algorithm and the error reduction algorithm was used with dynamically adapting support (shrinkwrap extension). No a priori knowledge on the pore shape was fed to the algorithm except for a finite pore extent. The phase retrieval approach proved successful for simulated data with and without noise and was validated in phantom experiments with well-defined pores using hyperpolarized xenon gas.

Experimental determination of pore shapes using phase retrieval from q-space NMR diffraction.

PubMed

Demberg, Kerstin; Laun, Frederik Bernd; Bertleff, Marco; Bachert, Peter; Kuder, Tristan Anselm

2018-05-01

This paper presents an approach to solving the phase problem in nuclear magnetic resonance (NMR) diffusion pore imaging, a method that allows imaging the shape of arbitrary closed pores filled with an NMR-detectable medium for investigation of the microstructure of biological tissue and porous materials. Classical q-space imaging composed of two short diffusion-encoding gradient pulses yields, analogously to diffraction experiments, the modulus squared of the Fourier transform of the pore image which entails an inversion problem: An unambiguous reconstruction of the pore image requires both magnitude and phase. Here the phase information is recovered from the Fourier modulus by applying a phase retrieval algorithm. This allows omitting experimentally challenging phase measurements using specialized temporal gradient profiles. A combination of the hybrid input-output algorithm and the error reduction algorithm was used with dynamically adapting support (shrinkwrap extension). No a priori knowledge on the pore shape was fed to the algorithm except for a finite pore extent. The phase retrieval approach proved successful for simulated data with and without noise and was validated in phantom experiments with well-defined pores using hyperpolarized xenon gas.

Detection and characterization of lesions on low-radiation-dose abdominal CT images postprocessed with noise reduction filters.

PubMed

Kalra, Mannudeep K; Maher, Michael M; Blake, Michael A; Lucey, Brian C; Karau, Kelly; Toth, Thomas L; Avinash, Gopal; Halpern, Elkan F; Saini, Sanjay

2004-09-01

To assess the effect of noise reduction filters on detection and characterization of lesions on low-radiation-dose abdominal computed tomographic (CT) images. Low-dose CT images of abdominal lesions in 19 consecutive patients (11 women, eight men; age range, 32-78 years) were obtained at reduced tube currents (120-144 mAs). These baseline low-dose CT images were postprocessed with six noise reduction filters; the resulting postprocessed images were then randomly assorted with baseline images. Three radiologists performed independent evaluation of randomized images for presence, number, margins, attenuation, conspicuity, calcification, and enhancement of lesions, as well as image noise. Side-by-side comparison of baseline images with postprocessed images was performed by using a five-point scale for assessing lesion conspicuity and margins, image noise, beam hardening, and diagnostic acceptability. Quantitative noise and contrast-to-noise ratio were obtained for all liver lesions. Statistical analysis was performed by using the Wilcoxon signed rank test, Student t test, and kappa test of agreement. Significant reduction of noise was observed in images postprocessed with filter F compared with the noise in baseline nonfiltered images (P =.004). Although the number of lesions seen on baseline images and that seen on postprocessed images were identical, lesions were less conspicuous on postprocessed images than on baseline images. A decrease in quantitative image noise and contrast-to-noise ratio for liver lesions was noted with all noise reduction filters. There was good interobserver agreement (kappa = 0.7). Although the use of currently available noise reduction filters improves image noise and ameliorates beam-hardening artifacts at low-dose CT, such filters are limited by a compromise in lesion conspicuity and appearance in comparison with lesion conspicuity and appearance on baseline low-dose CT images. Copyright RSNA, 2004

Speckle reduction of OCT images using an adaptive cluster-based filtering

NASA Astrophysics Data System (ADS)

Adabi, Saba; Rashedi, Elaheh; Conforto, Silvia; Mehregan, Darius; Xu, Qiuyun; Nasiriavanaki, Mohammadreza

2017-02-01

Optical coherence tomography (OCT) has become a favorable device in the dermatology discipline due to its moderate resolution and penetration depth. OCT images however contain grainy pattern, called speckle, due to the broadband source that has been used in the configuration of OCT. So far, a variety of filtering techniques is introduced to reduce speckle in OCT images. Most of these methods are generic and can be applied to OCT images of different tissues. In this paper, we present a method for speckle reduction of OCT skin images. Considering the architectural structure of skin layers, it seems that a skin image can benefit from being segmented in to differentiable clusters, and being filtered separately in each cluster by using a clustering method and filtering methods such as Wiener. The proposed algorithm was tested on an optical solid phantom with predetermined optical properties. The algorithm was also tested on healthy skin images. The results show that the cluster-based filtering method can reduce the speckle and increase the signal-to-noise ratio and contrast while preserving the edges in the image.

Summary of noise reduction characteristics of typical general aviation materials

NASA Technical Reports Server (NTRS)

Roskam, J.; Grosveld, F.; Van Aken, J.

1979-01-01

The paper presents the results of a large number of systematic tests to determine noise reduction characteristics of general aviation materials. Effects of material type (metallic and composite), thickness, panel stiffening, vibration damping materials, sound absorption materials and pressurization on noise reduction are included. Several promising methods for reducing cabin interior noise in light airplanes are discussed based on the results.

Truck Noise X : Noise Reduction Options for Diesel Powered International Harvester Trucks : Volume 2. Cost-Noise Analysis and Field Installation.

DOT National Transportation Integrated Search

1977-04-01

Noise reduction option development work was carried out on two inservice diesel powered IH trucks, consisting of a Cab-over model and a Conventional model with a baseline exterior noise level of 87 dB(A) each. Since no specific noise goals were set, ...

Airframe Noise Studies: Review and Future Direction

NASA Technical Reports Server (NTRS)

Rackl, Robert G.; Miller, Gregory; Guo, Yueping; Yamamoto, Kingo

2005-01-01

This report contains the following information: 1) a review of airframe noise research performed under NASA's Advanced Subsonic Transport (AST) program up to the year 2000, 2) a comparison of the year 1992 airframe noise predictions with those using a year 2000 baseline, 3) an assessment of various airframe noise reduction concepts as applied to the year 2000 baseline predictions, and 4) prioritized recommendations for future airframe noise reduction work. NASA's Aircraft Noise Prediction Program was the software used for all noise predictions and assessments. For future work, the recommendations for the immediate future focus on the development of design tools sensitive to airframe noise treatment effects and on improving the basic understanding of noise generation by the landing gear as well as on its reduction.

Integration of retinal image sequences

NASA Astrophysics Data System (ADS)

Ballerini, Lucia

1998-10-01

In this paper a method for noise reduction in ocular fundus image sequences is described. The eye is the only part of the human body where the capillary network can be observed along with the arterial and venous circulation using a non invasive technique. The study of the retinal vessels is very important both for the study of the local pathology (retinal disease) and for the large amount of information it offers on systematic haemodynamics, such as hypertension, arteriosclerosis, and diabetes. In this paper a method for image integration of ocular fundus image sequences is described. The procedure can be divided in two step: registration and fusion. First we describe an automatic alignment algorithm for registration of ocular fundus images. In order to enhance vessel structures, we used a spatially oriented bank of filters designed to match the properties of the objects of interest. To evaluate interframe misalignment we adopted a fast cross-correlation algorithm. The performances of the alignment method have been estimated by simulating shifts between image pairs and by using a cross-validation approach. Then we propose a temporal integration technique of image sequences so as to compute enhanced pictures of the overall capillary network. Image registration is combined with image enhancement by fusing subsequent frames of a same region. To evaluate the attainable results, the signal-to-noise ratio was estimated before and after integration. Experimental results on synthetic images of vessel-like structures with different kind of Gaussian additive noise as well as on real fundus images are reported.

Results of the noise measurement program on a standard and modified OH-6A helicopter

NASA Technical Reports Server (NTRS)

Henderson, H. R.; Peegg, R. J.; Hilton, D. A.

1973-01-01

A field noise measurement program has been conducted on a standard OH-6A helicopter and one that had been modified by reducing the rotor speed, altering rotor tip shape, and treating the engine exhaust and inlet to reduce the external noise levels. The modifications consisted of extensive aircraft design changes resulting in substantial noise reductions following state-of-art noise reduction techniques. The purpose of this study was to document the ground noise characteristics of each helicopter during flyover, hover, landing, and take-off operations. Based on an analysis of the measured results, the average of the overall on-track noise levels of the final modified helicopter was approximately 14 db lower than that for the standard helicopter. Narrow-band-spectra data of the hovering helicopter show a reduction in the overall noise due to the reductions achieved for the lifting main and antitorque tail rotor, engine exhaust, and gear box noise for the modified helicopter. The noise results of the test program are found to correlate generally with noise measurements made previously on this type of aircraft.