Adaptive wavelet Wiener filtering of ECG signals.
Smital, Lukáš; Vítek, Martin; Kozumplík, Jiří; Provazník, Ivo
2013-02-01
In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter.
Adaptive wavelet Wiener filtering of ECG signals.
Smital, Lukáš; Vítek, Martin; Kozumplík, Jiří; Provazník, Ivo
2013-02-01
In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter. PMID:23192472
Robust Wiener filtering for Adaptive Optics
Poyneer, L A
2004-06-17
In many applications of optical systems, the observed field in the pupil plane has a non-uniform phase component. This deviation of the phase of the field from uniform is called a phase aberration. In imaging systems this aberration will degrade the quality of the images. In the case of a large astronomical telescope, random fluctuations in the atmosphere lead to significant distortion. These time-varying distortions can be corrected using an Adaptive Optics (AO) system, which is a real-time control system composed of optical, mechanical and computational parts. Adaptive optics is also applicable to problems in vision science, laser propagation and communication. For a high-level overview, consult this web site. For an in-depth treatment of the astronomical case, consult these books.
Adaptive wiener image restoration kernel
Yuan, Ding
2007-06-05
A method and device for restoration of electro-optical image data using an adaptive Wiener filter begins with constructing imaging system Optical Transfer Function, and the Fourier Transformations of the noise and the image. A spatial representation of the imaged object is restored by spatial convolution of the image using a Wiener restoration kernel.
Cannistraci, Carlo Vittorio; Abbas, Ahmed; Gao, Xin
2015-01-01
Denoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet's performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis. PMID:25619991
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.
NASA Astrophysics Data System (ADS)
Mohamed, Khaled M.; Hardie, Russell C.
2015-12-01
We present a new patch-based image restoration algorithm using an adaptive Wiener filter (AWF) with a novel spatial-domain multi-patch correlation model. The new filter structure is referred to as a collaborative adaptive Wiener filter (CAWF). The CAWF employs a finite size moving window. At each position, the current observation window represents the reference patch. We identify the most similar patches in the image within a given search window about the reference patch. A single-stage weighted sum of all of the pixels in the similar patches is used to estimate the center pixel in the reference patch. The weights are based on a new multi-patch correlation model that takes into account each pixel's spatial distance to the center of its corresponding patch, as well as the intensity vector distances among the similar patches. One key advantage of the CAWF approach, compared with many other patch-based algorithms, is that it can jointly handle blur and noise. Furthermore, it can also readily treat spatially varying signal and noise statistics. To the best of our knowledge, this is the first multi-patch algorithm to use a single spatial-domain weighted sum of all pixels within multiple similar patches to form its estimate and the first to use a spatial-domain multi-patch correlation model to determine the weights. The experimental results presented show that the proposed method delivers high performance in image restoration in a variety of scenarios.
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.
SAR Speckle Noise Reduction Using Wiener Filter
NASA Technical Reports Server (NTRS)
Joo, T. H.; Held, D. N.
1983-01-01
Synthetic aperture radar (SAR) images are degraded by speckle. A multiplicative speckle noise model for SAR images is presented. Using this model, a Wiener filter is derived by minimizing the mean-squared error using the known speckle statistics. Implementation of the Wiener filter is discussed and experimental results are presented. Finally, possible improvements to this method are explored.
Wiener Filter Estimation of Transfer Functions
ERIC Educational Resources Information Center
Kessel, Robert
2004-01-01
The use of a Wiener filter estimate for the linear transfer function can significantly improve the description of behavioral dynamics. This report presents a two-pass, Monte-Carlo-based algorithm that is well suited to repeated-trials local average measurements. The Wiener filter transfer functions strongly suppress noise artifacts as well as…
Cornelis, Bram; Moonen, Marc; Wouters, Jan
2012-06-01
This paper evaluates noise reduction techniques in bilateral and binaural hearing aids. Adaptive implementations (on a real-time test platform) of the bilateral and binaural speech distortion weighted multichannel Wiener filter (SDW-MWF) and a competing bilateral fixed beamformer are evaluated. As the SDW-MWF relies on a voice activity detector (VAD), a realistic binaural VAD is also included. The test subjects (both normal hearing subjects and hearing aid users) are tested by an adaptive speech reception threshold (SRT) test in different spatial scenarios, including a realistic cafeteria scenario with nonstationary noise. The main conclusions are: (a) The binaural SDW-MWF can further improve the SRT (up to 2 dB) over the improvements achieved by bilateral algorithms, although a significant difference is only achievable if the binaural SDW-MWF uses a perfect VAD. However, in the cafeteria scenario only the binaural SDW-MWF achieves a significant SRT improvement (2.6 dB with perfect VAD, 2.2 dB with real VAD), for the group of hearing aid users. (b) There is no significant degradation when using a real VAD at the input signal-to-noise ratio (SNR) levels where the hearing aid users reach their SRT. (c) The bilateral SDW-MWF achieves no SRT improvements compared to the bilateral fixed beamformer.
Correia, Carlos M; Teixeira, Joel
2014-12-01
Computationally efficient wave-front reconstruction techniques for astronomical adaptive-optics (AO) systems have seen great development in the past decade. Algorithms developed in the spatial-frequency (Fourier) domain have gathered much attention, especially for high-contrast imaging systems. In this paper we present the Wiener filter (resulting in the maximization of the Strehl ratio) and further develop formulae for the anti-aliasing (AA) Wiener filter that optimally takes into account high-order wave-front terms folded in-band during the sensing (i.e., discrete sampling) process. We employ a continuous spatial-frequency representation for the forward measurement operators and derive the Wiener filter when aliasing is explicitly taken into account. We further investigate and compare to classical estimates using least-squares filters the reconstructed wave-front, measurement noise, and aliasing propagation coefficients as a function of the system order. Regarding high-contrast systems, we provide achievable performance results as a function of an ensemble of forward models for the Shack-Hartmann wave-front sensor (using sparse and nonsparse representations) and compute point-spread-function raw intensities. We find that for a 32×32 single-conjugated AOs system the aliasing propagation coefficient is roughly 60% of the least-squares filters, whereas the noise propagation is around 80%. Contrast improvements of factors of up to 2 are achievable across the field in the H band. For current and next-generation high-contrast imagers, despite better aliasing mitigation, AA Wiener filtering cannot be used as a standalone method and must therefore be used in combination with optical spatial filters deployed before image formation actually takes place.
Wiener filter for filtered back projection in digital breast tomosynthesis
NASA Astrophysics Data System (ADS)
Wang, Xinying; Mainprize, James G.; Wu, Gang; Yaffe, Martin J.
2012-03-01
Conventional filtered back projection (FBP) reconstruction for digital breast tomosynthesis (DBT) can suffer from a low signal to noise ratio. Because of the strong amplification by the reconstruction filters (ramp, apodization and slice thickness), noise at high spatial frequencies can be greatly increased. Image enhancement by Wiener filtering is investigated as a possible method to improve image quality. A neighborhood wavelet coefficient window technique is used to estimate the noise content of projection images and a Wiener filter is applied to the projection images. The neighborhood wavelet coefficient window is a non-linear technique, which may cause the Wiener filters estimated before and after the application of the reconstruction filters to be different. Image quality of a FBP reconstruction with and without Wiener filtering is investigated using a Fourier-based observer detectability metric ( d' ) for evaluation. Simulations of tomosynthesis are performed in both homogeneous and anatomic textured backgrounds containing lowcontrast masses or small microcalcifications. Initial results suggest that improvements in detectability can be achieved when the Wiener filter is applied, especially when the Wiener filter is estimated for the reconstruction filtered projections.
Manosueb, Anchalee; Koseeyaporn, Jeerasuda; Wardkein, Paramote
2014-01-01
This paper presents a technique for finding the optimal initial weight for adaptive filter by using difference equation. The obtained analytical response of the system identifies the appropriate weights for the system and shows that the MSE depends on the initial weight. The proposed technique is applied to eliminate the known frequency power line interference (PLI) signal in the electrocardiogram (ECG) signal. The PLI signal is considered as a combination of cosine and sine signals. The adaptive filter, therefore, attempts to adjust the amplitude of cosine and sine signals to synthesize a reference signal very similar to the contaminated PLI signal. To compare the potential of the proposed technique to other techniques, the system is simulated by using the Matlab program and the TMS320C6713 digital board. The simulation results demonstrate that the proposed technique enables the system to eliminate the PLI signal with the fastest time and gains the superior results of the recovered ECG signal.
Image watermarking extraction using Fourier domain Wiener filter
NASA Astrophysics Data System (ADS)
Birch, Philip; Pavlidis, Marios; Panwar, Ankit; Nnamadim, Ozoemena; Kypraios, Ioannis; Mitra, Bhargav; Young, Rupert; Chatwin, Chris
2008-03-01
Digital watermarking is a vital process for protecting the copyright of images. This paper presents a method of embedding a private robust watermark into a digital image. The full complex form the Wiener filter is used to extract the signal from the watermarked image. This is shown to outperform the more conventional approximate notation. The results are shown to be extremely noise insensitive.
Wavelet-domain TI Wiener-like filtering for complex MR data denoising.
Hu, Kai; Cheng, Qiaocui; Gao, Xieping
2016-10-01
Magnetic resonance (MR) images are affected by random noises, which degrade many image processing and analysis tasks. It has been shown that the noise in magnitude MR images follows a Rician distribution. Unlike additive Gaussian noise, the noise is signal-dependent, and consequently difficult to reduce, especially in low signal-to-noise ratio (SNR) images. Wirestam et al. in [20] proposed a Wiener-like filtering technique in wavelet-domain to reduce noise before construction of the magnitude MR image. Based on Wirestam's study, we propose a wavelet-domain translation-invariant (TI) Wiener-like filtering algorithm for noise reduction in complex MR data. The proposed denoising algorithm shows the following improvements compared with Wirestam's method: (1) we introduce TI property into the Wiener-like filtering in wavelet-domain to suppress artifacts caused by translations of the signal; (2) we integrate one Stein's Unbiased Risk Estimator (SURE) thresholding with two Wiener-like filters to make the hard-thresholding scale adaptive; and (3) the first Wiener-like filtering is used to filter the original noisy image in which the noise obeys Gaussian distribution and it provides more reasonable results. The proposed algorithm is applied to denoise the real and imaginary parts of complex MR images. To evaluate our proposed algorithm, we conduct extensive denoising experiments using T1-weighted simulated MR images, diffusion-weighted (DW) phantom and in vivo data. We compare our algorithm with other popular denoising methods. The results demonstrate that our algorithm outperforms others in term of both efficiency and robustness. PMID:27238055
Wavelet-domain TI Wiener-like filtering for complex MR data denoising.
Hu, Kai; Cheng, Qiaocui; Gao, Xieping
2016-10-01
Magnetic resonance (MR) images are affected by random noises, which degrade many image processing and analysis tasks. It has been shown that the noise in magnitude MR images follows a Rician distribution. Unlike additive Gaussian noise, the noise is signal-dependent, and consequently difficult to reduce, especially in low signal-to-noise ratio (SNR) images. Wirestam et al. in [20] proposed a Wiener-like filtering technique in wavelet-domain to reduce noise before construction of the magnitude MR image. Based on Wirestam's study, we propose a wavelet-domain translation-invariant (TI) Wiener-like filtering algorithm for noise reduction in complex MR data. The proposed denoising algorithm shows the following improvements compared with Wirestam's method: (1) we introduce TI property into the Wiener-like filtering in wavelet-domain to suppress artifacts caused by translations of the signal; (2) we integrate one Stein's Unbiased Risk Estimator (SURE) thresholding with two Wiener-like filters to make the hard-thresholding scale adaptive; and (3) the first Wiener-like filtering is used to filter the original noisy image in which the noise obeys Gaussian distribution and it provides more reasonable results. The proposed algorithm is applied to denoise the real and imaginary parts of complex MR images. To evaluate our proposed algorithm, we conduct extensive denoising experiments using T1-weighted simulated MR images, diffusion-weighted (DW) phantom and in vivo data. We compare our algorithm with other popular denoising methods. The results demonstrate that our algorithm outperforms others in term of both efficiency and robustness.
Optimal color image restoration: Wiener filter and quaternion Fourier transform
NASA Astrophysics Data System (ADS)
Grigoryan, Artyom M.; Agaian, Sos S.
2015-03-01
In this paper, we consider the model of quaternion signal degradation when the signal is convoluted and an additive noise is added. The classical model of such a model leads to the solution of the optimal Wiener filter, where the optimality with respect to the mean square error. The characteristic of this filter can be found in the frequency domain by using the Fourier transform. For quaternion signals, the inverse problem is complicated by the fact that the quaternion arithmetic is not commutative. The quaternion Fourier transform does not map the convolution to the operation of multiplication. In this paper, we analyze the linear model of the signal and image degradation with an additive independent noise and the optimal filtration of the signal and images in the frequency domain and in the quaternion space.
Adaptive control of Hammerstein-Wiener nonlinear systems
NASA Astrophysics Data System (ADS)
Zhang, Bi; Hong, Hyokchan; Mao, Zhizhong
2016-07-01
The Hammerstein-Wiener model is a block-oriented model, having a linear dynamic block sandwiched by two static nonlinear blocks. This note develops an adaptive controller for a special form of Hammerstein-Wiener nonlinear systems which are parameterized by the key-term separation principle. The adaptive control law and recursive parameter estimation are updated by the use of internal variable estimations. By modeling the errors due to the estimation of internal variables, we establish convergence and stability properties. Theoretical results show that parameter estimation convergence and closed-loop system stability can be guaranteed under sufficient condition. From a qualitative analysis of the sufficient condition, we introduce an adaptive weighted factor to improve the performance of the adaptive controller. Numerical examples are given to confirm the results in this paper.
Noise and speckle reduction in synthetic aperture radar imagery by nonparametric Wiener filtering.
Caprari, R S; Goh, A S; Moffatt, E K
2000-12-10
We present a Wiener filter that is especially suitable for speckle and noise reduction in multilook synthetic aperture radar (SAR) imagery. The proposed filter is nonparametric, not being based on parametrized analytical models of signal statistics. Instead, the Wiener-Hopf equation is expressed entirely in terms of observed signal statistics, with no reference to the possibly unobservable pure signal and noise. This Wiener filter is simple in concept and implementation, exactly minimum mean-square error, and directly applicable to signal-dependent and multiplicative noise. We demonstrate the filtering of a genuine two-look SAR image and show how a nonnegatively constrained version of the filter substantially reduces ringing.
On Wiener filtering and the physics behind statistical modeling.
Marbach, Ralf
2002-01-01
The closed-form solution of the so-called statistical multivariate calibration model is given in terms of the pure component spectral signal, the spectral noise, and the signal and noise of the reference method. The "statistical" calibration model is shown to be as much grounded on the physics of the pure component spectra as any of the "physical" models. There are no fundamental differences between the two approaches since both are merely different attempts to realize the same basic idea, viz., the spectrometric Wiener filter. The concept of the application-specific signal-to-noise ratio (SNR) is introduced, which is a combination of the two SNRs from the reference and the spectral data. Both are defined and the central importance of the latter for the assessment and development of spectroscopic instruments and methods is explained. Other statistics like the correlation coefficient, prediction error, slope deficiency, etc., are functions of the SNR. Spurious correlations and other practically important issues are discussed in quantitative terms. Most important, it is shown how to use a priori information about the pure component spectra and the spectral noise in an optimal way, thereby making the distinction between statistical and physical calibrations obsolete and combining the best of both worlds. Companies and research groups can use this article to realize significant savings in cost and time for development efforts.
Modified patch-based locally optimal Wiener method for interferometric SAR phase filtering
NASA Astrophysics Data System (ADS)
Wang, Yang; Huang, Haifeng; Dong, Zhen; Wu, Manqing
2016-04-01
This paper presents a modified patch-based locally optimal Wiener (PLOW) method for interferometric synthetic aperture radar (InSAR) phase filtering. PLOW is a linear minimum mean squared error (LMMSE) estimator based on a Gaussian additive noise condition. It jointly estimates moments, including mean and covariance, using a non-local technique. By using similarities between image patches, this method can effectively filter noise while preserving details. When applied to InSAR phase filtering, three modifications are proposed based on spatial variant noise. First, pixels are adaptively clustered according to their coherence magnitudes. Second, rather than a global estimator, a locally adaptive estimator is used to estimate noise covariance. Third, using the coherence magnitudes as weights, the mean of each cluster is estimated, using a weighted mean to further reduce noise. The performance of the proposed method is experimentally verified using simulated and real data. The results of our study demonstrate that the proposed method is on par or better than the non-local interferometric SAR (NL-InSAR) method.
Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum
NASA Astrophysics Data System (ADS)
Ming, Yang; Chen, Jin; Dong, Guangming
2011-07-01
In vibration analysis, weak fault feature extraction under strong background noise is of great importance. A method based on cyclic Wiener filter and envelope spectrum analysis is proposed. Cyclic Wiener filter exploits the spectral coherence theory induced by the second-order cyclostationary signal. The original signal is duplicated and shifted in the frequency domain by amounts corresponding to the cyclic frequencies. The noise component is optimally filtered by a filter-bank. The filtered signal is analyzed by performing envelope spectrum. In the envelope spectrum, characteristic frequencies are quite clear. Then the most impactive part is effectively extracted for further fault diagnosis. The effectiveness of the method is demonstrated on both simulated signal and actual data from rolling bearing accelerated life test.
Intensity transform and Wiener filter in measurement of blood flow in arteriography
NASA Astrophysics Data System (ADS)
Nunes, Polyana F.; Franco, Marcelo L. N.; Filho, João. B. D.; Patrocínio, Ana C.
2015-03-01
Using the arteriography examination, it is possible to check anomalies in blood vessels and diseases such as stroke, stenosis, bleeding and especially in the diagnosis of Encephalic Death in comatose individuals. Encephalic death can be diagnosed only when there is complete interruption of all brain functions, and hence the blood stream. During the examination, there may be some interference on the sensors, such as environmental factors, poor maintenance of equipment, patient movement, among other interference, which can directly affect the noise produced in angiography images. Then, we need to use digital image processing techniques to minimize this noise and improve the pixel count. Therefore, this paper proposes to use median filter and enhancement techniques for transformation of intensity using the sigmoid function together with the Wiener filter so you can get less noisy images. It's been realized two filtering techniques to remove the noise of images, one with the median filter and the other with the Wiener filter along the sigmoid function. For 14 tests quantified, including 7 Encephalic Death and 7 other cases, the technique that achieved a most satisfactory number of pixels quantified, also presenting a lesser amount of noise, is the Wiener filter sigmoid function, and in this case used with 0.03 cuttof.
NASA Technical Reports Server (NTRS)
Full, William E.; Eppler, Duane T.
1993-01-01
The effectivity of multichannel Wiener filters to improve images obtained with passive microwave systems was investigated by applying Wiener filters to passive microwave images of first-year sea ice. Four major parameters which define the filter were varied: the lag or pixel offset between the original and the desired scenes, filter length, the number of lines in the filter, and the weight applied to the empirical correlation functions. The effect of each variable on the image quality was assessed by visually comparing the results. It was found that the application of multichannel Wiener theory to passive microwave images of first-year sea ice resulted in visually sharper images with enhanced textural features and less high-frequency noise. However, Wiener filters induced a slight blocky grain to the image and could produce a type of ringing along scan lines traversing sharp intensity contrasts.
Sim, K S; Law, K K; Tso, C P
2007-11-01
A new filter is developed for the enhancement of scanning electron microscope (SEM) images. A mixed Lagrange time delay estimation auto-regression (MLTDEAR)-based interpolator is used to provide an estimate of noise variance to a standard Wiener filter. A variety of images are captured and the performance of the filter is shown to surpass the conventional noise filters. As all the information required for processing is extracted from a single image, this method is not constrained by image registration requirements and thus can be applied in real-time in cases where specimen drift is presented in the SEM image.
NASA Astrophysics Data System (ADS)
Yousef, Amr H.; Li, Jiang; Karim, Mohammad
2012-05-01
Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. The reconstructed image suffers from degradations such as blur, aliasing, photo-detector noise and registration and fusion error. Wiener filter can be used to remove artifacts and enhance the visual quality of the reconstructed images. In this paper, we introduce a new fast stochasticWiener filter for SR reconstruction and restoration that can be implemented efficiently in the frequency domain. Our derivation depends on the continuous-discrete-continuous (CDC) model that represents most of the degradations encountered during the image-gathering and image-display processes. We incorporate a new parameter that accounts for LR images registration and fusion errors. Also, we speeded up the performance of the filter by constraining it to work on small patches of the images. Beside this, we introduce two figures of merits: information rate and maximum realizable fidelity, which can be used to assess the visual quality of the resultant images. Simulations and experimental results demonstrate that the derived Wiener filter that can be implemented efficiently in the frequency domain can reduce aliasing, blurring, and noise and result in a sharper reconstructed image. Also, Quantitative assessment using the proposed figures coincides with the visual qualitative assessment. Finally, we evaluate our filter against other SR techniques and its results were very competitive.
Wiener filtering of the COBE Differential Microwave Radiometer data
NASA Technical Reports Server (NTRS)
Bunn, Emory F.; Fisher, Karl B.; Hoffman, Yehuda; Lahav, Ofer; Silk, Joseph; Zaroubi, Saleem
1994-01-01
We derive an optimal linear filter to suppress the noise from the cosmic background explorer satellite (COBE) Differential Microwave Radiometer (DMR) sky maps for a given power spectrum. We then apply the filter to the first-year DMR data, after removing pixels within 20 deg of the Galactic plane from the data. We are able to identify particular hot and cold spots in the filtered maps at a level 2 to 3 times the noise level. We use the formalism of constrained realizations of Gaussian random fields to assess the uncertainty in the filtered sky maps. In addition to improving the signal-to-noise ratio of the map as a whole, these techniques allow us to recover some information about the cosmic microwave background anisotropy in the missing Galactic plane region. From these maps we are able to determine which hot and cold spots in the data are statistically significant, and which may have been produced by noise. In addition, the filtered maps can be used for comparison with other experiments on similar angular scales.
NASA Technical Reports Server (NTRS)
Weissman, David E.; Staton, Leo D.
1991-01-01
A novel approach to the reduction of scattered, interfering signals that corrupt measurements of the signal backscattered from radar targets of interest is being developed. It is being explored with sphere measurements in an indoor microwave radar range. This method is based on the concept of Wiener filtering (which minimizes the difference between the signal plus noise and the desired signal in the time domain). In contrast to the traditional Wiener filter, in which the time domain error between two sequences are minimized, the approach reported uses the frequency domain phasor amplitudes of a swept frequency signal. It minimizes the difference (least-mean-square-magnitude) between the signal-plus-noise and the signal complex phasors, across the entire spectrum.
Practical considerations of the Wiener filtering technique on projection data for PET
Shao, L.; Karp, J.S. ); Countryman, P. )
1994-08-01
Wiener filtering (WF) attempt to recover a blurred object by incorporating the system modulation transfer function (MTF) and its noise characteristics. Although it has been widely studied in SPECT, few studies have been reported in PET. In this investigation, first, the authors discuss some factors in PET which affect the Poisson nature of the noise on data, such as the axial normalization, detector efficiency correction and randoms correction. Then the authors proposed a modified WF which compensates for the above factors. The modified WF provides a more accurate noise estimation method and a simple way to optimize the filter performance. Finally the authors apply the modified WF to projection data of the volumetric UGM PENN-PET 240H scanner in both transaxial and axial directions. A cold-hot sphere phantom and 3D brain phantom are used for evaluation. The modified WF is compared to the conventional WFs. The results indicate that (1) the conventional WFs may overestimate the noise power spectrum; (2) although the WFs are sensitive to the image statistics their overall performances are still quantitatively much better than without any restoration filtering; (3) in general, filtering in both axial and transaxial directions is better than the transaxial filtering only, but its superiority is more obvious when applied to scans with low image statistics; (4) in general, the Wiener filtering improves the PET image contrast and count recovery.
NASA Astrophysics Data System (ADS)
Mascarenhas, Nelson D. A.; Santos, Cid A. N.; Cruvinel, Paulo E.
1999-03-01
A minitomograph scanner for soil science was developed by the National Center for Research and Development of Agricultural Instrumentation (EMBRAPA/CNPDIA). The purpose of this paper is twofold. First, a statistical characterization of the noise affecting the projection measurements of this scanner is presented. Second, having determined the Poisson nature of this noise, a new method of filtering the projection data prior to the reconstruction is proposed. It is based on transforming the Poisson noise into Gaussian additive noise, filtering the projections in blocks through the Wiener filter and performing the inverse tranformation. Results with real data indicate that this method gives superior results, as compared to conventional backprojection with the ramp filter, by taking into consideration both resolution and noise, through a mean square error criterion.
NASA Astrophysics Data System (ADS)
Kuzma, H. A.; Kappler, K. A.; Rector, J. W.
2006-12-01
Kriging, wiener filters, support vector machines (SVMs), neural networks, linear and non-linear inversion are methods for predicting the values of one set of variables given the values of another. They can all be used to estimate a set of model parameters from measured data given that a physical relationship exists between models and data. However, since the methods were developed in different fields, the mathematics used to describe them tend to obscure rather than highlight the links between them. In this poster, we diagram the methods and clarify their connections in hopes that practitioners of one method will be able to understand and learn from the insights developed in another. At the heart of all of the methods are a set of coefficients that must be found by minimizing an objective function. The solution to the objective function can be found either by inverting a matrix, or by searching through a space of possible answers. We distinguish between direct inversion, in which the desired coefficients are those of the model itself, and indirect inversion, in which examples of models and data are used to estimate the coefficients of an inverse process that, once discovered, can be used to compute new models from new data. Kriging is developed from Geostatistics. The model is usually a rock property (such as gold concentration) and the data is a sample location (x,y,z). The desired coefficients are a set of weights which are used to predict the concentration of a sample taken at a new location based on a variogram. The variogram is computed by averaging across a given set of known samples and is manually adjusted to reflect prior knowledge. Wiener filters were developed in signal processing to predict the values of one time-series from measurements of another. A wiener filter can be derived from kriging by replacing variograms with correlation. Support vector machines are an offshoot of statistical learning theory. They can be written as a form of kriging in which
Cellular Signaling Networks Function as Generalized Wiener-Kolmogorov Filters to Suppress Noise
NASA Astrophysics Data System (ADS)
Hinczewski, Michael; Thirumalai, D.
2014-10-01
Cellular signaling involves the transmission of environmental information through cascades of stochastic biochemical reactions, inevitably introducing noise that compromises signal fidelity. Each stage of the cascade often takes the form of a kinase-phosphatase push-pull network, a basic unit of signaling pathways whose malfunction is linked with a host of cancers. We show that this ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov optimal noise filter. Using concepts from umbral calculus, we generalize the linear Wiener-Kolmogorov theory, originally introduced in the context of communication and control engineering, to take nonlinear signal transduction and discrete molecule populations into account. This allows us to derive rigorous constraints for efficient noise reduction in this biochemical system. Our mathematical formalism yields bounds on filter performance in cases important to cellular function—such as ultrasensitive response to stimuli. We highlight features of the system relevant for optimizing filter efficiency, encoded in a single, measurable, dimensionless parameter. Our theory, which describes noise control in a large class of signal transduction networks, is also useful both for the design of synthetic biochemical signaling pathways and the manipulation of pathways through experimental probes such as oscillatory input.
Analysis of moire fringes by Wiener filtering: An extension to the Fourier method
Harasse, Sebastien; Yashiro, Wataru; Momose, Atsushi
2012-07-31
In X-ray Talbot interferometry, tilting the phase grating with respect to the absorption grating results in the formation of spatial fringes. The analysis of this moire pattern, classically performed by the Fourier method, allows the extraction of the sample phase shift information from a single image. In this context, an extension to the Fourier method is proposed. The filter used to extract the fringe information is chosen optimally in the least-squares sense, given models for the zeroth and first order modes, noise and the modulation transfer function. The latter is obtained by measuring the detector response to moire fringes with increasing frequencies. The obtained Wiener filter allows a better reconstruction of the phase information at all fringe frequencies, compared to the usual box or gaussian filters. This is demonstrated quantitatively by experiments using synchrotron radiation.
Adaptive control of stochastic Hammerstein-Wiener nonlinear systems with measurement noise
NASA Astrophysics Data System (ADS)
Zhang, Bi; Mao, Zhizhong
2016-01-01
This paper deals with the adaptive control of a class of stochastic Hammerstein-Wiener nonlinear systems with measurement noise. Despite the fundamental progress achieved so far, a general theory framework about adaptive control of Hammerstein-Wiener models is still absent. Such situation is mainly due to the lack of an appropriate parameterisation model. To this end, this paper presents a novel parameterisation model that is to replace unmeasurable internal variables with their estimations. Then, the adaptive control algorithm to be applied is derived on the basis of self-tuning control. In addition, due to the use of the internal variable estimations, the stability and convergence properties are different from the self-tuning control. Our aim, in theoretical analysis, is to discover what limitations are in using the estimations instead of the true values in a control algorithm. Representative numerical examples are given and the simulation results verify the theoretical analysis.
On the externalization of virtual sound images in headphone reproduction: A Wiener filter approach
NASA Astrophysics Data System (ADS)
Kim, Sang-Myeong; Choi, Wonjae
2005-06-01
In-head localization of sound images is a critical problem in headphone reproduction. The paper investigates the degree of externalization in terms of the distance of auditory images for various synthesis and reproduction cases. An effective binaural headphone system was constructed by way of binaural synthesis using head-related impulse responses and individual headphone equalization using Wiener filter theory. The headphone system designed had an average reproduction performance error of 2.4% for five subjects with a random noise input, and was used to perform some subjective tests with a set of virtual sources equally spaced and distanced from the center of each subject's head in the horizontal plane. The effects of individual and nonindividual binaural syntheses and those of equalized and nonequalized reproductions were separately investigated. In the tests, each subject was instructed to indicate the distance of auditory images. The results obtained demonstrate that individual equalization is important for externalization, and individual synthesis is important for consistent distance perception. Thus, a combined use of both individual equalization and individual synthesis resulted in externalized sound images of a consistent distance. .
A Wiener-Wavelet-Based filter for de-noising satellite soil moisture retrievals
NASA Astrophysics Data System (ADS)
Massari, Christian; Brocca, Luca; Ciabatta, Luca; Moramarco, Tommaso; Su, Chun-Hsu; Ryu, Dongryeol; Wagner, Wolfgang
2014-05-01
The reduction of noise in microwave satellite soil moisture (SM) retrievals is of paramount importance for practical applications especially for those associated with the study of climate changes, droughts, floods and other related hydrological processes. So far, Fourier based methods have been used for de-noising satellite SM retrievals by filtering either the observed emissivity time series (Du, 2012) or the retrieved SM observations (Su et al. 2013). This contribution introduces an alternative approach based on a Wiener-Wavelet-Based filtering (WWB) technique, which uses the Entropy-Based Wavelet de-noising method developed by Sang et al. (2009) to design both a causal and a non-causal version of the filter. WWB is used as a post-retrieval processing tool to enhance the quality of observations derived from the i) Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E), ii) the Advanced SCATterometer (ASCAT), and iii) the Soil Moisture and Ocean Salinity (SMOS) satellite. The method is tested on three pilot sites located in Spain (Remedhus Network), in Greece (Hydrological Observatory of Athens) and in Australia (Oznet network), respectively. Different quantitative criteria are used to judge the goodness of the de-noising technique. Results show that WWB i) is able to improve both the correlation and the root mean squared differences between satellite retrievals and in situ soil moisture observations, and ii) effectively separates random noise from deterministic components of the retrieved signals. Moreover, the use of WWB de-noised data in place of raw observations within a hydrological application confirms the usefulness of the proposed filtering technique. Du, J. (2012), A method to improve satellite soil moisture retrievals based on Fourier analysis, Geophys. Res. Lett., 39, L15404, doi:10.1029/ 2012GL052435 Su,C.-H.,D.Ryu, A. W. Western, and W. Wagner (2013), De-noising of passive and active microwave satellite soil moisture time
Adaptive discrete-time sliding-mode control of nonlinear systems described by Wiener models
NASA Astrophysics Data System (ADS)
Salhi, Houda; Kamoun, Samira; Essounbouli, Najib; Hamzaoui, Abdelaziz
2016-03-01
In this paper, we propose an adaptive control scheme that can be applied to nonlinear systems with unknown parameters. The considered class of nonlinear systems is described by the block-oriented models, specifically, the Wiener models. These models consist of dynamic linear blocks in series with static nonlinear blocks. The proposed adaptive control method is based on the inverse of the nonlinear function block and on the discrete-time sliding-mode controller. The parameters adaptation are performed using a new recursive parametric estimation algorithm. This algorithm is developed using the adjustable model method and the least squares technique. A recursive least squares (RLS) algorithm is used to estimate the inverse nonlinear function. A time-varying gain is proposed, in the discrete-time sliding mode controller, to reduce the chattering problem. The stability of the closed-loop nonlinear system, with the proposed adaptive control scheme, has been proved. An application to a pH neutralisation process has been carried out and the simulation results clearly show the effectiveness of the proposed adaptive control scheme.
Sigron, Netta; Tselniker, Igor; Nazarathy, Moshe
2012-01-30
The MSDD carrier phase estimation technique is derived here for optically coherent QPSK transmission, introducing the principle of operation while providing intuitive insight in terms of a multi-symbol extension of naïve delay-detection. We derive here for the first time Wiener-optimized and LMS-adapted versions of MSDD, introduce simplified hardware realizations, and evaluate complexity and numerical performance tradeoffs of this highly robust and low-complexity carrier phase recovery method. A multiplier-free carrier phase recovery version of the MSDD provides nearly optimal performance for linewidths up to ~0.5 MHz, whereas for wider linewidths, the Wiener or LMS versions provide optimal performance at about 9 taps, using 1 or 2 complex multipliers per tap.
Frequency domain FIR and IIR adaptive filters
NASA Technical Reports Server (NTRS)
Lynn, D. W.
1990-01-01
A discussion of the LMS adaptive filter relating to its convergence characteristics and the problems associated with disparate eigenvalues is presented. This is used to introduce the concept of proportional convergence. An approach is used to analyze the convergence characteristics of block frequency-domain adaptive filters. This leads to a development showing how the frequency-domain FIR adaptive filter is easily modified to provide proportional convergence. These ideas are extended to a block frequency-domain IIR adaptive filter and the idea of proportional convergence is applied. Experimental results illustrating proportional convergence in both FIR and IIR frequency-domain block adaptive filters is presented.
Split quaternion nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2010-04-01
A split quaternion learning algorithm for the training of nonlinear finite impulse response adaptive filters for the processing of three- and four-dimensional signals is proposed. The derivation takes into account the non-commutativity of the quaternion product, an aspect neglected in the derivation of the existing learning algorithms. It is shown that the additional information taken into account by a rigorous treatment of quaternion algebra provides improved performance on hypercomplex processes. A rigorous analysis of the convergence of the proposed algorithms is also provided. Simulations on both benchmark and real-world signals support the approach.
Liu, Jie; Ying, Dongwen; Zhou, Ping
2014-12-01
Voluntary surface electromyogram (EMG) signals from neurological injury patients are often corrupted by involuntary background interference or spikes, imposing difficulties for myoelectric control. We present a novel framework to suppress involuntary background spikes during voluntary surface EMG recordings. The framework applies a Wiener filter to restore voluntary surface EMG signals based on tracking a priori signal to noise ratio (SNR) by using the decision-directed method. Semi-synthetic surface EMG signals contaminated by different levels of involuntary background spikes were constructed from a database of surface EMG recordings in a group of spinal cord injury subjects. After the processing, the onset detection of voluntary muscle activity was significantly improved against involuntary background spikes. The magnitude of voluntary surface EMG signals can also be reliably estimated for myoelectric control purpose. Compared with the previous sample entropy analysis for suppressing involuntary background spikes, the proposed framework is characterized by quick and simple implementation, making it more suitable for application in a myoelectric control system toward neurological injury rehabilitation. PMID:25443536
Liu, Jie; Ying, Dongwen; Zhou, Ping
2014-01-01
Voluntary surface electromyogram (EMG) signals from neurological injury patients are often corrupted by involuntary background interference or spikes, imposing difficulties for myoelectric control. We present a novel framework to suppress involuntary background spikes during voluntary surface EMG recordings. The framework applies a Wiener filter to restore voluntary surface EMG signals based on tracking a priori signal to noise ratio (SNR) by using the decision-directed method. Semi-synthetic surface EMG signals contaminated by different levels of involuntary background spikes were constructed from a database of surface EMG recordings in a group of spinal cord injury subjects. After the processing, the onset detection of voluntary muscle activity was significantly improved against involuntary background spikes. The magnitude of voluntary surface EMG signals can also be reliably estimated for myoelectric control purpose. Compared with the previous sample entropy analysis for suppressing involuntary background spikes, the proposed framework is characterized by quick and simple implementation, making it more suitable for application in a myoelectric control system toward neurological injury rehabilitation. PMID:25443536
An online novel adaptive filter for denoising time series measurements.
Willis, Andrew J
2006-04-01
A nonstationary form of the Wiener filter based on a principal components analysis is described for filtering time series data possibly derived from noisy instrumentation. The theory of the filter is developed, implementation details are presented and two examples are given. The filter operates online, approximating the maximum a posteriori optimal Bayes reconstruction of a signal with arbitrarily distributed and non stationary statistics. PMID:16649562
Adaptive Mallow's optimization for weighted median filters
NASA Astrophysics Data System (ADS)
Rachuri, Raghu; Rao, Sathyanarayana S.
2002-05-01
This work extends the idea of spectral optimization for the design of Weighted Median filters and employ adaptive filtering that updates the coefficients of the FIR filter from which the weights of the median filters are derived. Mallows' theory of non-linear smoothers [1] has proven to be of great theoretical significance providing simple design guidelines for non-linear smoothers. It allows us to find a set of positive weights for a WM filter whose sample selection probabilities (SSP's) are as close as possible to a SSP set predetermined by Mallow's. Sample selection probabilities have been used as a basis for designing stack smoothers as they give a measure of the filter's detail preserving ability and give non-negative filter weights. We will extend this idea to design weighted median filters admitting negative weights. The new method first finds the linear FIR filter coefficients adaptively, which are then used to determine the weights of the median filter. WM filters can be designed to have band-pass, high-pass as well as low-pass frequency characteristics. Unlike the linear filters, however, the weighted median filters are robust in the presence of impulsive noise, as shown by the simulation results.
Adaptable Iterative and Recursive Kalman Filter Schemes
NASA Technical Reports Server (NTRS)
Zanetti, Renato
2014-01-01
Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. The Iterated Kalman filter (IKF) and the Recursive Update Filter (RUF) are two algorithms that reduce the consequences of the linearization assumption of the EKF by performing N updates for each new measurement, where N is the number of recursions, a tuning parameter. This paper introduces an adaptable RUF algorithm to calculate N on the go, a similar technique can be used for the IKF as well.
Adaptive filtering in biological signal processing.
Iyer, V K; Ploysongsang, Y; Ramamoorthy, P A
1990-01-01
The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed.
NASA Astrophysics Data System (ADS)
Liu, Delian; Li, Zhaohui; Wang, Xiaorui; Zhang, Jianqi
2015-11-01
Target detection is of great importance both in civil and military fields. Here a new moving target detection approach is proposed, which employs a nonlinear adaptive filter to remove large fluctuations on temporal profiles that are produced by evolving clutters. Initially, this paper discusses the temporal behaviors of different pixels in infrared sequences. Then, the new nonlinear adaptive filter that is a variation of the median-modified Wiener filter is given to extract pulse signals on temporal profiles that relate to moving targets. Next, the variance of each temporal profile is estimated by segmenting each temporal profile into several segments to normalize the amplitude of the pulse signals. Finally, the proposed approach is tested via two infrared image sequences and compared with several conventional target detection algorithms. The results show our approach has a high effectiveness in extracting target temporal profiles amidst heavy and slowly evolving clutters.
Neural nets for adaptive filtering and adaptive pattern recognition
Widrow, B.; Winter, R.
1988-03-01
The fields of adaptive signal processing and adaptive neural networks have been developing independently but have that adaptive linear combiner (ALC) in common. With its inputs connected to a tapped delay line, the ALC becomes a key component of an adaptive filter. With its output connected to a quantizer, the ALC becomes an adaptive threshold element of adaptive neuron. Adaptive threshold elements, on the other hand, are the building blocks of neural networks. Today neural nets are the focus of widespread research interest. Areas of investigation include pattern recognition and trainable logic. Neural network systems have not yet had the commercial impact of adaptive filtering. The commonality of the ALC to adaptive signal processing and adaptive neural networks suggests the two fields have much to share with each other. This article describes practical applications of the ALC in signal processing and pattern recognition.
Enhancement of Electrolaryngeal Speech by Adaptive Filtering.
ERIC Educational Resources Information Center
Espy-Wilson, Carol Y.; Chari, Venkatesh R.; MacAuslan, Joel M.; Huang, Caroline B.; Walsh, Michael J.
1998-01-01
A study tested the quality and intelligibility, as judged by several listeners, of four users' electrolaryngeal speech, with and without filtering to compensate for perceptually objectionable acoustic characteristics. Results indicated that an adaptive filtering technique produced a noticeable improvement in the quality of the Transcutaneous…
Identification and control of nonlinear system based on Laguerre-ELM Wiener model
NASA Astrophysics Data System (ADS)
Tang, Yinggan; Han, Zhenzhen; Liu, Fucai; Guan, Xinping
2016-09-01
In this paper, a new Wiener model is presented for identification and control of single-input single-output (SISO) nonlinear systems. The proposed Wiener model consists of a linear Laguerre filter in cascaded with an extreme learning machine (ELM) neural network (called Laguerre-ELM Wiener model). Laguerre filter can approximate a stable linear system to any degree of accuracy with a small number of Laguerre filters, which provides a parsimony structure and high level accuracy simultaneously. To determine the appropriated number of Laguerre filters in Laguerre-ELM Wiener model, Lipschitz quotient criterion is adapted to determine the order of linear part. A generalized ELM algorithm is proposed to estimate the parameters of Laguerre-ELM Wiener model. Once the unknown nonlinear system is identified using Laguerre-ELM Wiener model, a generalized predictive control (GPC) algorithm is designed for control of nonlinear system. The advantage of the proposed control method is that it transfers a nonlinear control problem to a linear one by inserting the inverse of static nonlinear section. Simulation results demonstrate the effectiveness of the proposed identification and control algorithms.
Matched filter based iterative adaptive approach
NASA Astrophysics Data System (ADS)
Nepal, Ramesh; Zhang, Yan Rockee; Li, Zhengzheng; Blake, William
2016-05-01
Matched Filter sidelobes from diversified LPI waveform design and sensor resolution are two important considerations in radars and active sensors in general. Matched Filter sidelobes can potentially mask weaker targets, and low sensor resolution not only causes a high margin of error but also limits sensing in target-rich environment/ sector. The improvement in those factors, in part, concern with the transmitted waveform and consequently pulse compression techniques. An adaptive pulse compression algorithm is hence desired that can mitigate the aforementioned limitations. A new Matched Filter based Iterative Adaptive Approach, MF-IAA, as an extension to traditional Iterative Adaptive Approach, IAA, has been developed. MF-IAA takes its input as the Matched Filter output. The motivation here is to facilitate implementation of Iterative Adaptive Approach without disrupting the processing chain of traditional Matched Filter. Similar to IAA, MF-IAA is a user parameter free, iterative, weighted least square based spectral identification algorithm. This work focuses on the implementation of MF-IAA. The feasibility of MF-IAA is studied using a realistic airborne radar simulator as well as actual measured airborne radar data. The performance of MF-IAA is measured with different test waveforms, and different Signal-to-Noise (SNR) levels. In addition, Range-Doppler super-resolution using MF-IAA is investigated. Sidelobe reduction as well as super-resolution enhancement is validated. The robustness of MF-IAA with respect to different LPI waveforms and SNR levels is also demonstrated.
VSP wave separation by adaptive masking filters
NASA Astrophysics Data System (ADS)
Rao, Ying; Wang, Yanghua
2016-06-01
In vertical seismic profiling (VSP) data processing, the first step might be to separate the down-going wavefield from the up-going wavefield. When using a masking filter for VSP wave separation, there are difficulties associated with two termination ends of the up-going waves. A critical challenge is how the masking filter can restore the energy tails, the edge effect associated with these terminations uniquely exist in VSP data. An effective strategy is to implement masking filters in both τ-p and f-k domain sequentially. Meanwhile it uses a median filter, producing a clean but smooth version of the down-going wavefield, used as a reference data set for designing the masking filter. The masking filter is implemented adaptively and iteratively, gradually restoring the energy tails cut-out by any surgical mute. While the τ-p and the f-k domain masking filters target different depth ranges of VSP, this combination strategy can accurately perform in wave separation from field VSP data.
Kalman filter based control for Adaptive Optics
NASA Astrophysics Data System (ADS)
Petit, Cyril; Quiros-Pacheco, Fernando; Conan, Jean-Marc; Kulcsár, Caroline; Raynaud, Henri-François; Fusco, Thierry
2004-12-01
Classical Adaptive Optics suffer from a limitation of the corrected Field Of View. This drawback has lead to the development of MultiConjugated Adaptive Optics. While the first MCAO experimental set-ups are presently under construction, little attention has been paid to the control loop. This is however a key element in the optimization process especially for MCAO systems. Different approaches have been proposed in recent articles for astronomical applications : simple integrator, Optimized Modal Gain Integrator and Kalman filtering. We study here Kalman filtering which seems a very promising solution. Following the work of Brice Leroux, we focus on a frequential characterization of kalman filters, computing a transfer matrix. The result brings much information about their behaviour and allows comparisons with classical controllers. It also appears that straightforward improvements of the system models can lead to static aberrations and vibrations filtering. Simulation results are proposed and analysed thanks to our frequential characterization. Related problems such as model errors, aliasing effect reduction or experimental implementation and testing of Kalman filter control loop on a simplified MCAO experimental set-up could be then discussed.
Wiener restoration of sampled image data - End-to-end analysis
NASA Technical Reports Server (NTRS)
Fales, Carl L.; Huck, Friedrich O.; Mccormick, Judith A.; Park, Stephen K.
1988-01-01
The Wiener filter is formulated as a function of the basic image-gathering and image-reconstruction constraints, thereby providing a method for minimizing the mean-squared error between the (continuous-input) radiance field and its restored (continuous-output) representation. This formulation of the Wiener filter is further extended to the Wiener-characteristic filter, which provides a method for explicitly specifying the desired representation. Two specific examples of Wiener filters are presented.
Adaptive Filtering Using Recurrent Neural Networks
NASA Technical Reports Server (NTRS)
Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.
2005-01-01
A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.
Quaternion-valued nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2011-08-01
A class of nonlinear quaternion-valued adaptive filtering algorithms is proposed based on locally analytic nonlinear activation functions. To circumvent the stringent standard analyticity conditions which are prohibitive to the development of nonlinear adaptive quaternion-valued estimation models, we use the fact that stochastic gradient learning algorithms require only local analyticity at the operating point in the estimation space. It is shown that the quaternion-valued exponential function is locally analytic, and, since local analyticity extends to polynomials, products, and ratios, we show that a class of transcendental nonlinear functions can serve as activation functions in nonlinear and neural adaptive models. This provides a unifying framework for the derivation of gradient-based learning algorithms in the quaternion domain, and the derived algorithms are shown to have the same generic form as their real- and complex-valued counterparts. To make such models second-order optimal for the generality of quaternion signals (both circular and noncircular), we use recent developments in augmented quaternion statistics to introduce widely linear versions of the proposed nonlinear adaptive quaternion valued filters. This allows full exploitation of second-order information in the data, contained both in the covariance and pseudocovariances to cater rigorously for second-order noncircularity (improperness), and the corresponding power mismatch in the signal components. Simulations over a range of circular and noncircular synthetic processes and a real world 3-D noncircular wind signal support the approach. PMID:21712159
Quaternion-valued nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2011-08-01
A class of nonlinear quaternion-valued adaptive filtering algorithms is proposed based on locally analytic nonlinear activation functions. To circumvent the stringent standard analyticity conditions which are prohibitive to the development of nonlinear adaptive quaternion-valued estimation models, we use the fact that stochastic gradient learning algorithms require only local analyticity at the operating point in the estimation space. It is shown that the quaternion-valued exponential function is locally analytic, and, since local analyticity extends to polynomials, products, and ratios, we show that a class of transcendental nonlinear functions can serve as activation functions in nonlinear and neural adaptive models. This provides a unifying framework for the derivation of gradient-based learning algorithms in the quaternion domain, and the derived algorithms are shown to have the same generic form as their real- and complex-valued counterparts. To make such models second-order optimal for the generality of quaternion signals (both circular and noncircular), we use recent developments in augmented quaternion statistics to introduce widely linear versions of the proposed nonlinear adaptive quaternion valued filters. This allows full exploitation of second-order information in the data, contained both in the covariance and pseudocovariances to cater rigorously for second-order noncircularity (improperness), and the corresponding power mismatch in the signal components. Simulations over a range of circular and noncircular synthetic processes and a real world 3-D noncircular wind signal support the approach.
Precise adaptive photonic rf filters realized with adaptive Bragg gratings
NASA Astrophysics Data System (ADS)
Wickham, Michael G.; Upton, Eric L.
2000-09-01
The demand for higher data capacity and reduced levels of interference in the communications arena are driving dtat links toward high carrier frequencies and wider modulation bandwidths. Circuitry for performing intermediate frequency processing over these more demanding ranges is needed to provide complex signal processing. We have demonstrated photonics technologies utilizing Bragg Grating Signal Processing (BGSP), which can be used to perform a variety of RF filter functions. The desirable benefits of multiple-tap adaptive finite impulse response (FIR) filters, infinite impulse response (IIR) filters, and equalizers are well known; however, they are usually the province of digital signal processing and demand preprocessor sample rates that require high system power consumption. BGSPs provide these functions with discrete optical taps and digital controls while only requiring bandwidths easily provided by conventional RF circuitry. This is because the actual signal processing of the large information bandwidths is performed in the optical regime, while control functions are performed at RF frequencies compatible with integrated circuit technologies. To realize the performance benefits of photonic processing, the Bragg grating reflectors must be stabilized against environmental without unduly taxing the RF control circuitry. We have implemented a orthogonally coded tap modulation technique which stabilizes the transfer function of the signal processor and enables significant adaptive IF signal processing to be obtained with very low size, weight, and power. Our demonstration of a photonic proof-of-concept architecture is a reconfigurable, multiple-tap FIR filter that is dynamically controlled to implement low-pass, high-pass, band-pass, band-stop, and tunable filters operating over bandwidths of 3 Ghz.
Adaptive filters for detection of gravitational waves from coalescing binaries
Eleuteri, Antonio; Milano, Leopoldo; De Rosa, Rosario; Garufi, Fabio; Acernese, Fausto; Barone, Fabrizio; Giordano, Lara; Pardi, Silvio
2006-06-15
In this work we propose use of infinite impulse response adaptive line enhancer (IIR ALE) filters for detection of gravitational waves from coalescing binaries. We extend our previous work and define an adaptive matched filter structure. Filter performance is analyzed in terms of the tracking capability and determination of filter parameters. Furthermore, following the Neyman-Pearson strategy, receiver operating characteristics are derived, with closedform expressions for detection threshold, false alarm, and detection probability. Extensive tests demonstrate the effectiveness of adaptive filters both in terms of small computational cost and robustness.
Adaptive filtering image preprocessing for smart FPA technology
NASA Astrophysics Data System (ADS)
Brooks, Geoffrey W.
1995-05-01
This paper discusses two applications of adaptive filters for image processing on parallel architectures. The first, based on the results of previously accomplished work, summarizes the analyses of various adaptive filters implemented for pixel-level image prediction. FIR filters, fixed and adaptive IIR filters, and various variable step size algorithms were compared with a focus on algorithm complexity against the ability to predict future pixel values. A gaussian smoothing operation with varying spatial and temporal constants were also applied for comparisons of random noise reductions. The second application is a suggestion to use memory-adaptive IIR filters for detecting and tracking motion within an image. Objects within an image are made of edges, or segments, with varying degrees of motion. An application has been previously published that describes FIR filters connecting pixels and using correlations to determine motion and direction. This implementation seems limited to detecting motion coinciding with FIR filter operation rate and the associated harmonics. Upgrading the FIR structures with adaptive IIR structures can eliminate these limitations. These and any other pixel-level adaptive filtering application require data memory for filter parameters and some basic computational capability. Tradeoffs have to be made between chip real estate and these desired features. System tradeoffs will also have to be made as to where it makes the most sense to do which level of processing. Although smart pixels may not be ready to implement adaptive filters, applications such as these should give the smart pixel designer some long range goals.
Autonomous Navigation System Using a Fuzzy Adaptive Nonlinear H∞ Filter
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-01-01
Although nonlinear H∞ (NH∞) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds (δi) and adaptive disturbance attenuation (γ), which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter. PMID:25244587
Autonomous navigation system using a fuzzy adaptive nonlinear H∞ filter.
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-09-19
Although nonlinear H∞ (NH∞) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter.
Autonomous navigation system using a fuzzy adaptive nonlinear H∞ filter.
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-01-01
Although nonlinear H∞ (NH∞) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter. PMID:25244587
Diagnostic analysis of vibration signals using adaptive digital filtering techniques
NASA Technical Reports Server (NTRS)
Jewell, R. E.; Jones, J. H.; Paul, J. E.
1983-01-01
Signal enhancement techniques are described using recently developed digital adaptive filtering equipment. Adaptive filtering concepts are not new; however, as a result of recent advances in microprocessor-based electronics, hardware has been developed that has stable characteristics and of a size exceeding 1000th order. Selected data processing examples are presented illustrating spectral line enhancement, adaptive noise cancellation, and transfer function estimation in the presence of corrupting noise.
Filter. Remix. Make.: Cultivating Adaptability through Multimodality
ERIC Educational Resources Information Center
Dusenberry, Lisa; Hutter, Liz; Robinson, Joy
2015-01-01
This article establishes traits of adaptable communicators in the 21st century, explains why adaptability should be a goal of technical communication educators, and shows how multimodal pedagogy supports adaptability. Three examples of scalable, multimodal assignments (infographics, research interviews, and software demonstrations) that evidence…
Likelihood Methods for Adaptive Filtering and Smoothing. Technical Report #455.
ERIC Educational Resources Information Center
Butler, Ronald W.
The dynamic linear model or Kalman filtering model provides a useful methodology for predicting the past, present, and future states of a dynamic system, such as an object in motion or an economic or social indicator that is changing systematically with time. Recursive likelihood methods for adaptive Kalman filtering and smoothing are developed.…
Adaptive median filtering for preprocessing of time series measurements
NASA Technical Reports Server (NTRS)
Paunonen, Matti
1993-01-01
A median (L1-norm) filtering program using polynomials was developed. This program was used in automatic recycling data screening. Additionally, a special adaptive program to work with asymmetric distributions was developed. Examples of adaptive median filtering of satellite laser range observations and TV satellite time measurements are given. The program proved to be versatile and time saving in data screening of time series measurements.
Adaptive Control of Flexible Structures Using Residual Mode Filters
NASA Technical Reports Server (NTRS)
Balas, Mark J.; Frost, Susan
2010-01-01
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter. We have proposed a modified adaptive controller with a residual mode filter. The RMF is used to accommodate troublesome modes in the system that might otherwise inhibit the adaptive controller, in particular the ASPR condition. This new theory accounts for leakage of the disturbance term into the Q modes. A simple three-mode example shows that the RMF can restore stability to an otherwise unstable adaptively controlled system. This is done without modifying the adaptive controller design.
A hybrid method for optimization of the adaptive Goldstein filter
NASA Astrophysics Data System (ADS)
Jiang, Mi; Ding, Xiaoli; Tian, Xin; Malhotra, Rakesh; Kong, Weixue
2014-12-01
The Goldstein filter is a well-known filter for interferometric filtering in the frequency domain. The main parameter of this filter, alpha, is set as a power of the filtering function. Depending on it, considered areas are strongly or weakly filtered. Several variants have been developed to adaptively determine alpha using different indicators such as the coherence, and phase standard deviation. The common objective of these methods is to prevent areas with low noise from being over filtered while simultaneously allowing stronger filtering over areas with high noise. However, the estimators of these indicators are biased in the real world and the optimal model to accurately determine the functional relationship between the indicators and alpha is also not clear. As a result, the filter always under- or over-filters and is rarely correct. The study presented in this paper aims to achieve accurate alpha estimation by correcting the biased estimator using homogeneous pixel selection and bootstrapping algorithms, and by developing an optimal nonlinear model to determine alpha. In addition, an iteration is also merged into the filtering procedure to suppress the high noise over incoherent areas. The experimental results from synthetic and real data show that the new filter works well under a variety of conditions and offers better and more reliable performance when compared to existing approaches.
Estimated spectrum adaptive postfilter and the iterative prepost filtering algirighms
NASA Technical Reports Server (NTRS)
Linares, Irving (Inventor)
2004-01-01
The invention presents The Estimated Spectrum Adaptive Postfilter (ESAP) and the Iterative Prepost Filter (IPF) algorithms. These algorithms model a number of image-adaptive post-filtering and pre-post filtering methods. They are designed to minimize Discrete Cosine Transform (DCT) blocking distortion caused when images are highly compressed with the Joint Photographic Expert Group (JPEG) standard. The ESAP and the IPF techniques of the present invention minimize the mean square error (MSE) to improve the objective and subjective quality of low-bit-rate JPEG gray-scale images while simultaneously enhancing perceptual visual quality with respect to baseline JPEG images.
A Nonlinear Adaptive Filter for Gyro Thermal Bias Error Cancellation
NASA Technical Reports Server (NTRS)
Galante, Joseph M.; Sanner, Robert M.
2012-01-01
Deterministic errors in angular rate gyros, such as thermal biases, can have a significant impact on spacecraft attitude knowledge. In particular, thermal biases are often the dominant error source in MEMS gyros after calibration. Filters, such as J\\,fEKFs, are commonly used to mitigate the impact of gyro errors and gyro noise on spacecraft closed loop pointing accuracy, but often have difficulty in rapidly changing thermal environments and can be computationally expensive. In this report an existing nonlinear adaptive filter is used as the basis for a new nonlinear adaptive filter designed to estimate and cancel thermal bias effects. A description of the filter is presented along with an implementation suitable for discrete-time applications. A simulation analysis demonstrates the performance of the filter in the presence of noisy measurements and provides a comparison with existing techniques.
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.
Adaptive Restoration of Airborne Daedalus AADS1268 ATM Thermal Data
D. Yuan; E. Doak; P. Guss; A. Will
2002-01-01
To incorporate the georegistration and restoration processes into airborne data processing in support of U.S. Department of Energy's nuclear emergency response task, we developed an adaptive restoration filter for airborne Daedalus AADS1268 ATM thermal data based on the Wiener filtering theory. Preliminary assessment shows that this filter enhances the detectability of small weak thermal anomalies in AADS1268 thermal images.
Adaptive restoration of airborne Daedalus AADS1268 ATM thermal data
NASA Astrophysics Data System (ADS)
Yuan, Ding; Doak, Edwin L.; Guss, Paul; Will, Alan
2002-03-01
To incorporate the georegistration and restoration processes into airborne data processing in support of DOE's nuclear emergency response task, we developed an adaptive restoration filter for airborne Daedalus AADS1268 ATM thermal data based on the Wiener filtering theory. Preliminary assessment shows that this filter enhances the detectability of small weak thermal anomalies in AADS1268 thermal images.
Adaptive Control Using Residual Mode Filters Applied to Wind Turbines
NASA Technical Reports Server (NTRS)
Frost, Susan A.; Balas, Mark J.
2011-01-01
Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a model reference direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will augment the adaptive controller using a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. We apply these theoretical results to design an adaptive collective pitch controller for a high-fidelity simulation of a utility-scale, variable-speed wind turbine that has minimum phase zeros.
Sidelobe reduction via adaptive FIR filtering in SAR imagery.
Degraaf, S R
1994-01-01
The paper describes a class of adaptive weighting functions that greatly reduce sidelobes, interference, and noise in Fourier transform data. By restricting the class of adaptive weighting functions, the adaptively weighted Fourier transform data can be represented as the convolution of the unweighted Fourier transform with a data adaptive FIR filter where one selects the FIR filter coefficients to maximize signal-to-interference ratio. This adaptive sidelobe reduction (ASR) procedure is analogous to Capon's (1969) minimum variance method (MVM) of adaptive spectral estimation. Unlike MVM, which provides a statistical estimate of the real-valued power spectral density, thereby estimating noise level and improving resolution, ASR provides a single-realization complex-valued estimate of the Fourier transform that suppresses sidelobes and noise. Further, the computational complexity of ASR is dramatically lower than that of MVM, which is critical for large multidimensional problems such as synthetic aperture radar (SAR) image formation. ASR performance characteristics can be varied through the choice of filter order, l(1)- or l(2)-norm filter vector constraints and a separable or nonseparable multidimensional implementation. The author compares simulated point scattering SAR imagery produced by the ASR, MVM, and MUSIC algorithms and illustrates ASR performance on three sets of collected SAR imagery.
Adaptive filtering for ECG rejection from surface EMG recordings.
Marque, C; Bisch, C; Dantas, R; Elayoubi, S; Brosse, V; Pérot, C
2005-06-01
Surface electromyograms (EMG) of back muscles are often corrupted by electrocardiogram (ECG) signals. This noise in the EMG signals does not allow to appreciate correctly the spectral content of the EMG signals and to follow its evolution during, for example, a fatigue process. Several methods have been proposed to reject the ECG noise from EMG recordings, but seldom taking into account the eventual changes in ECG characteristics during the experiment. In this paper we propose an adaptive filtering algorithm specifically developed for the rejection of the electrocardiogram corrupting surface electromyograms (SEMG). The first step of the study was to choose the ECG electrode position in order to record the ECG with a shape similar to that found in the noised SEMGs. Then, the efficiency of different algorithms were tested on 28 erector spinae SEMG recordings. The best algorithm belongs to the fast recursive least square family (FRLS). More precisely, the best results were obtained with the simplified formulation of a FRLS algorithm. As an application of the adaptive filtering, the paper compares the evolutions of spectral parameters of noised or denoised (after adaptive filtering) surface EMGs recorded on erector spinae muscles during a trunk extension. The fatigue test was analyzed on 16 EMG recordings. After adaptive filtering, mean initial values of energy and of mean power frequency (MPF) were significantly lower and higher respectively. The differences corresponded to the removal of the ECG components. Furthermore, classical fatigue criteria (increase in energy and decrease in MPF values over time during the fatigue test) were better observed on the denoised EMGs. The mean values of the slopes of the energy-time and MPF-time linear relationships differed significantly when established before and after adaptive filtering. These results account for the efficacy of the adaptive filtering method proposed here to denoise electrophysiological signals.
Adaptive texture filtering for defect inspection in ultrasound images
NASA Astrophysics Data System (ADS)
Zmola, Carl; Segal, Andrew C.; Lovewell, Brian; Nash, Charles
1993-05-01
The use of ultrasonic imaging to analyze defects and characterize materials is critical in the development of non-destructive testing and non-destructive evaluation (NDT/NDE) tools for manufacturing. To develop better quality control and reliability in the manufacturing environment advanced image processing techniques are useful. For example, through the use of texture filtering on ultrasound images, we have been able to filter characteristic textures from highly-textured C-scan images of materials. The materials have highly regular characteristic textures which are of the same resolution and dynamic range as other important features within the image. By applying texture filters and adaptively modifying their filter response, we have examined a family of filters for removing these textures.
An information theoretic approach of designing sparse kernel adaptive filters.
Liu, Weifeng; Park, Il; Principe, José C
2009-12-01
This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented. PMID:19923047
An information theoretic approach of designing sparse kernel adaptive filters.
Liu, Weifeng; Park, Il; Principe, José C
2009-12-01
This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented.
A New Method to Cancel RFI---The Adaptive Filter
NASA Astrophysics Data System (ADS)
Bradley, R.; Barnbaum, C.
1996-12-01
An increasing amount of precious radio frequency spectrum in the VHF, UHF, and microwave bands is being utilized each year to support new commercial and military ventures, and all have the potential to interfere with radio astronomy observations. Some radio spectral lines of astronomical interest occur outside the protected radio astronomy bands and are unobservable due to heavy interference. Conventional approaches to deal with RFI include legislation, notch filters, RF shielding, and post-processing techniques. Although these techniques are somewhat successful, each suffers from insufficient interference cancellation. One concept of interference excision that has not been used before in radio astronomy is adaptive interference cancellation. The concept of adaptive interference canceling was first introduced in the mid-1970s as a way to reduce unwanted noise in low frequency (audio) systems. Examples of such systems include the canceling of maternal ECG in fetal electrocardiography and the reduction of engine noise in the passenger compartment of automobiles. Only recently have high-speed digital filter chips made adaptive filtering possible in a bandwidth as large a few megahertz, finally opening the door to astronomical uses. The system consists of two receivers: the main beam of the radio telescope receives the desired signal corrupted by RFI coming in the sidelobes, and the reference antenna receives only the RFI. The reference antenna is processed using a digital adaptive filter and then subtracted from the signal in the main beam, thus producing the system output. The weights of the digital filter are adjusted by way of an algorithm that minimizes, in a least-squares sense, the power output of the system. Through an adaptive-iterative process, the interference canceler will lock onto the RFI and the filter will adjust itself to minimize the effect of the RFI at the system output. We are building a prototype 100 MHz receiver and will measure the cancellation
Streak image denoising and segmentation using adaptive Gaussian guided filter.
Jiang, Zhuocheng; Guo, Baoping
2014-09-10
In streak tube imaging lidar (STIL), streak images are obtained using a CCD camera. However, noise in the captured streak images can greatly affect the quality of reconstructed 3D contrast and range images. The greatest challenge for streak image denoising is reducing the noise while preserving details. In this paper, we propose an adaptive Gaussian guided filter (AGGF) for noise removal and detail enhancement of streak images. The proposed algorithm is based on a guided filter (GF) and part of an adaptive bilateral filter (ABF). In the AGGF, the details are enhanced by optimizing the offset parameter. AGGF-denoised streak images are significantly sharper than those denoised by the GF. Moreover, the AGGF is a fast linear time algorithm achieved by recursively implementing a Gaussian filter kernel. Experimentally, AGGF demonstrates its capacity to preserve edges and thin structures and outperforms the existing bilateral filter and domain transform filter in terms of both visual quality and peak signal-to-noise ratio performance.
Streak image denoising and segmentation using adaptive Gaussian guided filter.
Jiang, Zhuocheng; Guo, Baoping
2014-09-10
In streak tube imaging lidar (STIL), streak images are obtained using a CCD camera. However, noise in the captured streak images can greatly affect the quality of reconstructed 3D contrast and range images. The greatest challenge for streak image denoising is reducing the noise while preserving details. In this paper, we propose an adaptive Gaussian guided filter (AGGF) for noise removal and detail enhancement of streak images. The proposed algorithm is based on a guided filter (GF) and part of an adaptive bilateral filter (ABF). In the AGGF, the details are enhanced by optimizing the offset parameter. AGGF-denoised streak images are significantly sharper than those denoised by the GF. Moreover, the AGGF is a fast linear time algorithm achieved by recursively implementing a Gaussian filter kernel. Experimentally, AGGF demonstrates its capacity to preserve edges and thin structures and outperforms the existing bilateral filter and domain transform filter in terms of both visual quality and peak signal-to-noise ratio performance. PMID:25321679
Hardware implementation of a discrete-time analog adaptive filter
Donohoe, G.W.
1981-01-01
This paper describes a hardware implementation of a discrete-time adaptive filter using a bucket-brigade device (BBD) tapped analog delay line, analog voltage multipliers and operational amplifier integrators and summing circuits. Some design considerations for this class of circuits are discussed.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-01-01
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. PMID:27472336
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-01-01
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. PMID:27472336
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-07-26
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.
Extended adaptive filtering for wide-angle SAR image formation
NASA Astrophysics Data System (ADS)
Wang, Yanwei; Roberts, William; Li, Jian
2005-05-01
For two-dimensional (2-D) spectral analysis, the adaptive filtering based technologies, such as CAPON and APES (Amplitude and Phase EStimation), are developed under the implicit assumption that the data sets are rectangular. However, in real SAR applications, especially for the wide-angle cases, the collected data sets are always non-rectangular. This raises the problem of how to extend the original adaptive filtering based algorithms for such kind of scenarios. In this paper, we propose an extended adaptive filtering (EAF) approach, which includes Extended APES (E-APES) and Extended CAPON (E-CAPON), for arbitrarily shaped 2-D data. The EAF algorithms adopt a missing-data approach where the unavailable data samples close to the collected data set are assumed missing. Using a group of filter-banks with varying sizes, these algorithms are non-iterative and do not require the estimation of the unavailable samples. The improved imaging results of the proposed algorithms are demonstrated by applying them to two different SAR data sets.
NASA Astrophysics Data System (ADS)
Chen, Yangkang
2016-07-01
The seislet transform has been demonstrated to have a better compression performance for seismic data compared with other well-known sparsity promoting transforms, thus it can be used to remove random noise by simply applying a thresholding operator in the seislet domain. Since the seislet transform compresses the seismic data along the local structures, the seislet thresholding can be viewed as a simple structural filtering approach. Because of the dependence on a precise local slope estimation, the seislet transform usually suffers from low compression ratio and high reconstruction error for seismic profiles that have dip conflicts. In order to remove the limitation of seislet thresholding in dealing with conflicting-dip data, I propose a dip-separated filtering strategy. In this method, I first use an adaptive empirical mode decomposition based dip filter to separate the seismic data into several dip bands (5 or 6). Next, I apply seislet thresholding to each separated dip component to remove random noise. Then I combine all the denoised components to form the final denoised data. Compared with other dip filters, the empirical mode decomposition based dip filter is data-adaptive. One only needs to specify the number of dip components to be separated. Both complicated synthetic and field data examples show superior performance of my proposed approach than the traditional alternatives. The dip-separated structural filtering is not limited to seislet thresholding, and can also be extended to all those methods that require slope information.
Adaptive gain and filtering circuit for a sound reproduction system
NASA Technical Reports Server (NTRS)
Engebretson, A. Maynard (Inventor); O'Connell, Michael P. (Inventor)
1998-01-01
Adaptive compressive gain and level dependent spectral shaping circuitry for a hearing aid include a microphone to produce an input signal and a plurality of channels connected to a common circuit output. Each channel has a preset frequency response. Each channel includes a filter with a preset frequency response to receive the input signal and to produce a filtered signal, a channel amplifier to amplify the filtered signal to produce a channel output signal, a threshold register to establish a channel threshold level, and a gain circuit. The gain circuit increases the gain of the channel amplifier when the channel output signal falls below the channel threshold level and decreases the gain of the channel amplifier when the channel output signal rises above the channel threshold level. A transducer produces sound in response to the signal passed by the common circuit output.
Kalman filtering to suppress spurious signals in Adaptive Optics control
Poyneer, L; Veran, J P
2010-03-29
In many scenarios, an Adaptive Optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.
Adaptive control of large space structures using recursive lattice filters
NASA Technical Reports Server (NTRS)
Sundararajan, N.; Goglia, G. L.
1985-01-01
The use of recursive lattice filters for identification and adaptive control of large space structures is studied. Lattice filters were used to identify the structural dynamics model of the flexible structures. This identification model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures is control engaged. This type of validation scheme prevents instability when the overall loop is closed. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods. The method uses the Linear Quadratic Guassian/Loop Transfer Recovery (LQG/LTR) approach to ensure stability against unmodeled higher frequency modes and achieves the desired performance.
Adaptive control of large space structures using recursive lattice filters
NASA Technical Reports Server (NTRS)
Goglia, G. L.
1985-01-01
The use of recursive lattice filters for identification and adaptive control of large space structures was studied. Lattice filters are used widely in the areas of speech and signal processing. Herein, they are used to identify the structural dynamics model of the flexible structures. This identified model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures control is engaged. This type of validation scheme prevents instability when the overall loop is closed. The results obtained from simulation were compared to those obtained from experiments. In this regard, the flexible beam and grid apparatus at the Aerospace Control Research Lab (ACRL) of NASA Langley Research Center were used as the principal candidates for carrying out the above tasks. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods.
Infinite impulse response modal filtering in visible adaptive optics
NASA Astrophysics Data System (ADS)
Agapito, G.; Arcidiacono, C.; Quirós-Pacheco, F.; Puglisi, A.; Esposito, S.
2012-07-01
Diffraction limited resolution adaptive optics (AO) correction in visible wavelengths requires a high performance control. In this paper we investigate infinite impulse response filters that optimize the wavefront correction: we tested these algorithms through full numerical simulations of a single-conjugate AO system comprising an adaptive secondary mirror with 1127 actuators and a pyramid wavefront sensor (WFS). The actual practicability of the algorithms depends on both robustness and knowledge of the real system: errors in the system model may even worsen the performance. In particular we checked the robustness of the algorithms in different conditions, proving that the proposed method can reject both disturbance and calibration errors.
Adaptive bilateral filter for sharpness enhancement and noise removal.
Zhang, Buyue; Allebach, Jan P
2008-05-01
In this paper, we present the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms in that the ABF does not involve detection of edges or their orientation, or extraction of edge profiles. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure. ABF restored images are significantly sharper than those restored by the bilateral filter. Compared with an USM based sharpening method-the optimal unsharp mask (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without the halo artifacts that appear in the OUM restored image. In terms of noise removal, ABF also outperforms the bilateral filter and the OUM. We demonstrate that ABF works well for both natural images and text images. PMID:18390373
Model Adaptation for Prognostics in a Particle Filtering Framework
NASA Technical Reports Server (NTRS)
Saha, Bhaskar; Goebel, Kai Frank
2011-01-01
One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.
Combination of Adaptive Feedback Cancellation and Binaural Adaptive Filtering in Hearing Aids
NASA Astrophysics Data System (ADS)
Lombard, Anthony; Reindl, Klaus; Kellermann, Walter
2009-12-01
We study a system combining adaptive feedback cancellation and adaptive filtering connecting inputs from both ears for signal enhancement in hearing aids. For the first time, such a binaural system is analyzed in terms of system stability, convergence of the algorithms, and possible interaction effects. As major outcomes of this study, a new stability condition adapted to the considered binaural scenario is presented, some already existing and commonly used feedback cancellation performance measures for the unilateral case are adapted to the binaural case, and possible interaction effects between the algorithms are identified. For illustration purposes, a blind source separation algorithm has been chosen as an example for adaptive binaural spatial filtering. Experimental results for binaural hearing aids confirm the theoretical findings and the validity of the new measures.
Speckle reduction in ultrasound images using nonisotropic adaptive filtering.
Eom, Kie B
2011-10-01
In this article, a speckle reduction approach for ultrasound imaging that preserves important features such as edges, corners and point targets is presented. Speckle reduction is an important problem in coherent imaging, such as ultrasound imaging or synthetic aperture radar, and many speckle reduction algorithms have been developed. Speckle is a non-additive and non-white process and the reduction of speckle without blurring sharp features is known to be difficult. The new speckle reduction algorithm presented in this article utilizes a nonhomogeneous filter that adapts to the proximity and direction of the nearest important features. To remove speckle without blurring important features, the location and direction of edges in the image are estimated. Then for each pixel in the image, the distance and angle to the nearest edge are efficiently computed by a two-pass algorithm and stored in distance and angle maps. Finally for each pixel, an adaptive directional filter aligned to the nearest edge is applied. The shape and orientation of the adaptive filter are determined from the distance and angle maps. The new speckle reduction algorithm is tested with both synthesized and real ultrasound images. The performance of the new algorithm is also compared with those of other speckle reduction approaches and it is shown that the new algorithm performs favorably in reducing speckle without blurring important features.
Adaptation and the temporal delay filter of fly motion detectors.
Harris, R A; O'Carroll, D C; Laughlin, S B
1999-08-01
Recent accounts attribute motion adaptation to a shortening of the delay filter in elementary motion detectors (EMDs). Using computer modelling and recordings from HS neurons in the drone-fly Eristalis tenax, we present evidence that challenges this theory. (i) Previous evidence for a change in the delay filter comes from 'image step' (or 'velocity impulse') experiments. We note a large discrepancy between the temporal frequency tuning predicted from these experiments and the observed tuning of motion sensitive cells. (ii) The results of image step experiments are highly sensitive to the experimental method used. (iii) An apparent motion stimulus reveals a much shorter EMD delay than suggested by previous 'image step' experiments. This short delay agrees with the observed temporal frequency sensitivity of the unadapted cell. (iv) A key prediction of a shortening delay filter is that the temporal frequency optimum of the cell should show a large shift to higher temporal frequencies after motion adaptation. We show little change in the temporal or spatial frequency (and hence velocity) optima following adaptation.
Frequency-shift low-pass filtering and least mean square adaptive filtering for ultrasound imaging
NASA Astrophysics Data System (ADS)
Wang, Shanshan; Li, Chunyu; Ding, Mingyue; Yuchi, Ming
2016-04-01
Ultrasound image quality enhancement is a problem of considerable interest in medical imaging modality and an ongoing challenge to date. This paper investigates a method based on frequency-shift low-pass filtering (FSLF) and least mean square adaptive filtering (LMSAF) for ultrasound image quality enhancement. FSLF is used for processing the ultrasound signal in the frequency domain, while LMSAPF in the time domain. Firstly, FSLF shifts the center frequency of the focused signal to zero. Then the real and imaginary part of the complex data are filtered respectively by finite impulse response (FIR) low-pass filter. Thus the information around the center frequency are retained while the undesired ones, especially background noises are filtered. Secondly, LMSAF multiplies the signals with an automatically adjusted weight vector to further eliminate the noises and artifacts. Through the combination of the two filters, the ultrasound image is expected to have less noises and artifacts and higher resolution, and contrast. The proposed method was verified with the RF data of the CIRS phantom 055A captured by SonixTouch DAQ system. Experimental results show that the background noises and artifacts can be efficiently restrained, the wire object has a higher resolution and the contrast ratio (CR) can be enhanced for about 12dB to 15dB at different image depth comparing to delay-and-sum (DAS).
NASA Astrophysics Data System (ADS)
Badaoui, M. El.; Danière, J.; Guillet, F.; Servière, C.
2005-11-01
The main purpose of this study is to characterize the relative noise given out by a diesel engine, around the Top Dead Centre (TDC) by quantifying the proportions of "mechanical noise" originating mainly from piston-slap on the one hand and 'thermal noise" originating from combustion on the other hand. Two different approaches are described here to solve this problem. In the first part of the paper, the cylinder pressure is measured and used as a reference in order to reconstruct the thermal noise. Next, we propose a method based on applying a cyclic Wiener filter to the measured cylinder pressure in order to separate the noises of mechanical and thermal origins. The final result is to reduce the engine resulting noise. The second part of the paper is devoted to blind source separation (BSS) methods applied on signals issued from accelerometers placed on one of the cylinders. It develops a BSS method based on a convolutive model of non-stationary mixtures and introduces a new method based on the joint diagonalization of time varying spectral matrices of the observations. Both methods are then applied to real data and the estimated sources are finally validated by several physical arguments.
Adaptive distributed Kalman filtering with wind estimation for astronomical adaptive optics.
Massioni, Paolo; Gilles, Luc; Ellerbroek, Brent
2015-12-01
In the framework of adaptive optics (AO) for astronomy, it is a common assumption to consider the atmospheric turbulent layers as "frozen flows" sliding according to the wind velocity profile. For this reason, having knowledge of such a velocity profile is beneficial in terms of AO control system performance. In this paper we show that it is possible to exploit the phase estimate from a Kalman filter running on an AO system in order to estimate wind velocity. This allows the update of the Kalman filter itself with such knowledge, making it adaptive. We have implemented such an adaptive controller based on the distributed version of the Kalman filter, for a realistic simulation of a multi-conjugate AO system with laser guide stars on a 30 m telescope. Simulation results show that this approach is effective and promising and the additional computational cost with respect to the distributed filter is negligible. Comparisons with a previously published slope detection and ranging wind profiler are made and the impact of turbulence profile quantization is assessed. One of the main findings of the paper is that all flavors of the adaptive distributed Kalman filter are impacted more significantly by turbulence profile quantization than the static minimum mean square estimator which does not incorporate wind profile information.
A New Adaptive Framework for Collaborative Filtering Prediction.
Almosallam, Ibrahim A; Shang, Yi
2008-06-01
Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix's system.
A New Adaptive Framework for Collaborative Filtering Prediction
Almosallam, Ibrahim A.; Shang, Yi
2010-01-01
Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix’s system. PMID:21572924
Switched Band-Pass Filters for Adaptive Transceivers
NASA Technical Reports Server (NTRS)
Wang, Ray
2007-01-01
Switched band-pass filters are key components of proposed adaptive, software- defined radio transceivers that would be parts of envisioned digital-data-communication networks that would enable real-time acquisition and monitoring of data from geographically distributed sensors. Examples of sensors to be connected to such networks include security cameras, radio-frequency identification units, and geolocation units based on the Global Positioning System. Through suitable software configuration and without changing hardware, these transceivers could be made to operate according to any of a number of complex wireless-communication standards that could be characterized by diverse modulation schemes, bandwidths, and data-handling protocols. The adaptive transceivers would include field-programmable gate arrays (FPGAs) and digital signal-processing hardware. In the receiving path of a transceiver, the incoming signal would be amplified by a low-noise amplifier (LNA). The output spectrum of the LNA would be processed by a band-pass filter operating in the frequency range between 900 MHz and 2.4 GHz. Then a down-converter would translate the signal to a lower frequency range to facilitate analog-to-digital conversion, which would be followed by baseband processing by one or more FPGAs. In the transmitting path, a digital stream would first be converted to an analog signal, which would then be up-converted to a selected frequency band before being applied to a transmitting power amplifier. The aforementioned band-pass filter in the receiving path would be a combination of resonant inductor-and-capacitor filters and switched band-pass filters. The overall combination would implement a switch function designed mathematically to exhibit desired frequency responses and to switch the signal in each frequency band to an analog-to-digital converter appropriate for that band to produce a digital intermediate-frequency signal for digital signal processing.
Adaptive filters and internal models: multilevel description of cerebellar function.
Porrill, John; Dean, Paul; Anderson, Sean R
2013-11-01
Cerebellar function is increasingly discussed in terms of engineering schemes for motor control and signal processing that involve internal models. To address the relation between the cerebellum and internal models, we adopt the chip metaphor that has been used to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections. This metaphor indicates that identifying the function of a particular cerebellar chip requires knowledge of both the general microcircuit algorithm and the chip's individual connections. Here we use a popular candidate algorithm as embodied in the adaptive filter, which learns to decorrelate its inputs from a reference ('teaching', 'error') signal. This algorithm is computationally powerful enough to be used in a very wide variety of engineering applications. However, the crucial issue is whether the external connectivity required by such applications can be implemented biologically. We argue that some applications appear to be in principle biologically implausible: these include the Smith predictor and Kalman filter (for state estimation), and the feedback-error-learning scheme for adaptive inverse control. However, even for plausible schemes, such as forward models for noise cancellation and novelty-detection, and the recurrent architecture for adaptive inverse control, there is unlikely to be a simple mapping between microzone function and internal model structure. This initial analysis suggests that cerebellar involvement in particular behaviours is therefore unlikely to have a neat classification into categories such as 'forward model'. It is more likely that cerebellar microzones learn a task-specific adaptive-filter operation which combines a number of signal-processing roles.
NASA Astrophysics Data System (ADS)
Meng, Yang; Gao, Shesheng; Zhong, Yongmin; Hu, Gaoge; Subic, Aleksandar
2016-03-01
The use of the direct filtering approach for INS/GNSS integrated navigation introduces nonlinearity into the system state equation. As the unscented Kalman filter (UKF) is a promising method for nonlinear problems, an obvious solution is to incorporate the UKF concept in the direct filtering approach to address the nonlinearity involved in INS/GNSS integrated navigation. However, the performance of the standard UKF is dependent on the accurate statistical characterizations of system noise. If the noise distributions of inertial instruments and GNSS receivers are not appropriately described, the standard UKF will produce deteriorated or even divergent navigation solutions. This paper presents an adaptive UKF with noise statistic estimator to overcome the limitation of the standard UKF. According to the covariance matching technique, the innovation and residual sequences are used to determine the covariance matrices of the process and measurement noises. The proposed algorithm can estimate and adjust the system noise statistics online, and thus enhance the adaptive capability of the standard UKF. Simulation and experimental results demonstrate that the performance of the proposed algorithm is significantly superior to that of the standard UKF and adaptive-robust UKF under the condition without accurate knowledge on system noise, leading to improved navigation precision.
Adaptive filtering of Echelle spectra of distant Quasars
NASA Technical Reports Server (NTRS)
Priebe, A.; Liebscher, D.-E.; Lorenz, H.; Richter, G.-M.
1992-01-01
The study of the Ly alpha - forest of distant (approximately greater than 3) Quasars is an important tool in obtaining a more detailed picture of the distribution of matter along the line of sight and thus of the general distribution of matter in the Universe and is therefore of important cosmological significance. Obviously, this is one of the tasks where spectral resolution plays an important role. The spectra used were obtained with the EFOSC at the ESO 3.6m telescope. Applying for the data reduction the standard Echelle procedure, as it is implemented for instance in the MIDAS-package, one uses stationary filters (e.g. median) for noise and cosmic particle event reduction in the 2-dimensional Echelle image. These filters are useful if the spatial spectrum of the noise reaches essentially higher frequencies then the highest resolution features in the image. Otherwise the resolution in the data will be degraded and the spectral lines smoothed. However, in the Echelle spectra the highest resolution is already in the range of one or a few pixels and therefore stationary filtering means always a loss of resolution. An Echelle reduction procedure on the basis of a space variable filter described which recognizes the local resolution in the presence of noise and adapts to it is developed. It was shown that this technique leads to an improvement in resolution by a factor of 2 with respect to standard procedures.
An Adaptive Multipath Mitigation Filter for GNSS Applications
NASA Astrophysics Data System (ADS)
Chang, Chung-Liang; Juang, Jyh-Ching
2008-12-01
Global navigation satellite system (GNSS) is designed to serve both civilian and military applications. However, the GNSS performance suffers from several errors, such as ionosphere delay, troposphere delay, ephemeris error, and receiver noise and multipath. Among these errors, the multipath is one of the most unpredictable error sources in high-accuracy navigation. This paper applies a modified adaptive filter to reduce code and carrier multipath errors in GPS. The filter employs a tap-delay line with an Adaline network to estimate the direction and the delayed-signal parameters. Then, the multipath effect is mitigated by subtracting the estimated multipath effects from the processed correlation function. The hardware complexity of the method is also compared with other existing methods. Simulation results show that the proposed method using field data has a significant reduction in multipath error especially in short-delay multipath scenarios.
Fast Source Camera Identification Using Content Adaptive Guided Image Filter.
Zeng, Hui; Kang, Xiangui
2016-03-01
Source camera identification (SCI) is an important topic in image forensics. One of the most effective fingerprints for linking an image to its source camera is the sensor pattern noise, which is estimated as the difference between the content and its denoised version. It is widely believed that the performance of the sensor-based SCI heavily relies on the denoising filter used. This study proposes a novel sensor-based SCI method using content adaptive guided image filter (CAGIF). Thanks to the low complexity nature of the CAGIF, the proposed method is much faster than the state-of-the-art methods, which is a big advantage considering the potential real-time application of SCI. Despite the advantage of speed, experimental results also show that the proposed method can achieve comparable or better performance than the state-of-the-art methods in terms of accuracy. PMID:27404627
Statistical-uncertainty-based adaptive filtering of lidar signals
Fuehrer, P. L.; Friehe, C. A.; Hristov, T. S.; Cooper, D. I.; Eichinger, W. E.
2000-02-10
An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometerological humidity data were used to calibrate the ratio of the lidar gains of the H{sub 2}O and the N{sub 2} photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem. (c) 2000 Optical Society of America.
Statistical-uncertainty-based adaptive filtering of lidar signals.
Fuehrer, P L; Friehe, C A; Hristov, T S; Cooper, D I; Eichinger, W E
2000-02-10
An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometeorological humidity data were used to calibrate the ratio of the lidar gains of the H(2)O and the N(2) photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem.
Attitude determination using an adaptive multiple model filtering Scheme
NASA Astrophysics Data System (ADS)
Lam, Quang; Ray, Surendra N.
1995-05-01
Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown
Multimodal Medical Image Fusion by Adaptive Manifold Filter.
Geng, Peng; Liu, Shuaiqi; Zhuang, Shanna
2015-01-01
Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images. PMID:26664494
An adaptive filtered back-projection for photoacoustic image reconstruction
Huang, He; Bustamante, Gilbert; Peterson, Ralph; Ye, Jing Yong
2015-05-15
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
An adaptive filtered back-projection for photoacoustic image reconstruction
Huang, He; Bustamante, Gilbert; Peterson, Ralph; Ye, Jing Yong
2015-01-01
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
A wavelet packet adaptive filtering algorithm for enhancing manatee vocalizations.
Gur, M Berke; Niezrecki, Christopher
2011-04-01
Approximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation.
A wavelet packet adaptive filtering algorithm for enhancing manatee vocalizations.
Gur, M Berke; Niezrecki, Christopher
2011-04-01
Approximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation. PMID:21476661
Controller-structure interaction compensation using adaptive residual mode filters
NASA Technical Reports Server (NTRS)
Davidson, Roger A.; Balas, Mark J.
1990-01-01
It is not feasible to construct controllers for large space structures or large scale systems (LSS's) which are of the same order as the structures. The complexity of the dynamics of these systems is such that full knowledge of its behavior cannot by processed by today's controller design methods. The controller for system performance of such a system is therefore based on a much smaller reduced-order model (ROM). Unfortunately, the interaction between the LSS and the ROM-based controller can produce instabilities in the closed-loop system due to the unmodeled dynamics of the LSS. Residual mode filters (RMF's) allow the systematic removal of these instabilities in a matter which does not require a redesign of the controller. In addition RMF's have a strong theoretical basis. As simple first- or second-order filters, the RMF CSI compensation technique is at once modular, simple and highly effective. RMF compensation requires knowledge of the dynamics of the system modes which resulted in the previous closed-loop instabilities (the residual modes), but this information is sometimes known imperfectly. An adaptive, self-tuning RMF design, which compensates for uncertainty in the frequency of the residual mode, has been simulated using continuous-time and discrete-time models of a flexible robot manipulator. Work has also been completed on the discrete-time experimental implementation on the Martin Marietta flexible robot manipulator experiment. This paper will present the results of that work on adaptive, self-tuning RMF's, and will clearly show the advantage of this adaptive compensation technique for controller-structure interaction (CSI) instabilities in actively-controlled LSS's.
Wiener-matrix image restoration beyond the sampling passband
NASA Technical Reports Server (NTRS)
Rahman, Zia-Ur; Alter-Gartenberg, Rachel; Fales, Carl L.; Huck, Friedrich O.
1991-01-01
A finer-than-sampling-lattice resolution image can be obtained using multiresponse image gathering and Wiener-matrix restoration. The multiresponse image gathering weighs the within-passband and aliased signal components differently, allowing the Wiener-matrix restoration filter to unscramble these signal components and restore spatial frequencies beyond the sampling passband of the photodetector array. A multiresponse images can be reassembled into a single minimum mean square error image with a resolution that is sq rt A times finer than the photodetector-array sampling lattice.
Simple method for adaptive filtering of motion artifacts in E-textile wearable ECG sensors.
Alkhidir, Tamador; Sluzek, Andrzej; Yapici, Murat Kaya
2015-08-01
In this paper, we have developed a simple method for adaptive out-filtering of the motion artifact from the electrocardiogram (ECG) obtained by using conductive textile electrodes. The textile electrodes were placed on the left and the right wrist to measure ECG through lead-1 configuration. The motion artifact was induced by simple hand movements. The reference signal for adaptive filtering was obtained by placing additional electrodes at one hand to capture the motion of the hand. The adaptive filtering was compared to independent component analysis (ICA) algorithm. The signal-to-noise ratio (SNR) for the adaptive filtering approach was higher than independent component analysis in most cases.
Adaptive noise cancellation based on beehive pattern evolutionary digital filter
NASA Astrophysics Data System (ADS)
Zhou, Xiaojun; Shao, Yimin
2014-01-01
Evolutionary digital filtering (EDF) exhibits the advantage of avoiding the local optimum problem by using cloning and mating searching rules in an adaptive noise cancellation system. However, convergence performance is restricted by the large population of individuals and the low level of information communication among them. The special beehive structure enables the individuals on neighbour beehive nodes to communicate with each other and thus enhance the information spread and random search ability of the algorithm. By introducing the beehive pattern evolutionary rules into the original EDF, this paper proposes an improved beehive pattern evolutionary digital filter (BP-EDF) to overcome the defects of the original EDF. In the proposed algorithm, a new evolutionary rule which combines competing cloning, complete cloning and assistance mating methods is constructed to enable the individuals distributed on the beehive to communicate with their neighbours. Simulation results are used to demonstrate the improved performance of the proposed algorithm in terms of convergence speed to the global optimum compared with the original methods. Experimental results also verify the effectiveness of the proposed algorithm in extracting feature signals that are contaminated by significant amounts of noise during the fault diagnosis task.
Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting
NASA Astrophysics Data System (ADS)
Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.
2014-12-01
Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.
Adaptive data filtering of inertial sensors with variable bandwidth.
Alam, Mushfiqul; Rohac, Jan
2015-02-02
MEMS (micro-electro-mechanical system)-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU), which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor's behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer's data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing.
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter.
Zhang, Zhen; Ma, Yaopeng
2016-02-06
A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively.
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter.
Zhang, Zhen; Ma, Yaopeng
2016-01-01
A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively. PMID:26861349
NASA Astrophysics Data System (ADS)
Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang
2016-02-01
Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix ‘R’ and the system noise V-C matrix ‘Q’. Then, the global filter uses R to calculate the information allocation factor ‘β’ for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-07-16
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.
An Adaptive Fourier Filter for Relaxing Time Stepping Constraints for Explicit Solvers
Gelb, Anne; Archibald, Richard K
2015-01-01
Filtering is necessary to stabilize piecewise smooth solutions. The resulting diffusion stabilizes the method, but may fail to resolve the solution near discontinuities. Moreover, high order filtering still requires cost prohibitive time stepping. This paper introduces an adaptive filter that controls spurious modes of the solution, but is not unnecessarily diffusive. Consequently we are able to stabilize the solution with larger time steps, but also take advantage of the accuracy of a high order filter.
Adaptive spatial filtering for daytime satellite quantum key distribution
NASA Astrophysics Data System (ADS)
Gruneisen, Mark T.; Sickmiller, Brett A.; Flanagan, Michael B.; Black, James P.; Stoltenberg, Kurt E.; Duchane, Alexander W.
2014-11-01
The rate of secure key generation (SKG) in quantum key distribution (QKD) is adversely affected by optical noise and loss in the quantum channel. In a free-space atmospheric channel, the scattering of sunlight into the channel can lead to quantum bit error ratios (QBERs) sufficiently large to preclude SKG. Furthermore, atmospheric turbulence limits the degree to which spatial filtering can reduce sky noise without introducing signal losses. A system simulation quantifies the potential benefit of tracking and higher-order adaptive optics (AO) technologies to SKG rates in a daytime satellite engagement scenario. The simulations are performed assuming propagation from a low-Earth orbit (LEO) satellite to a terrestrial receiver that includes an AO system comprised of a Shack-Hartmann wave-front sensor (SHWFS) and a continuous-face-sheet deformable mirror (DM). The effects of atmospheric turbulence, tracking, and higher-order AO on the photon capture efficiency are simulated using statistical representations of turbulence and a time-domain waveoptics hardware emulator. Secure key generation rates are then calculated for the decoy state QKD protocol as a function of the receiver field of view (FOV) for various pointing angles. The results show that at FOVs smaller than previously considered, AO technologies can enhance SKG rates in daylight and even enable SKG where it would otherwise be prohibited as a consequence of either background optical noise or signal loss due to turbulence effects.
Burst noise reduction of image by decimation and adaptive weighted median filter
NASA Astrophysics Data System (ADS)
Nakayama, Fumitaka; Meguro, Mitsuhiko; Hamada, Nozomu
2000-12-01
The removal of noise in image is one of the important issues, and useful as a preprocessing for edge detection, motion estimation and so on. Recently, many studies on the nonlinear digital filter for impulsive noise reduction have been reported. The median filter, the representative of the nonlinear filters, is very effective for removing impulsive noise and preserving sharp edge. In some cases, burst (i.e., successive) impulsive noise is added to image, and this type of noise is difficult to remove by using the median filter. In this paper, we propose an Adaptive Weighted Median (AWM) filter with Decimation (AWM-D filter) for burst noise reduction. This method can also be applied to recover large destructive regions, such as blotch and scratch. The proposed filter is an extension of the Decimated Median (DM) filter, which is useful for reducing successive impulsive noise. The DM filter can split long impulsive noise sequences into short ones, and remove burst noise in spite of the short filter window. Nevertheless, the DM filter also has two disadvantages. One is that the signals without added noise is unnecessary filtered. The other is that the position information in the window is not considered in the weight determinative process, as common in the median type filter. To improve detail-preserving property of the DM filter, we use the noise detection procedure and the AWM-D filter, which can be tuned by Least Mean Absolute (LMA) algorithm. The AWM-D filter preserves details more precisely than the median-type filter, because the AWM-D filter has the weights that can control the filter output. Through some simulations, the higher performance of the proposed filter is shown compared with the simple median, the WM filter, and the DM filter.
Non-adaptive robust filters for speckle noise reduction
NASA Astrophysics Data System (ADS)
Frery, Alejandro C.; Santanna, Sidnei J. S.
1993-06-01
After briefly reviewing some classical filters for speckle removal, five filters with characteristics of robustness, suitable for speckle noise reduction, are derived and implemented. These filters are the ones based on the trimmed maximum likelihood and moments estimators, the ones based on the median, on the inter-quartile range, and on the median absolute deviation. Assuming that observations within a synthetic aperture radar image are outcomes of independent Rayleigh random variables, these filters exhibit a good performance from both the signal-to-noise reduction and from the edge preserving criteria. The problem of filtering in an image is posed as an estimation problem.
Adaptive RSOV filter using the FELMS algorithm for nonlinear active noise control systems
NASA Astrophysics Data System (ADS)
Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou; Li, Tianrui
2013-01-01
This paper presents a recursive second-order Volterra (RSOV) filter to solve the problems of signal saturation and other nonlinear distortions that occur in nonlinear active noise control systems (NANC) used for actual applications. Since this nonlinear filter based on an infinite impulse response (IIR) filter structure can model higher than second-order and third-order nonlinearities for systems where the nonlinearities are harmonically related, the RSOV filter is more effective in NANC systems with either a linear secondary path (LSP) or a nonlinear secondary path (NSP). Simulation results clearly show that the RSOV adaptive filter using the multichannel structure filtered-error least mean square (FELMS) algorithm can further greatly reduce the computational burdens and is more suitable to eliminate nonlinear distortions in NANC systems than a SOV filter, a bilinear filter and a third-order Volterra (TOV) filter.
Infrared Target Acquisition Using An Adaptive Difference-Of-Boxes Filter
NASA Astrophysics Data System (ADS)
Guissin, Rami
1990-01-01
A variety of spatial filters have been previously proposed as detection filters for automated target acquisition. One class of filters, namely the matched filter, is designed for maximimum signal to noise response at true target locations. The filter design is a function of target dimensions and intensity distributions, and of the corresponding background spectrum. The filter sensitivity to target dimensions may be overcome by adapting the filter's dimensions to the incoming image signal, or by the economical use of (at least) two filters, designed separately for small and large targets. The robustness of the Difference-of-Boxes (DOB) filter is established for a class of targets having smooth, 2nd order intensity distributions, in the presence of both white noise and cluttered backgrounds.
NASA Technical Reports Server (NTRS)
Benardini, James N.; Koukol, Robert C.; Schubert, Wayne W.; Morales, Fabian; Klatte, Marlin F.
2012-01-01
A report describes an adaptation of a filter assembly to enable it to be used to filter out microorganisms from a propulsion system. The filter assembly has previously been used for particulates greater than 2 micrometers. Projects that utilize large volumes of nonmetallic materials of planetary protection concern pose a challenge to their bioburden budget, as a conservative specification value of 30 spores per cubic centimeter is typically used. Helium was collected utilizing an adapted filtration approach employing an existing Millipore filter assembly apparatus used by the propulsion team for particulate analysis. The filter holder on the assembly has a 47-mm diameter, and typically a 1.2-5 micrometer pore-size filter is used for particulate analysis making it compatible with commercially available sterilization filters (0.22 micrometers) that are necessary for biological sampling. This adaptation to an existing technology provides a proof-of-concept and a demonstration of successful use in a ground equipment system. This adaptation has demonstrated that the Millipore filter assembly can be utilized to filter out microorganisms from a propulsion system, whereas in previous uses the filter assembly was utilized for particulates greater than 2 micrometers.
A note about Norbert Wiener and his contribution to Harmonic Analysis and Tauberian Theorems
NASA Astrophysics Data System (ADS)
Almira, J. M.; Romero, A. E.
2009-05-01
In this note we explain the main motivations Norbert Wiener had for the creation of his Generalized Harmonic Analysis [13] and his Tauberian Theorems [14]. Although these papers belong to the most pure mathematical tradition, they were deeply based on some Engineering and Physics Problems and Wiener was able to use them for such diverse areas as Optics, Brownian motion, Filter Theory, Prediction Theory and Cybernetics.
Analysis of dynamic deformation processes with adaptive KALMAN-filtering
NASA Astrophysics Data System (ADS)
Eichhorn, Andreas
2007-05-01
In this paper the approach of a full system analysis is shown quantifying a dynamic structural ("white-box"-) model for the calculation of thermal deformations of bar-shaped machine elements. The task was motivated from mechanical engineering searching new methods for the precise prediction and computational compensation of thermal influences in the heating and cooling phases of machine tools (i.e. robot arms, etc.). The quantification of thermal deformations under variable dynamic loads requires the modelling of the non-stationary spatial temperature distribution inside the object. Based upon FOURIERS law of heat flow the high-grade non-linear temperature gradient is represented by a system of partial differential equations within the framework of a dynamic Finite Element topology. It is shown that adaptive KALMAN-filtering is suitable to quantify relevant disturbance influences and to identify thermal parameters (i.e. thermal diffusivity) with a deviation of only 0,2%. As result an identified (and verified) parametric model for the realistic prediction respectively simulation of dynamic temperature processes is presented. Classifying the thermal bend as the main deformation quantity of bar-shaped machine tools, the temperature model is extended to a temperature deformation model. In lab tests thermal load steps are applied to an aluminum column. Independent control measurements show that the identified model can be used to predict the columns bend with a mean deviation (
Adaptive filter for mine detection and classification in side-scan sonar imagery
NASA Astrophysics Data System (ADS)
Aridgides, Tom; Antoni, Diana; Fernandez, Manuel F.; Dobeck, Gerald J.
1995-06-01
A need exists to develop robust automatic techniques for discriminating between minelike target and clutter returns in sonar imagery. To address this need, an adaptive clutter suppression linear FIR filtering technique has been developed and applied to side scan sonar imagery data. The adaptive filtering procedure consists of four stages. First, a normalized average target signature (shape) within the filter window is computed using training set data. Second, the background clutter covariance matrix is computed by scanning the filter window over the data. Third, following substitutions of the average target signature and covariance expressions into a set of normal equations, an adaptive filter is computed which simultaneously suppresses the background clutter while preserving the peak of the average target signature. Finally, the data is filtered using the 2D adaptive range-crossrange filter. The overall mine detection processing string includes automatic gain control, data decimation, adaptive clutter filtering (ACF), 2D normalization, thresholding, exceedance clustering, limiting the number of exceedances and secondary thresholding processing blocks. The utility of the ACF processing string was demonstrated with three side scan sonar datasets. The ACF algorithm provided average probability of detection and false alarm rate performance similar to that obtained when utilizing an expert sonar operator.
NASA Astrophysics Data System (ADS)
Leibovich, N.; Barkai, E.
2015-08-01
The Wiener-Khinchin theorem shows how the power spectrum of a stationary random signal I (t ) is related to its correlation function ⟨I (t )I (t +τ )⟩ . We consider nonstationary processes with the widely observed aging correlation function ⟨I (t )I (t +τ )⟩˜tγϕEA(τ /t ) and relate it to the sample spectrum. We formulate two aging Wiener-Khinchin theorems relating the power spectrum to the time- and ensemble-averaged correlation functions, discussing briefly the advantages of each. When the scaling function ϕEA(x ) exhibits a nonanalytical behavior in the vicinity of its small argument we obtain the aging 1 /f -type of spectrum. We demonstrate our results with three examples: blinking quantum dots, single-file diffusion, and Brownian motion in a logarithmic potential, showing that our approach is valid for a wide range of physical mechanisms.
Adaptive multidirectional frequency domain filter for noise removal in wrapped phase patterns.
Liu, Guixiong; Chen, Dongxue; Peng, Yanhua; Zeng, Qilin
2016-08-01
In order to avoid the detrimental effects of excessive noise in the phase fringe patterns of a laser digital interferometer over the accuracy of phase unwrapping and the successful detection of mechanical fatigue defects, an effective method of adaptive multidirectional frequency domain filtering is introduced based on the characteristics of the energy spectrum of localized wrapped phase patterns. Not only can this method automatically set the cutoff frequency, but it can also effectively filter out noise while preserving the image edge information. Compared with the sine and cosine transform filtering and the multidirectional frequency domain filtering, the experimental results demonstrate that the image filtered by our method has the fewest number of residues and is the closest to the noise-free image, compared to the two aforementioned methods, demonstrating the effectiveness of this adaptive multidirectional frequency domain filter. PMID:27505376
An Efficient Adaptive Weighted Switching Median Filter for Removing High Density Impulse Noise
NASA Astrophysics Data System (ADS)
Nair, Madhu S.; Ameera Mol, P. M.
2014-09-01
Restoration of images corrupted by impulse noise is a very active research area in image processing. In this paper, an Efficient Adaptive Weighted Switching Median filter for restoration of images that are corrupted by high density impulse noise is proposed. The filtering is performed as a two phase process—a detection phase followed by a filtering phase. In the proposed method, noise detection is done by HEIND algorithm proposed by Duan et al. The filtering algorithm is then applied to the pixels which are detected as noisy by the detection algorithm. All uncorrupted pixels in the image are left unchanged. The filtering window size is chosen adaptively depending on the local noise distribution around each corrupted pixels. Noisy pixels are replaced by a weighted median value of uncorrupted pixels in the filtering window. The weight value assigned to each uncorrupted pixels depends on its closeness to the central pixel.
Microwave Photonic Filters for Interference Cancellation and Adaptive Beamforming
NASA Astrophysics Data System (ADS)
Chang, John
Wireless communication has experienced an explosion of growth, especially in the past half- decade, due to the ubiquity of wireless devices, such as tablets, WiFi-enabled devices, and especially smartphones. Proliferation of smartphones with powerful processors and graphic chips have given an increasing amount of people the ability to access anything from anywhere. Unfortunately, this ease of access has greatly increased mobile wireless bandwidth and have begun to stress carrier networks and spectra. Wireless interference cancellation will play a big role alongside the popularity of wire- less communication. In this thesis, we will investigate optical signal processing methods for wireless interference cancellation methods. Optics provide the perfect backdrop for interference cancellation. Mobile wireless data is already aggregated and transported through fiber backhaul networks in practice. By sandwiching the signal processing stage between the receiver and the fiber backhaul, processing can easily be done locally in one location. Further, optics offers the advantages of being instantaneously broadband and size, weight, and power (SWAP). We are primarily concerned with two methods for interference cancellation, based on microwave photonic filters, in this thesis. The first application is for a co-channel situation, in which a transmitter and receiver are co-located and transmitting at the same frequency. A novel analog optical technique extended for multipath interference cancellation of broadband signals is proposed and experimentally demonstrated in this thesis. The proposed architecture was able to achieve a maximum of 40 dB of cancellation over 200 MHz and 50 dB of cancellation over 10 MHz. The broadband nature of the cancellation, along with its depth, demonstrates both the precision of the optical components and the validity of the architecture. Next, we are interested in a scenario with dynamically changing interference, which requires an adaptive photonic
Impulse radar imaging for dispersive concrete using inverse adaptive filtering techniques
Arellano, J.; Hernandez, J.M.; Brase, J.
1993-05-01
This publication addresses applications of a delayed inverse model adaptive filter for modeled data obtained from short-pulse radar reflectometry. To determine the integrity of concrete, a digital adaptive filter was used, which allows compensation of dispersion and clutter generated by the concrete. A standard set of weights produced by an adaptive filter are used on modeled data to obtain the inverse-impulse response of the concrete. The data for this report include: Multiple target, nondispersive data; single-target, variable-size dispersive data; single-target, variable-depth dispersive data; and single-target, variable transmitted-pulse-width dispersive data. Results of this simulation indicate that data generated by the weights of the adaptive filter, coupled with a two-dimensional, synthetic-aperture focusing technique, successfully generate two-dimensional images of targets within the concrete from modeled data.
Adaptive Filtering for Large Space Structures: A Closed-Form Solution
NASA Technical Reports Server (NTRS)
Rauch, H. E.; Schaechter, D. B.
1985-01-01
In a previous paper Schaechter proposes using an extended Kalman filter to estimate adaptively the (slowly varying) frequencies and damping ratios of a large space structure. The time varying gains for estimating the frequencies and damping ratios can be determined in closed form so it is not necessary to integrate the matrix Riccati equations. After certain approximations, the time varying adaptive gain can be written as the product of a constant matrix times a matrix derived from the components of the estimated state vector. This is an important savings of computer resources and allows the adaptive filter to be implemented with approximately the same effort as the nonadaptive filter. The success of this new approach for adaptive filtering was demonstrated using synthetic data from a two mode system.
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg Y; Fernández, Eduardo
2010-01-01
In this paper, the fast least-mean-squares (LMS) algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and the signal processing stage was carried out by using both conventional filtering, which was already shown in a previous paper, and optimal adaptive filtering. The adaptive filtering process relied on the LMS adaptive filtering family, which has shown to have very good convergence and robustness properties, and here a comparative analysis between the results of the application of the conventional LMS algorithm and the fast LMS algorithm to solve a real-life filtering problem was carried out. In short, in this paper the piezoresistive accelerometer was tested for a multi-frequency acceleration excitation. Due to the kind of test conducted in this paper, the use of conventional filtering was discarded and the choice of one adaptive filter over the other was based on the signal-to-noise ratio improvement and the convergence rate. PMID:22315579
Adaptive Kalman-Bucy filter for differential absorption lidar time series data.
Warren, R E
1987-11-15
An extension of the Kalman-Bucy algorithm for on-line estimation of multimaterial path-integrated concentration from multiwavelength differential absorption lidar time series data is presented in which the system model covariance is adaptively estimated from the input data. Performance of the filter is compared with that of a nonadaptive Kalman-Bucy filter using synthetic and actual lidar data.
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg Y.; Fernández, Eduardo
2010-01-01
In this paper, the fast least-mean-squares (LMS) algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and the signal processing stage was carried out by using both conventional filtering, which was already shown in a previous paper, and optimal adaptive filtering. The adaptive filtering process relied on the LMS adaptive filtering family, which has shown to have very good convergence and robustness properties, and here a comparative analysis between the results of the application of the conventional LMS algorithm and the fast LMS algorithm to solve a real-life filtering problem was carried out. In short, in this paper the piezoresistive accelerometer was tested for a multi-frequency acceleration excitation. Due to the kind of test conducted in this paper, the use of conventional filtering was discarded and the choice of one adaptive filter over the other was based on the signal-to-noise ratio improvement and the convergence rate. PMID:22315579
Adaptive box filters for removal of random noise from digital images
Eliason, E.M.; McEwen, A.S.
1990-01-01
We have developed adaptive box-filtering algorithms to (1) remove random bit errors (pixel values with no relation to the image scene) and (2) smooth noisy data (pixels related to the image scene but with an additive or multiplicative component of noise). For both procedures, we use the standard deviation (??) of those pixels within a local box surrounding each pixel, hence they are adaptive filters. This technique effectively reduces speckle in radar images without eliminating fine details. -from Authors
Adaptive alpha-trimmed mean filters under deviations from assumed noise model.
Oten, Remzi; de Figueiredo, Rui J P
2004-05-01
Alpha-trimmed mean filters are widely used for the restoration of signals and images corrupted by additive non-Gaussian noise. They are especially preferred if the underlying noise deviates from Gaussian with the impulsive noise components. The key design issue of these filters is to select its only parameter, alpha, optimally for a given noise type. In image restoration, adaptive filters utilize the flexibility of selecting alpha according to some local noise statistics. In the present paper, we first review the existing adaptive alpha-trimmed mean filter schemes. We then analyze the performance of these filters when the underlying noise distribution deviates from the Gaussian and does not satisfy the assumptions such as symmetry. Specifically, the clipping effect and the mixed noise cases are analyzed. We also present a new adaptive alpha-trimmed filter implementation that detects the nonsymmetry points locally and applies alpha-trimmed mean filter that trims out the outlier pixels such as edges or impulsive noise according to this local decision. Comparisons of the speed and filtering performances under deviations from symmetry and Gaussian assumptions show that the proposed filter is a very good alternative to the existing schemes. PMID:15376595
Object tracking with adaptive HOG detector and adaptive Rao-Blackwellised particle filter
NASA Astrophysics Data System (ADS)
Rosa, Stefano; Paleari, Marco; Ariano, Paolo; Bona, Basilio
2012-01-01
Scenarios for a manned mission to the Moon or Mars call for astronaut teams to be accompanied by semiautonomous robots. A prerequisite for human-robot interaction is the capability of successfully tracking humans and objects in the environment. In this paper we present a system for real-time visual object tracking in 2D images for mobile robotic systems. The proposed algorithm is able to specialize to individual objects and to adapt to substantial changes in illumination and object appearance during tracking. The algorithm is composed by two main blocks: a detector based on Histogram of Oriented Gradient (HOG) descriptors and linear Support Vector Machines (SVM), and a tracker which is implemented by an adaptive Rao-Blackwellised particle filter (RBPF). The SVM is re-trained online on new samples taken from previous predicted positions. We use the effective sample size to decide when the classifier needs to be re-trained. Position hypotheses for the tracked object are the result of a clustering procedure applied on the set of particles. The algorithm has been tested on challenging video sequences presenting strong changes in object appearance, illumination, and occlusion. Experimental tests show that the presented method is able to achieve near real-time performances with a precision of about 7 pixels on standard video sequences of dimensions 320 × 240.
Transformations of Wiener measure and orthogonal expansions
NASA Astrophysics Data System (ADS)
Dorogovtsev, Andrey A.; Riabov, Georgii
2016-08-01
In this paper we study the structure of square integrable functionals measurable with respect to coalescing stochastic flows. The case of the Wiener process stopped at the moment of hitting an irregular continuous level is considered. Relying on the change of measure technique, we present a new construction of multiple stochastic integrals with respect to stopped Wiener process. An intrinsic analogue of the Itô-Wiener expansion for the space of square integrable functionals measurable with respect to the stopped Wiener process is constructed.
On Wiener polarity index of bicyclic networks
NASA Astrophysics Data System (ADS)
Ma, Jing; Shi, Yongtang; Wang, Zhen; Yue, Jun
2016-01-01
Complex networks are ubiquitous in biological, physical and social sciences. Network robustness research aims at finding a measure to quantify network robustness. A number of Wiener type indices have recently been incorporated as distance-based descriptors of complex networks. Wiener type indices are known to depend both on the network’s number of nodes and topology. The Wiener polarity index is also related to the cluster coefficient of networks. In this paper, based on some graph transformations, we determine the sharp upper bound of the Wiener polarity index among all bicyclic networks. These bounds help to understand the underlying quantitative graph measures in depth.
On Wiener polarity index of bicyclic networks
Ma, Jing; Shi, Yongtang; Wang, Zhen; Yue, Jun
2016-01-01
Complex networks are ubiquitous in biological, physical and social sciences. Network robustness research aims at finding a measure to quantify network robustness. A number of Wiener type indices have recently been incorporated as distance-based descriptors of complex networks. Wiener type indices are known to depend both on the network’s number of nodes and topology. The Wiener polarity index is also related to the cluster coefficient of networks. In this paper, based on some graph transformations, we determine the sharp upper bound of the Wiener polarity index among all bicyclic networks. These bounds help to understand the underlying quantitative graph measures in depth. PMID:26750820
On Wiener polarity index of bicyclic networks.
Ma, Jing; Shi, Yongtang; Wang, Zhen; Yue, Jun
2016-01-01
Complex networks are ubiquitous in biological, physical and social sciences. Network robustness research aims at finding a measure to quantify network robustness. A number of Wiener type indices have recently been incorporated as distance-based descriptors of complex networks. Wiener type indices are known to depend both on the network's number of nodes and topology. The Wiener polarity index is also related to the cluster coefficient of networks. In this paper, based on some graph transformations, we determine the sharp upper bound of the Wiener polarity index among all bicyclic networks. These bounds help to understand the underlying quantitative graph measures in depth. PMID:26750820
Adaptive filter for reconstruction of stereo audio signals
NASA Astrophysics Data System (ADS)
Cisowski, Krzysztof
2004-05-01
The paper presents a new approach to reconstruction of impulsively disturbed stereo audio signals. The problems of restoration of large blocks of missing samples are outlined. Present methods of removing of covariance defect are discussed. Model of stereophonic signal is defined and Kalman filter appropriate for this model is introduced. Modifications of the filter directing to the new method of reconstruction of block of missing samples are discussed. Projection based algorithm allows to recover samples of left (or right) stereo channel using additional information included in undistorted samples from the other channel.
A New Adaptive Image Denoising Method Based on Neighboring Coefficients
NASA Astrophysics Data System (ADS)
Biswas, Mantosh; Om, Hari
2016-03-01
Many good techniques have been discussed for image denoising that include NeighShrink, improved adaptive wavelet denoising method based on neighboring coefficients (IAWDMBNC), improved wavelet shrinkage technique for image denoising (IWST), local adaptive wiener filter (LAWF), wavelet packet thresholding using median and wiener filters (WPTMWF), adaptive image denoising method based on thresholding (AIDMT). These techniques are based on local statistical description of the neighboring coefficients in a window. These methods however do not give good quality of the images since they cannot modify and remove too many small wavelet coefficients simultaneously due to the threshold. In this paper, a new image denoising method is proposed that shrinks the noisy coefficients using an adaptive threshold. Our method overcomes these drawbacks and it has better performance than the NeighShrink, IAWDMBNC, IWST, LAWF, WPTMWF, and AIDMT denoising methods.
Adaptive box filters for removal of random noise from digital images
NASA Technical Reports Server (NTRS)
Eliason, Eric M.; Mcewen, Alfred S.
1990-01-01
Adaptive box-filtering algorithms to remove random bit errors and to smooth noisy data have been developed. For both procedures, the standard deviation of those pixels within a local box surrounding each pixel is used. A series of two or three filters with decreasing box sizes can be run to clean up extremely noisy images and to remove bit errors near sharp edges. The second filter, for noise smoothing, is similar to the 'sigma filter' of Lee (1983). The technique effectively reduces speckle in radar images without eliminating fine details.
A model for radar images and its application to adaptive digital filtering of multiplicative noise.
Frost, V S; Stiles, J A; Shanmugan, K S; Holtzman, J C
1982-02-01
Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.
Real-time 3D adaptive filtering for portable imaging systems
NASA Astrophysics Data System (ADS)
Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark
2015-03-01
Portable imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often not able to run with sufficient performance on a portable platform. In recent years, advanced multicore DSPs have been introduced that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms like 3D adaptive filtering, improving the image quality of portable medical imaging devices. In this study, the performance of a 3D adaptive filtering algorithm on a digital signal processor (DSP) is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec.
Low-Complexity Lossless Compression of Hyperspectral Imagery via Adaptive Filtering
NASA Technical Reports Server (NTRS)
Klimesh, M.
2005-01-01
A low-complexity, adaptive predictive technique for lossless compression of hyperspectral data is presented. The technique relies on the sign algorithm from the repertoire of adaptive filtering. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.
Learning Motivation and Adaptive Video Caption Filtering for EFL Learners Using Handheld Devices
ERIC Educational Resources Information Center
Hsu, Ching-Kun
2015-01-01
The aim of this study was to provide adaptive assistance to improve the listening comprehension of eleventh grade students. This study developed a video-based language learning system for handheld devices, using three levels of caption filtering adapted to student needs. Elementary level captioning excluded 220 English sight words (see Section 1…
Study on GPS attitude determination system aided INS using adaptive Kalman filter
NASA Astrophysics Data System (ADS)
Bian, Hongwei; Jin, Zhihua; Tian, Weifeng
2005-10-01
A marine INS/GPS (inertial navigation system/global positioning system) adaptive navigation system is presented in this paper. The GPS with two antennae providing vessel attitude is selected as the auxiliary system to fuse with INS. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The conventional Kalman filter (CKF) assumes that the statistics of the noise of each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However, the GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce a fuzzy logic control method into innovation-based adaptive estimation Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However, how to design the fuzzy logic controller is a very complicated problem, which is still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested in the developed INS/GPS integrated marine navigation system. Real field test results show that the adaptive Kalman filter outperforms the CKF with higher accuracy and robustness. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise existing in the Kalman filter.
An Adaptive Kalman Filter Using a Simple Residual Tuning Method
NASA Technical Reports Server (NTRS)
Harman, Richard R.
1999-01-01
One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. A. H. Jazwinski developed a specialized version of this technique for estimation of process noise. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.
An Adaptive Kalman Filter using a Simple Residual Tuning Method
NASA Technical Reports Server (NTRS)
Harman, Richard R.
1999-01-01
One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.
New Approach for IIR Adaptive Lattice Filter Structure Using Simultaneous Perturbation Algorithm
NASA Astrophysics Data System (ADS)
Martinez, Jorge Ivan Medina; Nakano, Kazushi; Higuchi, Kohji
Adaptive infinite impulse response (IIR), or recursive, filters are less attractive mainly because of the stability and the difficulties associated with their adaptive algorithms. Therefore, in this paper the adaptive IIR lattice filters are studied in order to devise algorithms that preserve the stability of the corresponding direct-form schemes. We analyze the local properties of stationary points, a transformation achieving this goal is suggested, which gives algorithms that can be efficiently implemented. Application to the Steiglitz-McBride (SM) and Simple Hyperstable Adaptive Recursive Filter (SHARF) algorithms is presented. Also a modified version of Simultaneous Perturbation Stochastic Approximation (SPSA) is presented in order to get the coefficients in a lattice form more efficiently and with a lower computational cost and complexity. The results are compared with previous lattice versions of these algorithms. These previous lattice versions may fail to preserve the stability of stationary points.
Prototype adaptive bow-tie filter based on spatial exposure time modulation
NASA Astrophysics Data System (ADS)
Badal, Andreu
2016-03-01
In recent years, there has been an increased interest in the development of dynamic bow-tie filters that are able to provide patient-specific x-ray beam shaping. We introduce the first physical prototype of a new adaptive bow-tie filter design based on the concept of "spatial exposure time modulation." While most existing bow-tie filters operate by attenuating the radiation beam differently in different locations using partially attenuating objects, the presented filter shapes the radiation field using two movable completely radio-opaque collimators. The aperture and speed of the collimators is modulated in synchrony with the x-ray exposure to selectively block the radiation emitted to different parts of the object. This mode of operation does not allow the reproduction of every possible attenuation profile, but it can reproduce the profile of any object with an attenuation profile monotonically decreasing from the center to the periphery, such as an object with an elliptical cross section. Therefore, the new adaptive filter provides the same advantages as the currently existing static bow-tie filters, which are typically designed to work for a pre-determined cylindrical object at a fixed distance from the source, and provides the additional capability to adapt its performance at image acquisition time to better compensate for the actual diameter and location of the imaged object. A detailed description of the prototype filter, the implemented control methods, and a preliminary experimental validation of its performance are presented.
An adaptive filter bank for motor imagery based Brain Computer Interface.
Thomas, Kavitha P; Guan, Cuntai; Tong, Lau Chiew; Prasad, Vinod A
2008-01-01
Brain Computer Interface (BCI) provides an alternative communication and control method for people with severe motor disabilities. Motor imagery patterns are widely used in Electroencephalogram (EEG) based BCIs. These motor imagery activities are associated with variation in alpha and beta band power of EEG signals called Event Related Desynchronization/synchronization (ERD/ERS). The dominant frequency bands are subject-specific and therefore performance of motor imagery based BCIs are sensitive to both temporal filtering and spatial filtering. As the optimum filter is strongly subject-dependent, we propose a method that selects the subject-specific discriminative frequency components using time-frequency plots of Fisher ratio of two-class motor imagery patterns. We also propose a low complexity adaptive Finite Impulse Response (FIR) filter bank system based on coefficient decimation technique which can realize the subject-specific bandpass filters adaptively depending on the information of Fisher ratio map. Features are extracted only from the selected frequency components. The proposed adaptive filter bank based system offers average classification accuracy of about 90%, which is slightly better than the existing fixed filter bank system. PMID:19162856
Stent enhancement in digital x-ray fluoroscopy using an adaptive feature enhancement filter
NASA Astrophysics Data System (ADS)
Jiang, Yuhao; Zachary, Josey
2016-03-01
Fluoroscopic images belong to the classes of low contrast and high noise. Simply lowering radiation dose will render the images unreadable. Feature enhancement filters can reduce patient dose by acquiring images at low dose settings and then digitally restoring them to the original quality. In this study, a stent contrast enhancement filter is developed to selectively improve the contrast of stent contour without dramatically boosting the image noise including quantum noise and clinical background noise. Gabor directional filter banks are implemented to detect the edges and orientations of the stent. A high orientation resolution of 9° is used. To optimize the use of the information obtained from Gabor filters, a computerized Monte Carlo simulation followed by ROC study is used to find the best nonlinear operator. The next stage of filtering process is to extract symmetrical parts in the stent. The global and local symmetry measures are used. The information gathered from previous two filter stages are used to generate a stent contour map. The contour map is then scaled and added back to the original image to get a contrast enhanced stent image. We also apply a spatio-temporal channelized Hotelling observer model and other numerical measures to characterize the response of the filters and contour map to optimize the selections of parameters for image quality. The results are compared to those filtered by an adaptive unsharp masking filter previously developed. It is shown that stent enhancement filter can effectively improve the stent detection and differentiation in the interventional fluoroscopy.
Independent motion detection with a rival penalized adaptive particle filter
NASA Astrophysics Data System (ADS)
Becker, Stefan; Hübner, Wolfgang; Arens, Michael
2014-10-01
Aggregation of pixel based motion detection into regions of interest, which include views of single moving objects in a scene is an essential pre-processing step in many vision systems. Motion events of this type provide significant information about the object type or build the basis for action recognition. Further, motion is an essential saliency measure, which is able to effectively support high level image analysis. When applied to static cameras, background subtraction methods achieve good results. On the other hand, motion aggregation on freely moving cameras is still a widely unsolved problem. The image flow, measured on a freely moving camera is the result from two major motion types. First the ego-motion of the camera and second object motion, that is independent from the camera motion. When capturing a scene with a camera these two motion types are adverse blended together. In this paper, we propose an approach to detect multiple moving objects from a mobile monocular camera system in an outdoor environment. The overall processing pipeline consists of a fast ego-motion compensation algorithm in the preprocessing stage. Real-time performance is achieved by using a sparse optical flow algorithm as an initial processing stage and a densely applied probabilistic filter in the post-processing stage. Thereby, we follow the idea proposed by Jung and Sukhatme. Normalized intensity differences originating from a sequence of ego-motion compensated difference images represent the probability of moving objects. Noise and registration artefacts are filtered out, using a Bayesian formulation. The resulting a posteriori distribution is located on image regions, showing strong amplitudes in the difference image which are in accordance with the motion prediction. In order to effectively estimate the a posteriori distribution, a particle filter is used. In addition to the fast ego-motion compensation, the main contribution of this paper is the design of the probabilistic
Adaptive filters for suppressing irregular hostile jamming in direct sequence spread-spectrum system
NASA Astrophysics Data System (ADS)
Lee, Jung Hoon; Lee, Choong Woong
A stable and high-performance adaptive filter for suppressing irregular hostile jamming in direct-sequence (DS) spread-spectrum systems is designed. A gradient-search fast converging algorithm (GFC) is suggested. For the case of a sudden parameter jump or incoming of an interference, the transient behaviors of the receiver using a GFC adaptive filter are investigated and compared with those of the receiver using a least-mean-square (LMS) or a lattice adaptive filter. The results are shown in the response graphs of the simulated receiver during the short period when the characteristic of a jammer is suddenly changed. Steady-state performances of those receivers are also evaluated in the sense of the excess mean-square error over that of an optimum receiver for suppressing stationary interferences.
NASA Astrophysics Data System (ADS)
Li, Wei; Haese-Coat, Veronique; Ronsin, Joseph
1996-03-01
An adaptive GA scheme is adopted for the optimal morphological filter design problem. The adaptive crossover and mutation rate which make the GA avoid premature and at the same time assure convergence of the program are successfully used in optimal morphological filter design procedure. In the string coding step, each string (chromosome) is composed of a structuring element coding chain concatenated with a filter sequence coding chain. In decoding step, each string is divided into 3 chains which then are decoded respectively into one structuring element with a size inferior to 5 by 5 and two concatenating morphological filter operators. The fitness function in GA is based on the mean-square-error (MSE) criterion. In string selection step, a stochastic tournament procedure is used to replace the simple roulette wheel program in order to accelerate the convergence. The final convergence of our algorithm is reached by a two step converging strategy. In presented applications of noise removal from texture images, it is found that with the optimized morphological filter sequences, the obtained MSE values are smaller than those using corresponding non-adaptive morphological filters, and the optimized shapes and orientations of structuring elements take approximately the same shapes and orientations as those of the image textons.
Sudeep, P V; Issac Niwas, S; Palanisamy, P; Rajan, Jeny; Xiaojun, Yu; Wang, Xianghong; Luo, Yuemei; Liu, Linbo
2016-04-01
Optical coherence tomography (OCT) has continually evolved and expanded as one of the most valuable routine tests in ophthalmology. However, noise (speckle) in the acquired images causes quality degradation of OCT images and makes it difficult to analyze the acquired images. In this paper, an iterative approach based on bilateral filtering is proposed for speckle reduction in multiframe OCT data. Gamma noise model is assumed for the observed OCT image. First, the adaptive version of the conventional bilateral filter is applied to enhance the multiframe OCT data and then the bias due to noise is reduced from each of the filtered frames. These unbiased filtered frames are then refined using an iterative approach. Finally, these refined frames are averaged to produce the denoised OCT image. Experimental results on phantom images and real OCT retinal images demonstrate the effectiveness of the proposed filter. PMID:26907572
An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics.
Xin, Yao; Li, Will X Y; Zhang, Zhaorui; Cheung, Ray C C; Song, Dong; Berger, Theodore W
2015-01-01
Neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding. Application specific integrated circuits (ASICs) do not have flexibility to switch between different adaptive filters while the cost for design and fabrication is formidable. In this research work, we explore an application specific instruction set processor (ASIP) for adaptive filters in neural decoding activity. The proposed architecture focuses on efficient computation for the most time-consuming matrix/vector operations among commonly used adaptive filters, being able to provide both flexibility and throughput. Evaluation and implementation results are provided to demonstrate that the proposed ASIP design is area-efficient while being competitive to commercial CPUs in computational performance.
An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics.
Xin, Yao; Li, Will X Y; Zhang, Zhaorui; Cheung, Ray C C; Song, Dong; Berger, Theodore W
2015-01-01
Neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding. Application specific integrated circuits (ASICs) do not have flexibility to switch between different adaptive filters while the cost for design and fabrication is formidable. In this research work, we explore an application specific instruction set processor (ASIP) for adaptive filters in neural decoding activity. The proposed architecture focuses on efficient computation for the most time-consuming matrix/vector operations among commonly used adaptive filters, being able to provide both flexibility and throughput. Evaluation and implementation results are provided to demonstrate that the proposed ASIP design is area-efficient while being competitive to commercial CPUs in computational performance. PMID:26451817
Method and system for training dynamic nonlinear adaptive filters which have embedded memory
NASA Technical Reports Server (NTRS)
Rabinowitz, Matthew (Inventor)
2002-01-01
Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6.
Stent enhancement using a locally adaptive unsharp masking filter in digital x-ray fluoroscopy
NASA Astrophysics Data System (ADS)
Jiang, Yuhao; Ekanayake, Eranda
2014-03-01
Low exposure X-ray fluoroscopy is used to guide some complicate interventional procedures. Due to the inherent high levels of noise, improving the visibility of some interventional devices such as stent will greatly benefit those interventional procedures. Stent, which is made up of tiny steel wires, is also suffered from contrast dilutions of large flat panel detector pixels. A novel adaptive unsharp masking filter has been developed to improve stent contrast in real-time applications. In unsharp masking processing, the background is estimated and subtracted from the original input image to create a foreground image containing objects of interest. A background estimator is therefore critical in the unsharp masking processing. In this specific study, orientation filter kernels are used as the background estimator. To make the process simple and fast, the kernels average along a line of pixels. A high orientation resolution of 18° is used. A nonlinear operator is then used to combine the information from the images generated from convolving the original background and noise only images with orientation filters. A computerized Monte Carlo simulation followed by ROC study is used to identify the best nonlinear operator. We then apply the unsharp masking filter to the images with stents present. It is shown that the locally adaptive unsharp making filter is an effective filter for improving stent visibility in the interventional fluoroscopy. We also apply a spatio-temporal channelized human observer model to quantitatively optimize and evaluate the filter.
Adaptive identification and control of structural dynamics systems using recursive lattice filters
NASA Technical Reports Server (NTRS)
Sundararajan, N.; Montgomery, R. C.; Williams, J. P.
1985-01-01
A new approach for adaptive identification and control of structural dynamic systems by using least squares lattice filters thar are widely used in the signal processing area is presented. Testing procedures for interfacing the lattice filter identification methods and modal control method for stable closed loop adaptive control are presented. The methods are illustrated for a free-free beam and for a complex flexible grid, with the basic control objective being vibration suppression. The approach is validated by using both simulations and experimental facilities available at the Langley Research Center.
NASA Astrophysics Data System (ADS)
Öhberg, Fredrik; Lundström, Ronnie; Grip, Helena
2013-08-01
For all segments and tests, a modified Kalman filter and a quasi-static sensor fusion algorithm were equally accurate (precision and accuracy ˜2-3°) compared to normalized least mean squares filtering, recursive least-squares filtering and standard Kalman filtering. The aims were to: (1) compare adaptive filtering techniques used for sensor fusion and (2) evaluate the precision and accuracy for a chosen adaptive filter. Motion sensors (based on inertial measurement units) are limited by accumulative integration errors arising from sensor bias. This drift can partly be handled with adaptive filtering techniques. To advance the measurement technique in this area, a new modified Kalman filter is developed. Differences in accuracy were observed during different tests especially drift in the internal/external rotation angle. This drift can be minimized if the sensors include magnetometers.
NASA Astrophysics Data System (ADS)
Abramovich, Iu. I.; Arov, D. Z.; Kachur, V. G.
1987-12-01
The paper considers the problem of finding the vector of an adaptive filter of stationary-noise compensation which corresponds to a Toeplitz correlation-matrix structure. The existence of a Toeplitz solution is demonstrated. Lower-bound estimates are obtained for the gain in noise-compensation efficiency using a priori information about the Toeplitz matrix structure. Constructive methods for obtaining adaptive solutions corresponding to these estimates are indicated.
Adaptive error covariances estimation methods for ensemble Kalman filters
Zhen, Yicun; Harlim, John
2015-08-01
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.
NASA Technical Reports Server (NTRS)
Balas, Mark; Frost, Susan
2012-01-01
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter.
NASA Astrophysics Data System (ADS)
Boz, Utku; Basdogan, Ipek
2015-12-01
Structural vibrations is a major cause for noise problems, discomfort and mechanical failures in aerospace, automotive and marine systems, which are mainly composed of plate-like structures. In order to reduce structural vibrations on these structures, active vibration control (AVC) is an effective approach. Adaptive filtering methodologies are preferred in AVC due to their ability to adjust themselves for varying dynamics of the structure during the operation. The filtered-X LMS (FXLMS) algorithm is a simple adaptive filtering algorithm widely implemented in active control applications. Proper implementation of FXLMS requires availability of a reference signal to mimic the disturbance and model of the dynamics between the control actuator and the error sensor, namely the secondary path. However, the controller output could interfere with the reference signal and the secondary path dynamics may change during the operation. This interference problem can be resolved by using an infinite impulse response (IIR) filter which considers feedback of the one or more previous control signals to the controller output and the changing secondary path dynamics can be updated using an online modeling technique. In this paper, IIR filtering based filtered-U LMS (FULMS) controller is combined with online secondary path modeling algorithm to suppress the vibrations of a plate-like structure. The results are validated through numerical and experimental studies. The results show that the FULMS with online secondary path modeling approach has more vibration rejection capabilities with higher convergence rate than the FXLMS counterpart.
Maier, Andreas; Wigstroem, Lars; Hofmann, Hannes G.; Hornegger, Joachim; Zhu Lei; Strobel, Norbert; Fahrig, Rebecca
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-ray 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
NASA Astrophysics Data System (ADS)
Hayes, Charles E.; McClellan, James H.; Scott, Waymond R.; Kerr, Andrew J.
2016-05-01
This work introduces two advances in wide-band electromagnetic induction (EMI) processing: a novel adaptive matched filter (AMF) and matched subspace detection methods. Both advances make use of recent work with a subspace SVD approach to separating the signal, soil, and noise subspaces of the frequency measurements The proposed AMF provides a direct approach to removing the EMI self-response while improving the signal to noise ratio of the data. Unlike previous EMI adaptive downtrack filters, this new filter will not erroneously optimize the EMI soil response instead of the EMI target response because these two responses are projected into separate frequency subspaces. The EMI detection methods in this work elaborate on how the signal and noise subspaces in the frequency measurements are ideal for creating the matched subspace detection (MSD) and constant false alarm rate matched subspace detection (CFAR) metrics developed by Scharf The CFAR detection metric has been shown to be the uniformly most powerful invariant detector.
Spatial adaptive upsampling filter for HDR image based on multiple luminance range
NASA Astrophysics Data System (ADS)
Chen, Qian; Su, Guan-ming; Peng, Yin
2014-03-01
In this paper, we propose an adaptive upsampling filter to spatially upscale HDR image based on luminance range of the HDR picture in each color channel. It first searches for the optimal luminance range values to partition an HDR image to three different parts: dark, mid-tone and highlight. Then we derive the optimal set of filter coefficients both vertically and horizontally for each part. When the HDR pixel is within the dark area, we apply one set of filter coefficients to vertically upsample the pixel. If the HDR pixel falls in mid-tone area, we apply another set of filter for vertical upsampling. Otherwise the HDR pixel is in highlight area, another set of filter will be applied for vertical upsampling. Horizontal upsampling will be carried out likewise based on its luminance. The inherent idea to partition HDR image to different luminance areas is based on the fact that most HDR images are created from multiple exposures. Different exposures usually demonstrate slight variation in captured signal statistics, such as noise level, subtle misalignment etc. Hence, to group different regions to three luminance partitions actually helps to eliminate the variation between signals, and to derive optimal filter for each group with signals of lesser variation is certainly more efficient than for the entire HDR image. Experimental results show that the proposed adaptive upsampling filter based on luminance ranges outperforms the optimal upsampling filter around 0.57dB for R channel, 0.44dB for G channel and 0.31dB for B channel.
A unified set-based test with adaptive filtering for gene-environment interaction analyses.
Liu, Qianying; Chen, Lin S; Nicolae, Dan L; Pierce, Brandon L
2016-06-01
In genome-wide gene-environment interaction (GxE) studies, a common strategy to improve power is to first conduct a filtering test and retain only the SNPs that pass the filtering in the subsequent GxE analyses. Inspired by two-stage tests and gene-based tests in GxE analysis, we consider the general problem of jointly testing a set of parameters when only a few are truly from the alternative hypothesis and when filtering information is available. We propose a unified set-based test that simultaneously considers filtering on individual parameters and testing on the set. We derive the exact distribution and approximate the power function of the proposed unified statistic in simplified settings, and use them to adaptively calculate the optimal filtering threshold for each set. In the context of gene-based GxE analysis, we show that although the empirical power function may be affected by many factors, the optimal filtering threshold corresponding to the peak of the power curve primarily depends on the size of the gene. We further propose a resampling algorithm to calculate P-values for each gene given the estimated optimal filtering threshold. The performance of the method is evaluated in simulation studies and illustrated via a genome-wide gene-gender interaction analysis using pancreatic cancer genome-wide association data. PMID:26496228
A unified set-based test with adaptive filtering for gene-environment interaction analyses
Liu, Qianying; Chen, Lin S.; Nicolae, Dan L.; Pierce, Brandon L.
2015-01-01
Summary In genome-wide gene-environment interaction (GxE) studies, a common strategy to improve power is to first conduct a filtering test and retain only the SNPs that pass the filtering in the subsequent GxE analyses. Inspired by two-stage tests and gene-based tests in GxE analysis, we consider the general problem of jointly testing a set of parameters when only a few are truly from the alternative hypothesis and when filtering information is available. We propose a unified set-based test that simultaneously considers filtering on individual parameters and testing on the set. We derive the exact distribution and approximate the power function of the proposed unified statistic in simplified settings, and use them to adaptively calculate the optimal filtering threshold for each set. In the context of gene-based GxE analysis, we show that although the empirical power function may be affected by many factors, the optimal filtering threshold corresponding to the peak of the power curve primarily depends on the size of the gene. We further propose a resampling algorithm to calculate p-values for each gene given the estimated optimal filtering threshold. The performance of the method is evaluated in simulation studies and illustrated via a genome-wide gene-gender interaction analysis using pancreatic cancer genome-wide association data. PMID:26496228
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm. PMID:26920086
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm.
A Self-Adaptive Missile Guidance System for Statistical Inputs
NASA Technical Reports Server (NTRS)
Peery, H. Rodney
1960-01-01
A method of designing a self-adaptive missile guidance system is presented. The system inputs are assumed to be known in a statistical sense only. Newton's modified Wiener theory is utilized in the design of the system and to establish the performance criterion. The missile is assumed to be a beam rider, to have a g limiter, and to operate over a flight envelope where the open-loop gain varies by a factor of 20. It is shown that the percent of time that missile acceleration limiting occurs can be used effectively to adjust the coefficients of the Wiener filter. The result is a guidance system which adapts itself to a changing environment and gives essentially optimum filtering and minimum miss distance.
Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators
NASA Astrophysics Data System (ADS)
Law, K. J. H.; Sanz-Alonso, D.; Shukla, A.; Stuart, A. M.
2016-06-01
In the context of filtering chaotic dynamical systems it is well-known that partial observations, if sufficiently informative, can be used to control the inherent uncertainty due to chaos. The purpose of this paper is to investigate, both theoretically and numerically, conditions on the observations of chaotic systems under which they can be accurately filtered. In particular, we highlight the advantage of adaptive observation operators over fixed ones. The Lorenz '96 model is used to exemplify our findings. We consider discrete-time and continuous-time observations in our theoretical developments. We prove that, for fixed observation operator, the 3DVAR filter can recover the system state within a neighbourhood determined by the size of the observational noise. It is required that a sufficiently large proportion of the state vector is observed, and an explicit form for such sufficient fixed observation operator is given. Numerical experiments, where the data is incorporated by use of the 3DVAR and extended Kalman filters, suggest that less informative fixed operators than given by our theory can still lead to accurate signal reconstruction. Adaptive observation operators are then studied numerically; we show that, for carefully chosen adaptive observation operators, the proportion of the state vector that needs to be observed is drastically smaller than with a fixed observation operator. Indeed, we show that the number of state coordinates that need to be observed may even be significantly smaller than the total number of positive Lyapunov exponents of the underlying system.
ADAPTIVE LAPLACIAN FILTERING FOR SENSORIMOTOR RHYTHM-BASED BRAIN-COMPUTER INTERFACES
Lu, Jun; McFarland, Dennis J.; Wolpaw, Jonathan R.
2013-01-01
Objective Sensorimotor rhythms (SMRs) are 8–30 Hz oscillations in the EEG recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two, or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-ratio of EEG. Here we test the hypothesis that a new filter design, called an “adaptive Laplacian (ALAP) filter,” can provide better performance for SMR-based BCIs. Approach An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights, and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing leave-one-out cross-validation error through a gradient descent method, and is computationally feasible. Main results Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filter. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy as well as in mean squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. Significance Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs. PMID:23220879
Understanding spike-triggered covariance using Wiener theory for receptive field identification
Sandler, Roman A.; Marmarelis, Vasilis Z.
2015-01-01
Receptive field identification is a vital problem in sensory neurophysiology and vision. Much research has been done in identifying the receptive fields of nonlinear neurons whose firing rate is determined by the nonlinear interactions of a small number of linear filters. Despite more advanced methods that have been proposed, spike-triggered covariance (STC) continues to be the most widely used method in such situations due to its simplicity and intuitiveness. Although the connection between STC and Wiener/Volterra kernels has often been mentioned in the literature, this relationship has never been explicitly derived. Here we derive this relationship and show that the STC matrix is actually a modified version of the second-order Wiener kernel, which incorporates the input autocorrelation and mixes first- and second-order dynamics. It is then shown how, with little modification of the STC method, the Wiener kernels may be obtained and, from them, the principal dynamic modes, a set of compact and efficient linear filters that essentially combine the spike-triggered average and STC matrix and generalize to systems with both continuous and point-process outputs. Finally, using Wiener theory, we show how these obtained filters may be corrected when they were estimated using correlated inputs. Our correction technique is shown to be superior to those commonly used in the literature for both correlated Gaussian images and natural images. PMID:26230978
NASA Technical Reports Server (NTRS)
Keel, Byron M.
1989-01-01
An optimum adaptive clutter rejection filter for use with airborne Doppler weather radar is presented. The radar system is being designed to operate at low-altitudes for the detection of windshear in an airport terminal area where ground clutter returns may mask the weather return. The coefficients of the adaptive clutter rejection filter are obtained using a complex form of a square root normalized recursive least squares lattice estimation algorithm which models the clutter return data as an autoregressive process. The normalized lattice structure implementation of the adaptive modeling process for determining the filter coefficients assures that the resulting coefficients will yield a stable filter and offers possible fixed point implementation. A 10th order FIR clutter rejection filter indexed by geographical location is designed through autoregressive modeling of simulated clutter data. Filtered data, containing simulated dry microburst and clutter return, are analyzed using pulse-pair estimation techniques. To measure the ability of the clutter rejection filters to remove the clutter, results are compared to pulse-pair estimates of windspeed within a simulated dry microburst without clutter. In the filter evaluation process, post-filtered pulse-pair width estimates and power levels are also used to measure the effectiveness of the filters. The results support the use of an adaptive clutter rejection filter for reducing the clutter induced bias in pulse-pair estimates of windspeed.
An adaptive filter for studying the life cycle of optical rogue waves.
Liu, Chu; Rees, Eric J; Laurila, Toni; Jian, Shuisheng; Kaminski, Clemens F
2010-12-01
We present an adaptive numerical filter for analyzing fiber-length dependent properties of optical rogue waves, which are highly intense and extremely red-shifted solitons that arise during supercontinuum generation in photonic crystal fiber. We use this filter to study a data set of 1000 simulated supercontinuum pulses, produced from 5 ps pump pulses containing random noise. Optical rogue waves arise in different supercontinuum pulses at various positions along the fiber, and exhibit a lifecycle: their intensity peaks over a finite range of fiber length before declining slowly.
Adaptive nonlocal means filtering based on local noise level for CT denoising
Li, Zhoubo; Trzasko, Joshua D.; Lake, David S.; Blezek, Daniel J.; Manduca, Armando; Yu, Lifeng; Fletcher, Joel G.; McCollough, Cynthia H.
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 analytical 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
Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces
NASA Astrophysics Data System (ADS)
Lu, Jun; McFarland, Dennis J.; Wolpaw, Jonathan R.
2013-02-01
Objective. Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an ‘adaptive Laplacian (ALAP) filter’, can provide better performance for SMR-based BCIs. Approach. An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible. Main results. Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. Significance. Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.
Tone reproduction for high-dynamic range imaging based on adaptive filtering
NASA Astrophysics Data System (ADS)
Ha, Changwoo; Lee, Joohyun; Jeong, Jechang
2014-03-01
A tone reproduction algorithm with enhanced contrast of high-dynamic range images on conventional low-dynamic range display devices is presented. The proposed algorithm consists mainly of block-based parameter estimation, a characteristic-based luminance adjustment, and an adaptive Gaussian filter using minimum description length. Instead of relying only on the reduction of the dynamic range, a characteristic-based luminance adjustment process modifies the luminance values. The Gaussian-filtered luminance value is obtained from appropriate value of variance, and the contrast is then enhanced through the use of a relation between the adjusted luminance and Gaussian-filtered luminance values. In the final tone-reproduction process, the proposed algorithm combines color and luminance components in order to preserve the color consistency. The experimental results demonstrate that the proposed algorithm achieves a good subjective quality while enhancing the contrast of the image details.
Ensembles of adaptive spatial filters increase BCI performance: an online evaluation
NASA Astrophysics Data System (ADS)
Sannelli, Claudia; Vidaurre, Carmen; Müller, Klaus-Robert; Blankertz, Benjamin
2016-08-01
Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain-computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI
Ensembles of adaptive spatial filters increase BCI performance: an online evaluation
NASA Astrophysics Data System (ADS)
Sannelli, Claudia; Vidaurre, Carmen; Müller, Klaus-Robert; Blankertz, Benjamin
2016-08-01
Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain–computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI
Multicomponent AM-FM demodulation based on energy separation and adaptive filtering
NASA Astrophysics Data System (ADS)
Qin, Yi
2013-07-01
Multicomponent AM-FM demodulation is an important tool in many engineering applications. To improve the demodulation accuracy of the commonly used methods, such as iterative Hilbert transform (IHT) and Hilbert-Huang transform (HHT), a new multicomponent AM-FM demodulation method is proposed in this paper. The proposed method achieves multicomponent demodulation by using an iteratively energy separation algorithm and adaptive low-pass filtering. Using the frequency spectra of instantaneous amplitude and frequency obtained by the energy separation algorithm at each level, the used filters are adaptively designed. In addition, this proposed method uses symmetric extension to solve the boundary effect in the estimation of instantaneous amplitudes and frequencies. The demodulation process is automatic for an arbitrary signal. Simulation and application results show that the proposed method has high demodulation accuracy than IHT, HHT and other typical methods, and it can be effectively applied to extracting weak fault feature from mechanical vibration signals.
NASA Technical Reports Server (NTRS)
Penland, Cecile; Ghil, Michael; Weickmann, Klaus M.
1991-01-01
The spectral resolution and statistical significance of a harmonic analysis obtained by low-order MEM can be improved by subjecting the data to an adaptive filter. This adaptive filter consists of projecting the data onto the leading temporal empirical orthogonal functions obtained from singular spectrum analysis (SSA). The combined SSA-MEM method is applied both to a synthetic time series and a time series of AAM data. The procedure is very effective when the background noise is white and less so when the background noise is red. The latter case obtains in the AAM data. Nevertheless, reliable evidence for intraseasonal and interannual oscillations in AAM is detected. The interannual periods include a quasi-biennial one and an LF one, of 5 years, both related to the El Nino/Southern Oscillation. In the intraseasonal band, separate oscillations of about 48.5 and 51 days are ascertained.
Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum
Wilson, Emma D.; Assaf, Tareq; Pearson, Martin J.; Rossiter, Jonathan M.; Dean, Paul; Anderson, Sean R.; Porrill, John
2015-01-01
The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks. PMID:26257638
Adaptive filter design based on the LMS algorithm for delay elimination in TCR/FC compensators.
Hooshmand, Rahmat Allah; Torabian Esfahani, Mahdi
2011-04-01
Thyristor controlled reactor with fixed capacitor (TCR/FC) compensators have the capability of compensating reactive power and improving power quality phenomena. Delay in the response of such compensators degrades their performance. In this paper, a new method based on adaptive filters (AF) is proposed in order to eliminate delay and increase the response of the TCR compensator. The algorithm designed for the adaptive filters is performed based on the least mean square (LMS) algorithm. In this design, instead of fixed capacitors, band-pass LC filters are used. To evaluate the filter, a TCR/FC compensator was used for nonlinear and time varying loads of electric arc furnaces (EAFs). These loads caused occurrence of power quality phenomena in the supplying system, such as voltage fluctuation and flicker, odd and even harmonics and unbalancing in voltage and current. The above design was implemented in a realistic system model of a steel complex. The simulation results show that applying the proposed control in the TCR/FC compensator efficiently eliminated delay in the response and improved the performance of the compensator in the power system.
Design of adaptive control systems by means of self-adjusting transversal filters
NASA Technical Reports Server (NTRS)
Merhav, S. J.
1986-01-01
The design of closed-loop adaptive control systems based on nonparametric identification was addressed. Implementation is by self-adjusting Least Mean Square (LMS) transversal filters. The design concept is Model Reference Adaptive Control (MRAC). Major issues are to preserve the linearity of the error equations of each LMS filter, and to prevent estimation bias that is due to process or measurement noise, thus providing necessary conditions for the convergence and stability of the control system. The controlled element is assumed to be asymptotically stable and minimum phase. Because of the nonparametric Finite Impulse Response (FIR) estimates provided by the LMS filters, a-priori information on the plant model is needed only in broad terms. Following a survey of control system configurations and filter design considerations, system implementation is shown here in Single Input Single Output (SISO) format which is readily extendable to multivariable forms. In extensive computer simulation studies the controlled element is represented by a second-order system with widely varying damping, natural frequency, and relative degree.
Adaptive filter design based on the LMS algorithm for delay elimination in TCR/FC compensators.
Hooshmand, Rahmat Allah; Torabian Esfahani, Mahdi
2011-04-01
Thyristor controlled reactor with fixed capacitor (TCR/FC) compensators have the capability of compensating reactive power and improving power quality phenomena. Delay in the response of such compensators degrades their performance. In this paper, a new method based on adaptive filters (AF) is proposed in order to eliminate delay and increase the response of the TCR compensator. The algorithm designed for the adaptive filters is performed based on the least mean square (LMS) algorithm. In this design, instead of fixed capacitors, band-pass LC filters are used. To evaluate the filter, a TCR/FC compensator was used for nonlinear and time varying loads of electric arc furnaces (EAFs). These loads caused occurrence of power quality phenomena in the supplying system, such as voltage fluctuation and flicker, odd and even harmonics and unbalancing in voltage and current. The above design was implemented in a realistic system model of a steel complex. The simulation results show that applying the proposed control in the TCR/FC compensator efficiently eliminated delay in the response and improved the performance of the compensator in the power system. PMID:21193194
Adaptive non-local means filtering based on local noise level for CT denoising
NASA Astrophysics Data System (ADS)
Li, Zhoubo; Yu, Lifeng; Trzasko, Joshua D.; Fletcher, Joel G.; McCollough, Cynthia H.; Manduca, Armando
2012-03-01
Radiation dose from CT scans is an increasing health concern in the practice of radiology. Higher dose scans can produce clearer images with high diagnostic quality, but may increase the potential risk of radiation-induced cancer or other side effects. Lowering radiation dose alone generally produces a noisier image and may degrade diagnostic performance. Recently, CT dose reduction based on non-local means (NLM) filtering for noise reduction has yielded promising results. However, traditional NLM denoising operates under the assumption that image noise is spatially uniform noise, while in CT images the noise level varies significantly within and across slices. Therefore, applying NLM filtering to CT data using a global filtering strength cannot achieve optimal denoising performance. In this work, we have developed a technique for efficiently estimating the local noise level for CT images, and have modified the NLM algorithm to adapt to local variations in noise level. The local noise level estimation technique matches the true noise distribution determined from multiple repetitive scans of a phantom object very well. The modified NLM algorithm provides more effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with the clinical workflow.
Filter for biomedical imaging and image processing
NASA Astrophysics Data System (ADS)
Mondal, Partha P.; Rajan, K.; Ahmad, Imteyaz
2006-07-01
Image filtering techniques have numerous potential applications in biomedical imaging and image processing. The design of filters largely depends on the a priori, knowledge about the type of noise corrupting the image. This makes the standard filters application specific. Widely used filters such as average, Gaussian, and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high-frequency details, making the image nonsmooth. An integrated general approach to design a finite impulse response filter based on Hebbian learning is proposed for optimal image filtering. This algorithm exploits the interpixel correlation by updating the filter coefficients using Hebbian learning. The algorithm is made iterative for achieving efficient learning from the neighborhood pixels. This algorithm performs optimal smoothing of the noisy image by preserving high-frequency as well as low-frequency features. Evaluation results show that the proposed finite impulse response filter is robust under various noise distributions such as Gaussian noise, salt-and-pepper noise, and speckle noise. Furthermore, the proposed approach does not require any a priori knowledge about the type of noise. The number of unknown parameters is few, and most of these parameters are adaptively obtained from the processed image. The proposed filter is successfully applied for image reconstruction in a positron emission tomography imaging modality. The images reconstructed by the proposed algorithm are found to be superior in quality compared with those reconstructed by existing PET image reconstruction methodologies.
NASA Astrophysics Data System (ADS)
Man, Jun; Li, Weixuan; Zeng, Lingzao; Wu, Laosheng
2016-06-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a sufficiently large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos expansion (PCE) to represent and propagate the uncertainties in parameters and states. However, PCKF suffers from the so-called "curse of dimensionality". Its computational cost increases drastically with the increasing number of parameters and system nonlinearity. Furthermore, PCKF may fail to provide accurate estimations due to the joint updating scheme for strongly nonlinear models. Motivated by recent developments in uncertainty quantification and EnKF, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected at each assimilation step; the "restart" scheme is utilized to eliminate the inconsistency between updated model parameters and states variables. The performance of RAPCKF is systematically tested with numerical cases of unsaturated flow models. It is shown that the adaptive approach and restart scheme can significantly improve the performance of PCKF. Moreover, RAPCKF has been demonstrated to be more efficient than EnKF with the same computational cost.
Subotić, Miško; Šarić, Zoran; Jovičić, Slobodan T
2012-03-01
Transient otoacoustic emission (TEOAE) is a method widely used in clinical practice for assessment of hearing quality. The main problem in TEOAE detection is its much lower level than the level of environmental and biological noise. While the environmental noise level can be controlled, the biological noise can be only reduced by appropriate signal processing. This paper presents a new two-probe preprocessing TEOAE system for suppression of the biological noise by adaptive filtering. The system records biological noises in both ears and applies a specific adaptive filtering approach for suppression of biological noise in the ear canal with TEOAE. The adaptive filtering approach includes robust sign error LMS algorithm, stimuli response summation according to the derived non-linear response (DNLR) technique, subtraction of the estimated TEOAE signal and residual noise suppression. The proposed TEOAE detection system is tested by three quality measures: signal-to-noise ratio (S/N), reproducibility of TEOAE, and measurement time. The maximal TEOAE detection improvement is dependent on the coherence function between biological noise in left and right ears. The experimental results show maximal improvement of 7 dB in S/N, improvement in reproducibility near 40% and reduction in duration of TEOAE measurement of over 30%.
Speckle reduction in optical coherence tomography images using digital filtering
Ozcan, Aydogan; Bilenca, Alberto; Desjardins, Adrien E.; Bouma, Brett E.; Tearney, Guillermo J.
2009-01-01
Speckle noise is a ubiquitous artifact that limits the interpretation of optical coherence tomography images. Here we apply various speckle-reduction digital filters to optical coherence tomography images and compare their performance. Our results indicate that shift-invariant, nonorthogonal wavelet-transform-based filters together with enhanced Lee and adaptive Wiener filters can significantly reduce speckle and increase the signal-to-noise ratio, while preserving strong edges. The speckle reduction capabilities of these filters are also compared with speckle reduction from incoherent angular compounding. Our results suggest that by using these digital filters, the number of individual angles required to attain a certain level of speckle reduction can be decreased. PMID:17728812
Adaptive approximation of higher order posterior statistics
Lee, Wonjung
2014-02-01
Filtering is an approach for incorporating observed data into time-evolving systems. Instead of a family of Dirac delta masses that is widely used in Monte Carlo methods, we here use the Wiener chaos expansion for the parametrization of the conditioned probability distribution to solve the nonlinear filtering problem. The Wiener chaos expansion is not the best method for uncertainty propagation without observations. Nevertheless, the projection of the system variables in a fixed polynomial basis spanning the probability space might be a competitive representation in the presence of relatively frequent observations because the Wiener chaos approach not only leads to an accurate and efficient prediction for short time uncertainty quantification, but it also allows to apply several data assimilation methods that can be used to yield a better approximate filtering solution. The aim of the present paper is to investigate this hypothesis. We answer in the affirmative for the (stochastic) Lorenz-63 system based on numerical simulations in which the uncertainty quantification method and the data assimilation method are adaptively selected by whether the dynamics is driven by Brownian motion and the near-Gaussianity of the measure to be updated, respectively.
Cubature on Wiener Space: Pathwise Convergence
Bayer, Christian Friz, Peter K.
2013-04-15
Cubature on Wiener space (Lyons and Victoir in Proc. R. Soc. Lond. A 460(2041):169-198, 2004) provides a powerful alternative to Monte Carlo simulation for the integration of certain functionals on Wiener space. More specifically, and in the language of mathematical finance, cubature allows for fast computation of European option prices in generic diffusion models.We give a random walk interpretation of cubature and similar (e.g. the Ninomiya-Victoir) weak approximation schemes. By using rough path analysis, we are able to establish weak convergence for general path-dependent option prices.
Ship detection for high resolution optical imagery with adaptive target filter
NASA Astrophysics Data System (ADS)
Ju, Hongbin
2015-10-01
Ship detection is important due to both its civil and military use. In this paper, we propose a novel ship detection method, Adaptive Target Filter (ATF), for high resolution optical imagery. The proposed framework can be grouped into two stages, where in the first stage, a test image is densely divided into different detection windows and each window is transformed to a feature vector in its feature space. The Histograms of Oriented Gradients (HOG) is accumulated as a basic feature descriptor. In the second stage, the proposed ATF highlights all the ship regions and suppresses the undesired backgrounds adaptively. Each detection window is assigned a score, which represents the degree of the window belonging to a certain ship category. The ATF can be adaptively obtained by the weighted Logistic Regression (WLR) according to the distribution of backgrounds and targets of the input image. The main innovation of our method is that we only need to collect positive training samples to build the filter, while the negative training samples are adaptively generated by the input image. This is different to other classification method such as Support Vector Machine (SVM) and Logistic Regression (LR), which need to collect both positive and negative training samples. The experimental result on 1-m high resolution optical images shows the proposed method achieves a desired ship detection performance with higher quality and robustness than other methods, e.g., SVM and LR.
Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators
Stuart, Andrew M.; Shukla, Abhishek; Sanz-Alonso, Daniel; Law, K. J. H.
2016-02-23
In the context of filtering chaotic dynamical systems it is well-known that partial observations, if sufficiently informative, can be used to control the inherent uncertainty due to chaos. The purpose of this paper is to investigate, both theoretically and numerically, conditions on the observations of chaotic systems under which they can be accurately filtered. In particular, we highlight the advantage of adaptive observation operators over fixed ones. The Lorenz ’96 model is used to exemplify our findings. Here, we consider discrete-time and continuous-time observations in our theoretical developments. We prove that, for fixed observation operator, the 3DVAR filter can recovermore » the system state within a neighbourhood determined by the size of the observational noise. It is required that a sufficiently large proportion of the state vector is observed, and an explicit form for such sufficient fixed observation operator is given. Numerical experiments, where the data is incorporated by use of the 3DVAR and extended Kalman filters, suggest that less informative fixed operators than given by our theory can still lead to accurate signal reconstruction. Adaptive observation operators are then studied numerically; we show that, for carefully chosen adaptive observation operators, the proportion of the state vector that needs to be observed is drastically smaller than with a fixed observation operator. Indeed, we show that the number of state coordinates that need to be observed may even be significantly smaller than the total number of positive Lyapunov exponents of the underlying system.« less
NASA Astrophysics Data System (ADS)
Sartori, Pablo; Tu, Yuhai
2011-04-01
Two distinct mechanisms for filtering noise in an input signal are identified in a class of adaptive sensory networks. We find that the high-frequency noise is filtered by the output degradation process through time-averaging; while the low-frequency noise is damped by adaptation through negative feedback. Both filtering processes themselves introduce intrinsic noises, which are found to be unfiltered and can thus amount to a significant internal noise floor even without signaling. These results are applied to E. coli chemotaxis. We show unambiguously that the molecular mechanism for the Berg-Purcell time-averaging scheme is the dephosphorylation of the response regulator CheY-P, not the receptor adaptation process as previously suggested. The high-frequency noise due to the stochastic ligand binding-unbinding events and the random ligand molecule diffusion is averaged by the CheY-P dephosphorylation process to a negligible level in E. coli. We identify a previously unstudied noise source caused by the random motion of the cell in a ligand gradient. We show that this random walk induced signal noise has a divergent low-frequency component, which is only rendered finite by the receptor adaptation process. For gradients within the E. coli sensing range, this dominant external noise can be comparable to the significant intrinsic noise in the system. The dependence of the response and its fluctuations on the key time scales of the system are studied systematically. We show that the chemotaxis pathway may have evolved to optimize gradient sensing, strong response, and noise control in different time scales.
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback-Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback–Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
NASA Astrophysics Data System (ADS)
Yushkov, Konstantin B.; Molchanov, Vladimir Y.; Belousov, Pavel V.; Abrosimov, Aleksander Y.
2016-01-01
We report a method for edge enhancement in the images of transparent samples using analog image processing in coherent light. The experimental technique is based on adaptive spatial filtering with an acousto-optic tunable filter in a telecentric optical system. We demonstrate processing of microscopic images of unstained and stained histological sections of human thyroid tumor with improved contrast.
Improving the response of accelerometers for automotive applications by using LMS adaptive filters.
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg; Fernández, Eduardo
2010-01-01
In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s(2)) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory. PMID:22315542
Locally adaptive regression filter-based infrared focal plane array non-uniformity correction
NASA Astrophysics Data System (ADS)
Li, Jia; Qin, Hanlin; Yan, Xiang; Huang, He; Zhao, Yingjuan; Zhou, Huixin
2015-10-01
Due to the limitations of the manufacturing technology, the response rates to the same infrared radiation intensity in each infrared detector unit are not identical. As a result, the non-uniformity of infrared focal plane array, also known as fixed pattern noise (FPN), is generated. To solve this problem, correcting the non-uniformity in infrared image is a promising approach, and many non-uniformity correction (NUC) methods have been proposed. However, they have some defects such as slow convergence, ghosting and scene degradation. To overcome these defects, a novel non-uniformity correction method based on locally adaptive regression filter is proposed. First, locally adaptive regression method is used to separate the infrared image into base layer containing main scene information and the detail layer containing detailed scene with FPN. Then, the detail layer sequence is filtered by non-linear temporal filter to obtain the non-uniformity. Finally, the high quality infrared image is obtained by subtracting non-uniformity component from original image. The experimental results show that the proposed method can significantly eliminate the ghosting and the scene degradation. The results of correction are superior to the THPF-NUC and NN-NUC in the aspects of subjective visual and objective evaluation index.
Improving the response of accelerometers for automotive applications by using LMS adaptive filters.
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg; Fernández, Eduardo
2010-01-01
In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s(2)) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory.
Chen, Xiyuan; Wang, Xiying; Xu, Yuan
2014-01-01
This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively. PMID:25502124
Chen, Xiyuan; Wang, Xiying; Xu, Yuan
2014-12-09
This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.
High performance 3D adaptive filtering for DSP based portable medical imaging systems
NASA Astrophysics Data System (ADS)
Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark
2015-03-01
Portable medical imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. Despite their constraints on power, size and cost, portable imaging devices must still deliver high quality images. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often cannot be run with sufficient performance on a portable platform. In recent years, advanced multicore digital signal processors (DSP) have been developed that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms on a portable platform. In this study, the performance of a 3D adaptive filtering algorithm on a DSP is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec with an Ultrasound 3D probe. Relative performance and power is addressed between a reference PC (Quad Core CPU) and a TMS320C6678 DSP from Texas Instruments.
Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter
NASA Astrophysics Data System (ADS)
Yoshikawa, Ruriha; Teramoto, Atsushi; Matsubara, Tomoko; Fujita, Hiroshi
2013-03-01
Breast cancer is a serious health concern for all women. Computer-aided detection for mammography has been used for detecting mass and micro-calcification. However, there are challenges regarding the automated detection of the architectural distortion about the sensitivity. In this study, we propose a novel automated method for detecting architectural distortion. Our method consists of the analysis of the mammary gland structure, detection of the distorted region, and reduction of false positive results. We developed the adaptive Gabor filter for analyzing the mammary gland structure that decides filter parameters depending on the thickness of the gland structure. As for post-processing, healthy mammary glands that run from the nipple to the chest wall are eliminated by angle analysis. Moreover, background mammary glands are removed based on the intensity output image obtained from adaptive Gabor filter. The distorted region of the mammary gland is then detected as an initial candidate using a concentration index followed by binarization and labeling. False positives in the initial candidate are eliminated using 23 types of characteristic features and a support vector machine. In the experiments, we compared the automated detection results with interpretations by a radiologist using 50 cases (200 images) from the Digital Database of Screening Mammography (DDSM). As a result, true positive rate was 82.72%, and the number of false positive per image was 1.39. There results indicate that the proposed method may be useful for detecting architectural distortion in mammograms.
Ray, Jaideep; Lefantzi, Sophia; Najm, Habib N.; Kennedy, Christopher A.
2006-01-01
Block-structured adaptively refined meshes (SAMR) strive for efficient resolution of partial differential equations (PDEs) solved on large computational domains by clustering mesh points only where required by large gradients. Previous work has indicated that fourth-order convergence can be achieved on such meshes by using a suitable combination of high-order discretizations, interpolations, and filters and can deliver significant computational savings over conventional second-order methods at engineering error tolerances. In this paper, we explore the interactions between the errors introduced by discretizations, interpolations and filters. We develop general expressions for high-order discretizations, interpolations, and filters, in multiple dimensions, using a Fourier approach, facilitating the high-order SAMR implementation. We derive a formulation for the necessary interpolation order for given discretization and derivative orders. We also illustrate this order relationship empirically using one and two-dimensional model problems on refined meshes. We study the observed increase in accuracy with increasing interpolation order. We also examine the empirically observed order of convergence, as the effective resolution of the mesh is increased by successively adding levels of refinement, with different orders of discretization, interpolation, or filtering.
Codimension- p Paley-Wiener theorems
NASA Astrophysics Data System (ADS)
Yang, Yan; Qian, Tao; Sommen, Frank
2007-04-01
We obtain the generalized codimension- p Cauchy-Kovalevsky extension of the exponential function e^{i
Adaptive hybrid likelihood model for visual tracking based on Gaussian particle filter
NASA Astrophysics Data System (ADS)
Wang, Yong; Tan, Yihua; Tian, Jinwen
2010-07-01
We present a new scheme based on multiple-cue integration for visual tracking within a Gaussian particle filter framework. The proposed method integrates the color, shape, and texture cues of an object to construct a hybrid likelihood model. During the measurement step, the likelihood model can be switched adaptively according to environmental changes, which improves the object representation to deal with the complex disturbances, such as appearance changes, partial occlusions, and significant clutter. Moreover, the confidence weights of the cues are adjusted online through the estimation using a particle filter, which ensures the tracking accuracy and reliability. Experiments are conducted on several real video sequences, and the results demonstrate that the proposed method can effectively track objects in complex scenarios. Compared with previous similar approaches through some quantitative and qualitative evaluations, the proposed method performs better in terms of tracking robustness and precision.
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF. PMID:27475606
NASA Astrophysics Data System (ADS)
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.
Automatic balancing of AMB systems using plural notch filter and adaptive synchronous compensation
NASA Astrophysics Data System (ADS)
Xu, Xiangbo; Chen, Shao; Zhang, Yanan
2016-07-01
To achieve automatic balancing in active magnetic bearing (AMB) system, a control method with notch filters and synchronous compensators is widely employed. However, the control precision is significantly affected by the synchronous compensation error, which is caused by parameter errors and variations of the power amplifiers. Furthermore, the computation effort may become intolerable if a 4-degree-of-freedom (dof) AMB system is studied. To solve these problems, an adaptive automatic balancing control method in the AMB system is presented in this study. Firstly, a 4-dof radial AMB system is described and analyzed. To simplify the controller design, the 4-dof dynamic equations are transferred into two plural functions related to translation and rotation, respectively. Next, to achieve automatic balancing of the AMB system, two synchronous equations are formed. Solution of them leads to a control strategy based on notch filters and feedforward controllers with an inverse function of the power amplifier. The feedforward controllers can be simplified as synchronous phases and amplitudes. Then, a plural phase-shift notch filter which can identify the synchronous components in 2-dof motions is formulated, and an adaptive compensation method that can form two closed-loop systems to tune the synchronous amplitude of the feedforward controller and the phase of the plural notch filter is proposed. Finally, the proposed control strategy is verified by both simulations and experiments on a test rig of magnetically suspended control moment gyro. The results indicate that this method can fulfill the automatic balancing of the AMB system with a light computational load.
The 2dF Galaxy Redshift Survey: Wiener reconstruction of the cosmic web
NASA Astrophysics Data System (ADS)
Erdoǧdu, Pirin; Lahav, Ofer; Zaroubi, Saleem; Efstathiou, George; Moody, Steve; Peacock, John A.; Colless, Matthew; Baldry, Ivan K.; Baugh, Carlton M.; Bland-Hawthorn, Joss; Bridges, Terry; Cannon, Russell; Cole, Shaun; Collins, Chris; Couch, Warrick; Dalton, Gavin; De Propris, Roberto; Driver, Simon P.; Ellis, Richard S.; Frenk, Carlos S.; Glazebrook, Karl; Jackson, Carole; Lewis, Ian; Lumsden, Stuart; Maddox, Steve; Madgwick, Darren; Norberg, Peder; Peterson, Bruce A.; Sutherland, Will; Taylor, Keith
2004-08-01
We reconstruct the underlying density field of the Two-degree Field Galaxy Redshift Survey (2dFGRS) for the redshift range 0.035 < z < 0.200 using the Wiener filtering method. The Wiener filter suppresses shot noise and accounts for selection and incompleteness effects. The method relies on prior knowledge of the 2dF power spectrum of fluctuations and the combination of matter density and bias parameters, however the results are only slightly affected by changes to these parameters. We present maps of the density field. We use a variable smoothing technique with two different effective resolutions: 5 and 10 h-1 Mpc at the median redshift of the survey. We identify all major superclusters and voids in the survey. In particular, we find two large superclusters and two large local voids. The full set of colour maps can be viewed on the World Wide Web at http://www.ast.cam.ac.uk/~pirin.
Liu, Yan; Pecht, Michael G
2006-01-01
The effectiveness of electrocardiogram (ECG) monitors can be significantly impaired by motion artifacts which can cause misdiagnoses, lead to inappropriate treatment decisions, and trigger false alarms. Skin stretch associated with patient motion is a significant source of motion artifacts in current ECG monitoring. In this study, motion artifacts are adaptively filtered by using skin strain as the reference variable. Skin strain is measured non-invasively using a light emitting diode (LED) and an optical sensor incorporated in an ECG electrode. The results demonstrate that this device and method can significantly reduce skin strain induced ECG artifacts.
Evaluation of an adaptive filtering algorithm for CT cardiac imaging with EKG modulated tube current
NASA Astrophysics Data System (ADS)
Li, Jianying; Hsieh, Jiang; Mohr, Kelly; Okerlund, Darin
2005-04-01
We have developed an adaptive filtering algorithm for cardiac CT scans with EKG-modulated tube current to optimize resolution and noise for different cardiac phases and to provide safety net for cases where end-systole phase is used for coronary imaging. This algorithm has been evaluated using patient cardiac CT scans where lower tube currents are used for the systolic phases. In this paper, we present the evaluation results. The results demonstrated that with the use of the proposed algorithm, we could improve image quality for all cardiac phases, while providing greater noise and streak artifact reduction for systole phases where lower CT dose were used.
Forward scattering detection of a submerged moving target based on adaptive filtering technique.
He, Chuanlin; Yang, Kunde; Lei, Bo; Ma, Yuanliang
2015-09-01
Forward scattered waves are always overwhelmed by severely intense direct blasts when a submerged target crosses the source-receiver line. A processing scheme called direct blast suppression based on adaptive filtering (DBS-AF) is proposed to suppress such blasts. A verification experiment was conducted in a lake with a vertical hydrophone array and 10 kHz CW impulses. Processing results show that the direct blast is suppressed in a single channel, and an intruding target is identified by the lobes in the detection curve. The detection performance is improved by adopting a time-delay beam-former on the array as a pre-processing technique. PMID:26428829
NASA Astrophysics Data System (ADS)
Koga, Takanori; Suetake, Noriaki
2015-02-01
This paper describes the detail-preserving impulse noise removal performance of a one-dimensional (1-D) switching median filter (SMF) applied along an adaptive space-filling curve. Usually, a SMF with a two-dimensional (2-D) filter window is widely used for impulse noise removal while still preserving detailed parts in an input image. However, the noise detector of the 2-D filter does not always distinguish between the original pixels and the noise-corrupted ones perfectly. In particular, pixels constituting thin lines in an input image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered regardless of the necessity of the filtering. To cope with this problem, we propose a new impulse noise removal method based on a 1-D SMF and a space-filling curve which is adaptively drawn using a minimum spanning tree reflecting structural context of an input image.
Sadjadi, Firooz A; Mahalanobis, Abhijit
2006-05-01
We report the development of a technique for adaptive selection of polarization ellipse tilt and ellipticity angles such that the target separation from clutter is maximized. From the radar scattering matrix [S] and its complex components, in phase and quadrature phase, the elements of the Mueller matrix are obtained. Then, by means of polarization synthesis, the radar cross section of the radar scatters are obtained at different transmitting and receiving polarization states. By designing a maximum average correlation height filter, we derive a target versus clutter distance measure as a function of four transmit and receive polarization state angles. The results of applying this method on real synthetic aperture radar imagery indicate a set of four transmit and receive angles that lead to maximum target versus clutter discrimination. These optimum angles are different for different targets. Hence, by adaptive control of the state of polarization of polarimetric radar, one can noticeably improve the discrimination of targets from clutter.
High dynamic range image rendering with a Retinex-based adaptive filter.
Meylan, Laurence; Süsstrunk, Sabine
2006-09-01
We propose a new method to render high dynamic range images that models global and local adaptation of the human visual system. Our method is based on the center-surround Retinex model. The novelties of our method is first to use an adaptive filter, whose shape follows the image high-contrast edges, thus reducing halo artifacts common to other methods. Second, only the luminance channel is processed, which is defined by the first component of a principal component analysis. Principal component analysis provides orthogonality between channels and thus reduces the chromatic changes caused by the modification of luminance. We show that our method efficiently renders high dynamic range images and we compare our results with the current state of the art. PMID:16948325
Baresová, E; Grieszbach, G; Schack, B; Vilser, W; Bräuer-Burchardt, C; Senff, I
This study deals with methods focused on estimating blood velocity. The estimation of the linear trend function of a non-stationary signal based on the adaptive recursive estimation of the mean value function is used for the determination of the time delay of two indicator dilution curves. The filter property of this trend operator depends on the choice of a constant parameter c, the so-called adaptation factor. The functional connection between the filter property and the adaptation factor is considered in such a way that an objective calculation of arterial blood velocity in retinal vessels is possible.
AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal
Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang
2015-01-01
An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal. PMID:26512665
AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.
Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang
2015-10-23
An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.
Experimental Demonstration of Adaptive Infrared Multispectral Imaging using Plasmonic Filter Array
Jang, Woo-Yong; Ku, Zahyun; Jeon, Jiyeon; Kim, Jun Oh; Lee, Sang Jun; Park, James; Noyola, Michael J.; Urbas, Augustine
2016-01-01
In our previous theoretical study, we performed target detection using a plasmonic sensor array incorporating the data-processing technique termed “algorithmic spectrometry”. We achieved the reconstruction of a target spectrum by extracting intensity at multiple wavelengths with high resolution from the image data obtained from the plasmonic array. The ultimate goal is to develop a full-scale focal plane array with a plasmonic opto-coupler in order to move towards the next generation of versatile infrared cameras. To this end, and as an intermediate step, this paper reports the experimental demonstration of adaptive multispectral imagery using fabricated plasmonic spectral filter arrays and proposed target detection scenarios. Each plasmonic filter was designed using periodic circular holes perforated through a gold layer, and an enhanced target detection strategy was proposed to refine the original spectrometry concept for spatial and spectral computation of the data measured from the plasmonic array. Both the spectrum of blackbody radiation and a metal ring object at multiple wavelengths were successfully reconstructed using the weighted superposition of plasmonic output images as specified in the proposed detection strategy. In addition, plasmonic filter arrays were theoretically tested on a target at extremely high temperature as a challenging scenario for the detection scheme. PMID:27721506
Charisis, Vasileios S; Hadjileontiadis, Leontios J
2016-01-01
A new feature extraction technique for the detection of lesions created from mucosal inflammations in Crohn’s disease, based on wireless capsule endoscopy (WCE) images processing is presented here. More specifically, a novel filtering process, namely Hybrid Adaptive Filtering (HAF), was developed for efficient extraction of lesion-related structural/textural characteristics from WCE images, by employing Genetic Algorithms to the Curvelet-based representation of images. Additionally, Differential Lacunarity (DLac) analysis was applied for feature extraction from the HAF-filtered images. The resulted scheme, namely HAF-DLac, incorporates support vector machines for robust lesion recognition performance. For the training and testing of HAF-DLac, an 800-image database was used, acquired from 13 patients who undertook WCE examinations, where the abnormal cases were grouped into mild and severe, according to the severity of the depicted lesion, for a more extensive evaluation of the performance. Experimental results, along with comparison with other related efforts, have shown that the HAF-DLac approach evidently outperforms them in the field of WCE image analysis for automated lesion detection, providing higher classification results, up to 93.8% (accuracy), 95.2% (sensitivity), 92.4% (specificity) and 92.6% (precision). The promising performance of HAF-DLac paves the way for a complete computer-aided diagnosis system that could support physicians’ clinical practice.
Experimental Demonstration of Adaptive Infrared Multispectral Imaging using Plasmonic Filter Array
NASA Astrophysics Data System (ADS)
Jang, Woo-Yong; Ku, Zahyun; Jeon, Jiyeon; Kim, Jun Oh; Lee, Sang Jun; Park, James; Noyola, Michael J.; Urbas, Augustine
2016-10-01
In our previous theoretical study, we performed target detection using a plasmonic sensor array incorporating the data-processing technique termed “algorithmic spectrometry”. We achieved the reconstruction of a target spectrum by extracting intensity at multiple wavelengths with high resolution from the image data obtained from the plasmonic array. The ultimate goal is to develop a full-scale focal plane array with a plasmonic opto-coupler in order to move towards the next generation of versatile infrared cameras. To this end, and as an intermediate step, this paper reports the experimental demonstration of adaptive multispectral imagery using fabricated plasmonic spectral filter arrays and proposed target detection scenarios. Each plasmonic filter was designed using periodic circular holes perforated through a gold layer, and an enhanced target detection strategy was proposed to refine the original spectrometry concept for spatial and spectral computation of the data measured from the plasmonic array. Both the spectrum of blackbody radiation and a metal ring object at multiple wavelengths were successfully reconstructed using the weighted superposition of plasmonic output images as specified in the proposed detection strategy. In addition, plasmonic filter arrays were theoretically tested on a target at extremely high temperature as a challenging scenario for the detection scheme.
NASA Astrophysics Data System (ADS)
Dong, Gangqi; Zhu, Zheng H.
2016-05-01
This paper presents a real-time, vision-based algorithm for the pose and motion estimation of non-cooperative targets and its application in visual servo robotic manipulator to perform autonomous capture. A hybrid approach of adaptive extended Kalman filter and photogrammetry is developed for the real-time pose and motion estimation of non-cooperative targets. Based on the pose and motion estimates, the desired pose and trajectory of end-effector is defined and the corresponding desired joint angles of the robotic manipulator are derived by inverse kinematics. A close-loop visual servo control scheme is then developed for the robotic manipulator to track, approach and capture the target. Validating experiments are designed and performed on a custom-built six degrees of freedom robotic manipulator with an eye-in-hand configuration. The experimental results demonstrate the feasibility, effectiveness and robustness of the proposed adaptive extended Kalman filter enabled pose and motion estimation and visual servo strategy.
Bai, Mingsian R; Chi, Li-Wen; Liang, Li-Huang; Lo, Yi-Yang
2016-02-01
In this paper, an evolutionary exposition is given in regard to the enhancing strategies for acoustic echo cancellers (AECs). A fixed beamformer (FBF) is utilized to focus on the near-end speaker while suppressing the echo from the far end. In reality, the array steering vector could differ considerably from the ideal freefield plane wave model. Therefore, an experimental procedure is developed to interpolate a practical array model from the measured frequency responses. Subband (SB) filtering with polyphase implementation is exploited to accelerate the cancellation process. Generalized sidelobe canceller (GSC) composed of an FBF and an adaptive blocking module is combined with AEC to maximize cancellation performance. Another enhancement is an internal iteration (IIT) procedure that enables efficient convergence in the adaptive SB filters within a sample time. Objective tests in terms of echo return loss enhancement (ERLE), perceptual evaluation of speech quality (PESQ), word recognition rate for automatic speech recognition (ASR), and subjective listening tests are conducted to validate the proposed AEC approaches. The results show that the GSC-SB-AEC-IIT approach has attained the highest ERLE without speech quality degradation, even in double-talk scenarios. PMID:26936567
Adaptive filtering of ECG interference on surface EEnGs based on signal averaging.
Garcia-Casado, Javier; Martinez-de-Juan, Jose L; Ponce, Jose L
2006-06-01
An external electroenterogram (EEnG) is the recording of the small bowel myoelectrical signal using contact electrodes placed on the abdominal surface. It is a weak signal affected by possible movements and by the interferences of respiration and, principally, of the cardiac signal. In this paper an adaptive filtering technique was proposed to identify and subsequently cancel ECG interference on canine surface EEnGs by means of a signal averaging process time-locked with the R-wave. Twelve recording sessions were carried out on six conscious dogs in the fasting state. The adaptive filtering technique used increases the signal-to-interference ratio of the raw surface EEnG from 16.7 +/- 6.5 dB up to 31.9 +/- 4.0 dB. In addition to removing ECG interference, this technique has been proven to respect intestinal SB activity, i.e. the EEnG component associated with bowel contractions, despite the fact that they overlap in the frequency domain. In this way, more robust non-invasive intestinal motility indicators can be obtained with correlation coefficients of 0.68 +/- 0.09 with internal intestinal activity. The method proposed here may also be applied to other biological recordings affected by cardiac interference and could be a very helpful tool for future applications of non-invasive recordings of gastrointestinal signals.
Research of fetal ECG extraction using wavelet analysis and adaptive filtering.
Wu, Shuicai; Shen, Yanni; Zhou, Zhuhuang; Lin, Lan; Zeng, Yanjun; Gao, Xiaofeng
2013-10-01
Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transform (SWT), and the wavelet coefficients at each scale were obtained. For each scale, the detail coefficients were processed by the LMS algorithm. The coefficient of the abdominal signal was taken as the original input of the LMS adaptive filtering system, and the coefficient of the thoracic signal as the reference input. Then, correlations of the processed wavelet coefficients were computed. The threshold was set and noise components were removed with the SSNF algorithm. Finally, the processed wavelet coefficients were reconstructed by inverse SWT to obtain fetal ECG. Twenty cases of simulated data and 12 cases of clinical data were used. Experimental results showed that the proposed method outperforms the LMS algorithm: (1) it shows improvement in case of superposition R-peaks of fetal ECG and maternal ECG; (2) noise disturbance is eliminated by incorporating the SSNF algorithm and the extracted waveform is more stable; and (3) the performance is proven quantitatively by SNR calculation. The results indicated that the proposed algorithm can be used for extracting fetal ECG from abdominal signals.
Crowder, S.V.; Eshleman, L.
1998-08-01
In many manufacturing environments such as the nuclear weapons complex, emphasis has shifted from the regular production and delivery of large orders to infrequent small orders. However, the challenge to maintain the same high quality and reliability standards white building much smaller lot sizes remains. To meet this challenge, specific areas need more attention, including fast and on-target process start-up, low volume statistical process control, process characterization with small experiments, and estimating reliability given few actual performance tests of the product. In this paper the authors address the issue of low volume statistical process control. They investigate an adaptive filtering approach to process monitoring with a relatively short time series of autocorrelated data. The emphasis is on estimation and minimization of mean squared error rather than the traditional hypothesis testing and run length analyses associated with process control charting. The authors develop an adaptive filtering technique that assumes initial process parameters are unknown, and updates the parameters as more data become available. Using simulation techniques, they study the data requirements (the length of a time series of autocorrelated data) necessary to adequately estimate process parameters. They show that far fewer data values are needed than is typically recommended for process control applications. And they demonstrate the techniques with a case study from the nuclear weapons manufacturing complex.
CROWDER, STEPHEN V.
1999-09-01
In many manufacturing environments such as the nuclear weapons complex, emphasis has shifted from the regular production and delivery of large orders to infrequent small orders. However, the challenge to maintain the same high quality and reliability standards while building much smaller lot sizes remains. To meet this challenge, specific areas need more attention, including fast and on-target process start-up, low volume statistical process control, process characterization with small experiments, and estimating reliability given few actual performance tests of the product. In this paper we address the issue of low volume statistical process control. We investigate an adaptive filtering approach to process monitoring with a relatively short time series of autocorrelated data. The emphasis is on estimation and minimization of mean squared error rather than the traditional hypothesis testing and run length analyses associated with process control charting. We develop an adaptive filtering technique that assumes initial process parameters are unknown, and updates the parameters as more data become available. Using simulation techniques, we study the data requirements (the length of a time series of autocorrelated data) necessary to adequately estimate process parameters. We show that far fewer data values are needed than is typically recommended for process control applications. We also demonstrate the techniques with a case study from the nuclear weapons manufacturing complex.
Johansson, A Torbjorn; White, Paul R
2011-08-01
This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, which is an established technique for frequency tracking. For application to automatic whistle processing, methods for detection and improved frequency tracking through frequency crossings as well as interfering transients are developed and coupled to the frequency tracker. Background noise estimation and compensation is accomplished using order statistics and pre-whitening. Using simulated signals as well as recorded calls of marine mammals and a human whistled speech utterance, it is shown that the proposed method can detect more simultaneous whistles than two competing spectrogram-based methods while not reporting any false alarms on the example datasets. In one example, it extracts complete 1.4 and 1.8 s bottlenose dolphin whistles successfully through frequency crossings. The method performs detection and estimates frequency tracks even at high sweep rates. The algorithm is also shown to be effective on human whistled utterances. PMID:21877804
Carmena, Jose M.
2016-01-01
Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to
Shanechi, Maryam M; Orsborn, Amy L; Carmena, Jose M
2016-04-01
Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain's behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user's motor intention during CLDA-a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter
Chen, Ming-Hung
2015-01-01
This paper proposes a new adaptive filter for wind generators that combines instantaneous reactive power compensation technology and current prediction controller, and therefore this system is characterized by low harmonic distortion, high power factor, and small DC-link voltage variations during load disturbances. The performance of the system was first simulated using MATLAB/Simulink, and the possibility of an adaptive digital low-pass filter eliminating current harmonics was confirmed in steady and transient states. Subsequently, a digital signal processor was used to implement an active power filter. The experimental results indicate, that for the rated operation of 2 kVA, the system has a total harmonic distortion of current less than 5.0% and a power factor of 1.0 on the utility side. Thus, the transient performance of the adaptive filter is superior to the traditional digital low-pass filter and is more economical because of its short computation time compared with other types of adaptive filters. PMID:26451391
Chen, Ming-Hung
2015-01-01
This paper proposes a new adaptive filter for wind generators that combines instantaneous reactive power compensation technology and current prediction controller, and therefore this system is characterized by low harmonic distortion, high power factor, and small DC-link voltage variations during load disturbances. The performance of the system was first simulated using MATLAB/Simulink, and the possibility of an adaptive digital low-pass filter eliminating current harmonics was confirmed in steady and transient states. Subsequently, a digital signal processor was used to implement an active power filter. The experimental results indicate, that for the rated operation of 2 kVA, the system has a total harmonic distortion of current less than 5.0% and a power factor of 1.0 on the utility side. Thus, the transient performance of the adaptive filter is superior to the traditional digital low-pass filter and is more economical because of its short computation time compared with other types of adaptive filters.
Chen, Ming-Hung
2015-01-01
This paper proposes a new adaptive filter for wind generators that combines instantaneous reactive power compensation technology and current prediction controller, and therefore this system is characterized by low harmonic distortion, high power factor, and small DC-link voltage variations during load disturbances. The performance of the system was first simulated using MATLAB/Simulink, and the possibility of an adaptive digital low-pass filter eliminating current harmonics was confirmed in steady and transient states. Subsequently, a digital signal processor was used to implement an active power filter. The experimental results indicate, that for the rated operation of 2 kVA, the system has a total harmonic distortion of current less than 5.0% and a power factor of 1.0 on the utility side. Thus, the transient performance of the adaptive filter is superior to the traditional digital low-pass filter and is more economical because of its short computation time compared with other types of adaptive filters. PMID:26451391
Mazumder, Ria; Clymer, Bradley D; Mo, Xiaokui; White, Richard D; Kolipaka, Arunark
2016-06-01
Diffusion tensor imaging (DTI) is used to quantify myocardial fiber orientation based on helical angles (HA). Accurate HA measurements require multiple excitations (NEX) and/or several diffusion encoding directions (DED). However, increasing NEX and/or DED increases acquisition time (TA). Therefore, in this study, we propose to reduce TA by implementing a 3D adaptive anisotropic Gaussian filter (AAGF) on the DTI data acquired from ex-vivo healthy and infarcted porcine hearts. DTI was performed on ex-vivo hearts [9-healthy, 3-myocardial infarction (MI)] with several combinations of DED and NEX. AAGF, mean (AVF) and median filters (MF) were applied on the primary eigenvectors of the diffusion tensor prior to HA estimation. The performance of AAGF was compared against AVF and MF. Root mean square error (RMSE), concordance correlation-coefficients and Bland-Altman's technique was used to determine optimal combination of DED and NEX that generated the best HA maps in the least possible TA. Lastly, the effect of implementing AAGF on the infarcted porcine hearts was also investigated. RMSE in HA estimation for AAGF was lower compared to AVF or MF. Post-filtering (AAGF) fewer DED and NEX were required to achieve HA maps with similar integrity as those obtained from higher NEX and/or DED. Pathological alterations caused in HA orientation in the MI model were preserved post-filtering (AAGF). Our results demonstrate that AAGF reduces TA without affecting the integrity of the myocardial microstructure. PMID:26843150
Wiener criterion for X-elliptic operators
NASA Astrophysics Data System (ADS)
Tralli, Giulio; Uguzzoni, Francesco
2015-12-01
In this note we prove a Wiener criterion of regularity of boundary points for the Dirichlet problem related to X-elliptic operators in divergence form enjoying the doubling condition and the Poincaré inequality. As a step towards this result, we exhibit some other characterizations of regularity in terms of the capacitary potentials. Finally, we also show that a cone-type criterion holds true in our setting.
Wiener-Hammerstein system identification - an evolutionary approach
NASA Astrophysics Data System (ADS)
Naitali, Abdessamad; Giri, Fouad
2016-01-01
The problem of identifying parametric Wiener-Hammerstein (WH) systems is addressed within the evolutionary optimisation context. Specifically, a hybrid culture identification method is developed that involves model structure adaptation using genetic recombination and model parameter learning using particle swarm optimisation. The method enjoys three interesting features: (1) the risk of premature convergence of model parameter estimates to local optima is significantly reduced, due to the constantly maintained diversity of model candidates; (2) no prior knowledge is needed except for upper bounds on the system structure indices; (3) the method is fully autonomous as no interaction is needed with the user during the optimum search process. The performances of the proposed method will be illustrated and compared to alternative methods using a well-established WH benchmark.
NASA Astrophysics Data System (ADS)
Barton, P.
1987-04-01
The basic principles of adaptive antennas are outlined in terms of the Wiener-Hopf expression for maximizing signal to noise ratio in an arbitrary noise environment; the analogy with generalized matched filter theory provides a useful aid to understanding. For many applications, there is insufficient information to achieve the above solution and thus non-optimum constrained null steering algorithms are also described, together with a summary of methods for preventing wanted signals being nulled by the adaptive system. The three generic approaches to adaptive weight control are discussed; correlation steepest descent, weight perturbation and direct solutions based on sample matrix conversion. The tradeoffs between hardware complexity and performance in terms of null depth and convergence rate are outlined. The sidelobe cancellor technique is described. Performance variation with jammer power and angular distribution is summarized and the key performance limitations identified. The configuration and performance characteristics of both multiple beam and phase scan array antennas are covered, with a brief discussion of performance factors.
Piaggi, Paolo; Menicucci, Danilo; Gentili, Claudio; Handjaras, Giacomo; Gemignani, Angelo; Landi, Alberto
2014-05-01
Sources of noise in resting-state fMRI experiments include instrumental and physiological noises, which need to be filtered before a functional connectivity analysis of brain regions is performed. These noisy components show autocorrelated and nonstationary properties that limit the efficacy of standard techniques (i.e. time filtering and general linear model). Herein we describe a novel approach based on the combination of singular spectrum analysis and adaptive filtering, which allows a greater noise reduction and yields better connectivity estimates between regions at rest, providing a new feasible procedure to analyze fMRI data.
Adaptive UAV attitude estimation employing unscented Kalman Filter, FOAM and low-cost MEMS sensors.
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.
Singh, Omkar; Sunkaria, Ramesh Kumar
2015-01-01
Separating an information-bearing signal from the background noise is a general problem in signal processing. In a clinical environment during acquisition of an electrocardiogram (ECG) signal, The ECG signal is corrupted by various noise sources such as powerline interference (PLI), baseline wander and muscle artifacts. This paper presents novel methods for reduction of powerline interference in ECG signals using empirical wavelet transform (EWT) and adaptive filtering. The proposed methods are compared with the empirical mode decomposition (EMD) based PLI cancellation methods. A total of six methods for PLI reduction based on EMD and EWT are analysed and their results are presented in this paper. The EWT-based de-noising methods have less computational complexity and are more efficient as compared with the EMD-based de-noising methods. PMID:25412942
Color filter array demosaicing: an adaptive progressive interpolation based on the edge type
NASA Astrophysics Data System (ADS)
Dong, Qiqi; Liu, Zhaohui
2015-10-01
Color filter array (CFA) is one of the key points for single-sensor digital cameras to produce color images. Bayer CFA is the most commonly used pattern. In this array structure, the sampling frequency of green is two times of red or blue, which is consistent with the sensitivity of human eyes to colors. However, each sensor pixel only samples one of three primary color values. To render a full-color image, an interpolation process, commonly referred to CFA demosaicing, is required to estimate the other two missing color values at each pixel. In this paper, we explore an adaptive progressive interpolation based on the edge type algorithm. The proposed demosaicing method consists of two successive steps: an interpolation step that estimates missing color values according to various edges and a post-processing step by iterative interpolation.
NASA Astrophysics Data System (ADS)
Ibey, Bennett; Subramanian, Hariharan; Ericson, Nance; Xu, Weijian; Wilson, Mark; Cote, Gerard L.
2005-03-01
A blood perfusion and oxygenation sensor has been developed for in situ monitoring of transplanted organs. In processing in situ data, motion artifacts due to increased perfusion can create invalid oxygenation saturation values. In order to remove the unwanted artifacts from the pulsatile signal, adaptive filtering was employed using a third wavelength source centered at 810nm as a reference signal. The 810 nm source resides approximately at the isosbestic point in the hemoglobin absorption curve where the absorbance of light is nearly equal for oxygenated and deoxygenated hemoglobin. Using an autocorrelation based algorithm oxygenation saturation values can be obtained without the need for large sampling data sets allowing for near real-time processing. This technique has been shown to be more reliable than traditional techniques and proven to adequately improve the measurement of oxygenation values in varying perfusion states.
Ko, Byung-hoon; Lee, Takhyung; Choi, Changmok; Kim, Youn-ho; Park, Gunguk; Kang, KyoungHo; Bae, Sang Kon; Shin, Kunsoo
2012-01-01
The electrocardiogram (ECG) is the main measurement parameter for effectively diagnosing chronic disease and guiding cardio-fitness therapy. ECGs contaminated by noise or artifacts disrupt the normal functioning of the automatic analysis algorithm. The objective of this study is to evaluate a method of measuring the HCP variation in motion artifacts through direct monitoring. The proposed wearable sensing device has two channels. One channel is used to measure the ECG through a differential amplifier. The other is for monitoring motion artifacts using the modified electrode and the same differential amplifier. Noise reduction was performed using adaptive filtering, based on a reference signal highly correlated with it. Direct measurement of HCP variations can eliminate the need for additional sensors. PMID:23366209
Yoon, Paul K; Zihajehzadeh, Shaghayegh; Bong-Soo Kang; Park, Edward J
2015-08-01
This paper proposes a novel indoor localization method using the Bluetooth Low Energy (BLE) and an inertial measurement unit (IMU). The multipath and non-line-of-sight errors from low-power wireless localization systems commonly result in outliers, affecting the positioning accuracy. We address this problem by adaptively weighting the estimates from the IMU and BLE in our proposed cascaded Kalman filter (KF). The positioning accuracy is further improved with the Rauch-Tung-Striebel smoother. The performance of the proposed algorithm is compared against that of the standard KF experimentally. The results show that the proposed algorithm can maintain high accuracy for position tracking the sensor in the presence of the outliers.
Yoon, Paul K; Zihajehzadeh, Shaghayegh; Bong-Soo Kang; Park, Edward J
2015-08-01
This paper proposes a novel indoor localization method using the Bluetooth Low Energy (BLE) and an inertial measurement unit (IMU). The multipath and non-line-of-sight errors from low-power wireless localization systems commonly result in outliers, affecting the positioning accuracy. We address this problem by adaptively weighting the estimates from the IMU and BLE in our proposed cascaded Kalman filter (KF). The positioning accuracy is further improved with the Rauch-Tung-Striebel smoother. The performance of the proposed algorithm is compared against that of the standard KF experimentally. The results show that the proposed algorithm can maintain high accuracy for position tracking the sensor in the presence of the outliers. PMID:26736389
Adaptation of Gabor filters for simulation of human preattentive mechanism for a mobile robot
NASA Astrophysics Data System (ADS)
Kulkarni, Naren; Naghdy, Golshah A.
1993-08-01
Vision guided mobile robot navigation is complex and requires analysis of tremendous amounts of information in real time. In order to simplify the task and reduce the amount of information, human preattentive mechanism can be adapted [Nag90]. During the preattentive search the scene is analyzed rapidly but in sufficient detail for the attention to be focused on the `area of interest.' The `area of interest' can further be scrutinized in more detail for recognition purposes. This `area of interest' can be a text message to facilitate navigation. Gabor filters and an automated turning mechanism are used to isolate the `area of interest.' These regions are subsequently processed with optimal spatial resolution for perception tasks. This method has clear advantages over the global operators in that, after an initial search, it scans each region of interest with optimum resolution. This reduces the volume of information for recognition stages and ensures that no region is over or under estimated.
Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance. PMID:23012559
Adaptive filters for monitoring localized brain activity from surface potential time series
Spencer, M.E. . Signal and Image Processing Inst. TRW, Inc., Redondo Beach, CA ); Leahy, R.M. . Signal and Image Processing Inst.); Mosher, J.C. . Signal and Image Processing Inst. Lo
1992-01-01
We address the problem of processing electroencephalographic (EEG) data to monitor the time series of the components of a current dipole source vector at a given location in the head. This is the spatial filtering problem for vector sources in a lossy, three dimensional, zero delay medium. Dipolar and distributed sources at other than the desired location are canceled or attenuated with an adaptive linearly constrained minimum variance (LCMV) beamformer. Actual EEG data acquired from a human subject serves as the interference in a case where the desired source is simulated and superimposed on the actual data. It is shown that the LCMV beamformer extracts the desired dipole time series while effectively canceling the subjects interference.
Adaptive filters for monitoring localized brain activity from surface potential time series
Spencer, M.E. |; Leahy, R.M.; Mosher, J.C. |; Lewis, P.S.
1992-12-01
We address the problem of processing electroencephalographic (EEG) data to monitor the time series of the components of a current dipole source vector at a given location in the head. This is the spatial filtering problem for vector sources in a lossy, three dimensional, zero delay medium. Dipolar and distributed sources at other than the desired location are canceled or attenuated with an adaptive linearly constrained minimum variance (LCMV) beamformer. Actual EEG data acquired from a human subject serves as the interference in a case where the desired source is simulated and superimposed on the actual data. It is shown that the LCMV beamformer extracts the desired dipole time series while effectively canceling the subjects interference.
Local stimulus disambiguation with global motion filters predicts adaptive surround modulation.
Dellen, Babette; Torras, Carme
2013-10-01
Humans have no problem segmenting different motion stimuli despite the ambiguity of local motion signals. Adaptive surround modulation, i.e., the apparent switching between integrative and antagonistic modes, is assumed to play a crucial role in this process. However, so far motion processing models based on local integration have not been able to provide a unifying explanation for this phenomenon. This motivated us to investigate the problem of local stimulus disambiguation in an alternative and fundamentally distinct motion-processing model which uses global motion filters for velocity computation. Local information is reconstructed at the end of the processing stream through the constructive interference of global signals, i.e., inverse transformations. We show that in this model local stimulus disambiguation can be achieved by means of a novel filter embedded in this architecture. This gives rise to both integrative and antagonistic effects which are in agreement with those observed in psychophysical experiments with humans, providing a functional explanation for effects of motion repulsion.
Adaptive Filter-bank Approach to Restoration and Spectral Analysis of Gapped Data
NASA Astrophysics Data System (ADS)
Stoica, Petre; Larsson, Erik G.; Li, Jian
2000-10-01
The main topic of this paper is the nonparametric estimation of complex (both amplitude and phase) spectra from gapped data, as well as the restoration of such data. The focus is on the extension of the APES (amplitude and phase estimation) approach to data sequences with gaps. APES, which is one of the most successful existing nonparametric approaches to the spectral analysis of full data sequences, uses a bank of narrowband adaptive (both frequency and data dependent) filters to estimate the spectrum. A recent interpretation of this approach showed that the filterbank used by APES and the resulting spectrum minimize a least-squares (LS) fitting criterion between the filtered sequence and its spectral decomposition. The extended approach, which is called GAPES for somewhat obvious reasons, capitalizes on the aforementioned interpretation: it minimizes the APES-LS fitting criterion with respect to the missing data as well. This should be a sensible thing to do whenever the full data sequence is stationary, and hence the missing data have the same spectral content as the available data. We use both simulated and real data examples to show that GAPES estimated spectra and interpolated data sequences have excellent accuracy. We also show the performance gain achieved by GAPES over two of the most commonly used approaches for gapped-data spectral analysis, viz., the periodogram and the parametric CLEAN method. This work was partly supported by the Swedish Foundation for Strategic Research.
A waveguide invariant adaptive matched filter for active sonar target depth classification.
Goldhahn, Ryan; Hickman, Granger; Krolik, Jeffrey
2011-04-01
This paper addresses depth discrimination of a water column target from bottom clutter discretes in wideband active sonar. To facilitate classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide invariant spectral density matrix (WI-SDM), which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is achieved using a waveguide invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) derived from signal covariance matrices averaged over the uncertain channel parameters. Simulation and real data results from the SCARAB98 and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach. Receiver operating characteristics (ROC) for robust waveguide invariant depth classification approaches are presented which illustrate performance under uncertain environmental conditions. PMID:21476638
Local stimulus disambiguation with global motion filters predicts adaptive surround modulation.
Dellen, Babette; Torras, Carme
2013-10-01
Humans have no problem segmenting different motion stimuli despite the ambiguity of local motion signals. Adaptive surround modulation, i.e., the apparent switching between integrative and antagonistic modes, is assumed to play a crucial role in this process. However, so far motion processing models based on local integration have not been able to provide a unifying explanation for this phenomenon. This motivated us to investigate the problem of local stimulus disambiguation in an alternative and fundamentally distinct motion-processing model which uses global motion filters for velocity computation. Local information is reconstructed at the end of the processing stream through the constructive interference of global signals, i.e., inverse transformations. We show that in this model local stimulus disambiguation can be achieved by means of a novel filter embedded in this architecture. This gives rise to both integrative and antagonistic effects which are in agreement with those observed in psychophysical experiments with humans, providing a functional explanation for effects of motion repulsion. PMID:23685285
A waveguide invariant adaptive matched filter for active sonar target depth classification.
Goldhahn, Ryan; Hickman, Granger; Krolik, Jeffrey
2011-04-01
This paper addresses depth discrimination of a water column target from bottom clutter discretes in wideband active sonar. To facilitate classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide invariant spectral density matrix (WI-SDM), which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is achieved using a waveguide invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) derived from signal covariance matrices averaged over the uncertain channel parameters. Simulation and real data results from the SCARAB98 and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach. Receiver operating characteristics (ROC) for robust waveguide invariant depth classification approaches are presented which illustrate performance under uncertain environmental conditions.
Shih, Cheng-Ting; Lin, Hsin-Hon; Chuang, Keh-Shih; Wu, Jay; Chang, Shu-Jun
2014-08-15
Purpose: Several positron emission tomography (PET) scanners with special detector block arrangements have been developed in recent years to improve the resolution of PET images. However, the discontinuous detector blocks cause gaps in the sinogram. This study proposes an adaptive discrete cosine transform-based (aDCT) filter for gap-inpainting. Methods: The gap-corrupted sinogram was morphologically closed and subsequently converted to the DCT domain. A certain number of the largest coefficients in the DCT spectrum were identified to determine the low-frequency preservation region. The weighting factors for the remaining coefficients were determined by an exponential weighting function. The aDCT filter was constructed and applied to two digital phantoms and a simulated phantom introduced with various levels of noise. Results: For the Shepp-Logan head phantom, the aDCT filter filled the gaps effectively. For the Jaszczak phantom, no secondary artifacts were induced after aDCT filtering. The percent mean square error and mean structure similarity of the aDCT filter were superior to those of the DCT2 filter at all noise levels. For the simulated striatal dopamine innervation study, the aDCT filter recovered the shape of the striatum and restored the striatum to reference activity ratios to the ideal value. Conclusions: The proposed aDCT filter can recover the missing gap data in the sinogram and improve the image quality and quantitative accuracy of PET images.
Lu, Jun; Xie, Kan; McFarland, Dennis J
2014-07-01
Movement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible.
Wang, Xin; Wu, Linhui; Yi, Xi; Zhang, Yanqi; Zhang, Limin; Zhao, Huijuan; Gao, Feng
2015-01-01
Due to both the physiological and morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic diffuse fluorescence tomography (DFT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. In this regime, the extended Kalman filtering (EKF) based method shows numerous advantages including accurate modeling, online estimation of multiparameters, and universal applicability to any optical fluorophore. Nevertheless the performance of the conventional EKF highly hinges on the exact and inaccessible prior knowledge about the initial values. To address the above issues, an adaptive-EKF scheme is proposed based on a two-compartmental model for the enhancement, which utilizes a variable forgetting-factor to compensate the inaccuracy of the initial states and emphasize the effect of the current data. It is demonstrated using two-dimensional simulative investigations on a circular domain that the proposed adaptive-EKF can obtain preferable estimation of the pharmacokinetic-rates to the conventional-EKF and the enhanced-EKF in terms of quantitativeness, noise robustness, and initialization independence. Further three-dimensional numerical experiments on a digital mouse model validate the efficacy of the method as applied in realistic biological systems. PMID:26089975
Emergence of band-pass filtering through adaptive spiking in the owl's cochlear nucleus
MacLeod, Katrina M.; Lubejko, Susan T.; Steinberg, Louisa J.; Köppl, Christine; Peña, Jose L.
2014-01-01
In the visual, auditory, and electrosensory modalities, stimuli are defined by first- and second-order attributes. The fast time-pressure signal of a sound, a first-order attribute, is important, for instance, in sound localization and pitch perception, while its slow amplitude-modulated envelope, a second-order attribute, can be used for sound recognition. Ascending the auditory pathway from ear to midbrain, neurons increasingly show a preference for the envelope and are most sensitive to particular envelope modulation frequencies, a tuning considered important for encoding sound identity. The level at which this tuning property emerges along the pathway varies across species, and the mechanism of how this occurs is a matter of debate. In this paper, we target the transition between auditory nerve fibers and the cochlear nucleus angularis (NA). While the owl's auditory nerve fibers simultaneously encode the fast and slow attributes of a sound, one synapse further, NA neurons encode the envelope more efficiently than the auditory nerve. Using in vivo and in vitro electrophysiology and computational analysis, we show that a single-cell mechanism inducing spike threshold adaptation can explain the difference in neural filtering between the two areas. We show that spike threshold adaptation can explain the increased selectivity to modulation frequency, as input level increases in NA. These results demonstrate that a spike generation nonlinearity can modulate the tuning to second-order stimulus features, without invoking network or synaptic mechanisms. PMID:24790170
Dong, Feng; Pierpaoli, Elena; Gunn, James E.; Wechsler, Risa H.
2007-10-29
We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is {approx} 85% complete and over 90% pure for clusters with masses above 1.0 x 10{sup 14}h{sup -1} M and redshifts up to z = 0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensity of {Delta} = 200, we find the derived cluster richness {Lambda}{sub 200} a roughly linear indicator of its virial mass M{sub 200}, which well recovers the relation between total luminosity and cluster mass of the input simulation.
Low-complexity nonlinear adaptive filter based on a pipelined bilinear recurrent neural network.
Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou
2011-09-01
To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.
Adaptive Control of Non-Minimum Phase Modal Systems Using Residual Mode Filters2. Parts 1 and 2
NASA Technical Reports Server (NTRS)
Balas, Mark J.; Frost, Susan
2011-01-01
Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. This paper will be divided into two parts. Here in Part I we will review the basic adaptive control approach and introduce the primary ideas. In Part II, we will present the RMF methodology and complete the proofs of all our results. Also, we will apply the above theoretical results to a simple flexible structure example to illustrate the behavior with and without the residual mode filter.
NASA Technical Reports Server (NTRS)
Toldalagi, P. M.
1980-01-01
A review is made of recursive statistical regression techniques incorporating past or past and future observations through smoothing and Kalman filtering, respectively; with results for the cases of the Tiros-N/MSU and Nimbus-6/Scams remote sensing satellite experiments. In response to the lack of a satisfactory model for the medium sounded, which is presently a major limitation on retrieval technique performance, a novel, global approach is proposed which casts the retrieval problem into the framework of adaptive filtering. A numerical implementation of such an adaptive system is presented, with a multilayer, semi-spectral general circulation model for the atmosphere being used to fine-tune the sensor as well as the dynamical equations of a Kalman filter. It is shown that the assimilation of radiometric data becomes a straightforward subproblem.
Longmire, M S; Milton, A F; Takken, E H
1982-11-01
Several 1-D signal processing techniques have been evaluated by simulation with a digital computer using high-spatial-resolution (0.15 mrad) noise data gathered from back-lit clouds and uniform sky with a scanning data collection system operating in the 4.0-4.8-microm spectral band. Two ordinary bandpass filters and a least-mean-square (LMS) spatial filter were evaluated in combination with a fixed or adaptive threshold algorithm. The combination of a 1-D LMS filter and a 1-D adaptive threshold sensor was shown to reject extreme cloud clutter effectively and to provide nearly equal signal detection in a clear and cluttered sky, at least in systems whose NEI (noise equivalent irradiance) exceeds 1.5 x 10(-13) W/cm(2) and whose spatial resolution is better than 0.15 x 0.36 mrad. A summary gives highlights of the work, key numerical results, and conclusions.
Conductivity image enhancement in MREIT using adaptively weighted spatial averaging filter
2014-01-01
Background In magnetic resonance electrical impedance tomography (MREIT), we reconstruct conductivity images using magnetic flux density data induced by externally injected currents. Since we extract magnetic flux density data from acquired MR phase images, the amount of measurement noise increases in regions of weak MR signals. Especially for local regions of MR signal void, there may occur excessive amounts of noise to deteriorate the quality of reconstructed conductivity images. In this paper, we propose a new conductivity image enhancement method as a postprocessing technique to improve the image quality. Methods Within a magnetic flux density image, the amount of noise varies depending on the position-dependent MR signal intensity. Using the MR magnitude image which is always available in MREIT, we estimate noise levels of measured magnetic flux density data in local regions. Based on the noise estimates, we adjust the window size and weights of a spatial averaging filter, which is applied to reconstructed conductivity images. Without relying on a partial differential equation, the new method is fast and can be easily implemented. Results Applying the novel conductivity image enhancement method to experimental data, we could improve the image quality to better distinguish local regions with different conductivity contrasts. From phantom experiments, the estimated conductivity values had 80% less variations inside regions of homogeneous objects. Reconstructed conductivity images from upper and lower abdominal regions of animals showed much less artifacts in local regions of weak MR signals. Conclusion We developed the fast and simple method to enhance the conductivity image quality by adaptively adjusting the weights and window size of the spatial averaging filter using MR magnitude images. Since the new method is implemented as a postprocessing step, we suggest adopting it without or with other preprocessing methods for application studies where conductivity
NASA Astrophysics Data System (ADS)
Kiani, Maryam; Pourtakdoust, Seid H.
2014-12-01
A novel algorithm is presented in this study for estimation of spacecraft's attitudes and angular rates from vector observations. In this regard, a new cubature-quadrature particle filter (CQPF) is initially developed that uses the Square-Root Cubature-Quadrature Kalman Filter (SR-CQKF) to generate the importance proposal distribution. The developed CQPF scheme avoids the basic limitation of particle filter (PF) with regards to counting the new measurements. Subsequently, CQPF is enhanced to adjust the sample size at every time step utilizing the idea of confidence intervals, thus improving the efficiency and accuracy of the newly proposed adaptive CQPF (ACQPF). In addition, application of the q-method for filter initialization has intensified the computation burden as well. The current study also applies ACQPF to the problem of attitude estimation of a low Earth orbit (LEO) satellite. For this purpose, the undertaken satellite is equipped with a three-axis magnetometer (TAM) as well as a sun sensor pack that provide noisy geomagnetic field data and Sun direction measurements, respectively. The results and performance of the proposed filter are investigated and compared with those of the extended Kalman filter (EKF) and the standard particle filter (PF) utilizing a Monte Carlo simulation. The comparison demonstrates the viability and the accuracy of the proposed nonlinear estimator.
NASA Astrophysics Data System (ADS)
Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar
2011-12-01
This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.
NASA Astrophysics Data System (ADS)
Rodríguez-Caballero, E.; Afana, A.; Chamizo, S.; Solé-Benet, A.; Canton, Y.
2016-07-01
Terrestrial laser scanning (TLS), widely known as light detection and ranging (LiDAR) technology, is increasingly used to provide highly detailed digital terrain models (DTM) with millimetric precision and accuracy. In order to generate a DTM, TLS data has to be filtered from undesired spurious objects, such as vegetation, artificial structures, etc., Early filtering techniques, successfully applied to airborne laser scanning (ALS), fail when applied to TLS data, as they heavily smooth the terrain surface and do not retain their real morphology. In this article, we present a new methodology for filtering TLS data based on the geometric and radiometric properties of the scanned surfaces. This methodology was built on previous morphological filters that select the minimum point height within a sliding window as the real surface. However, contrary to those methods, which use a fixed window size, the new methodology operates under different spatial scales represented by different window sizes, and can be adapted to different types and sizes of plants. This methodology has been applied to two study areas of differing vegetation type and density. The accuracy of the final DTMs was improved by ∼30% under dense canopy plants and over ∼40% on the open spaces between plants, where other methodologies drastically underestimated the real surface heights. This resulted in more accurate representation of the soil surface and microtopography than up-to-date techniques, eventually having strong implications in hydrological and geomorphological studies.
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.
A piezo-shunted kirigami auxetic lattice for adaptive elastic wave filtering
NASA Astrophysics Data System (ADS)
Ouisse, Morvan; Collet, Manuel; Scarpa, Fabrizio
2016-11-01
Tailoring the dynamical behavior of wave-guide structures can provide an efficient and physically elegant approach for optimizing mechanical components with regards to vibroacoustic propagation. Architectured materials as pyramidal core kirigami cells combined with smart systems may represent a promising way to improve the vibroacoustic quality of structural components. This paper describes the design and modeling of a pyramidal core with auxetic (negative Poisson’s ratio) characteristics and distributed shunted piezoelectric patches that allow for wave propagation control. The core is produced using a kirigami technique, inspired by the cutting/folding processes of the ancient Japanese art. The kirigami structure has a pyramidal unit cell shape that creates an in-plane negative Poisson’s ratio macroscopic behavior. This structure exhibits in-plane elastic properties (Young’s and shear modulus) which are higher than the out-of-plane ones, and hence this lattice has very specific properties in terms of wave propagation that are investigated in this work. The short-circuited configuration is first analyzed, before using negative capacitance and resistance as a shunt which provides impressive band gaps in the low frequency range. All configurations are investigated by using a full analysis of the Brillouin zone, rendering possible the deep understanding of the dynamical properties of the smart lattice. The results are presented in terms of dispersion and directivity diagrams, and the smart lattice shows quite interesting properties for the adaptive filtering of elastic waves at low frequencies bandwidths.
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006
Seismic random noise attenuation based on adaptive time-frequency peak filtering
NASA Astrophysics Data System (ADS)
Deng, Xinhuan; Ma, Haitao; Li, Yue; Zeng, Qian
2015-02-01
Time-frequency peak filtering (TFPF) method uses a specific window with fixed length to recover band-limited signal in stationary random noise. However, the derivatives of signal such as seismic wavelets may change rapidly in some short time intervals. In this case, TFPF equipped with fixed window length will not provide an optimal solution. In this letter, we present an adaptive version of TFPF for seismic random noise attenuation. In our version, the improved intersection of confidence intervals combined with short-time energy criterion is used to preprocess the noisy signal. And then, we choose an appropriate threshold to divide the noisy signal into signal, buffer and noise. Different optimal window lengths are used in each type of segments. We test the proposed method on both synthetic and field seismic data. The experimental results illustrate that the proposed method makes the degree of amplitude preservation raise more than 10% and signal-to-noise (SNR) improve 2-4 dB compared with the original algorithm.
NASA Astrophysics Data System (ADS)
Wells, Gregg B.; Ricci, Anthony J.
2011-11-01
In the auditory system, mechanotransduction occurs in the hair cell sensory hair bundle and is the first major step in the translation of mechanical energy into electrical. Tonotopic variations in the activation kinetics of this process are posited to provide a low pass filter to the input. An adaptation process, also associated with mechanotransduction, is postulated to provide a high pass filter to the input in a tonotopic manner. Together a bandpass filter is created at the hair cell input. Corresponding mechanical components to both activation and adaptation are also suggested to be involved in generating cochlear amplification. A paradox to this story is that hair cells where the mechanotransduction properties are most robust possess an intrinsic electrical resonance mechanism proposed to account for all required tuning and amplification. A simple Hodgkin-Huxley type model is presented to attempt to determine the role of the activation and adaptation kinetics in further tuning hair cells that exhibit electrical resonance. Results further support that steady state mechanotransduction properties are critical for setting the resting potential of the hair cell while the kinetics of activation and adaptation are important for sharpening tuning around the characteristic frequency of the hair cell.
Tankanag, Arina V; Chemeris, Nikolay K
2009-10-01
The paper describes an original method for analysis of the peripheral blood flow oscillations measured with the laser Doppler flowmetry (LDF) technique. The method is based on the continuous wavelet transform and adaptive wavelet theory and applies an adaptive wavelet filtering to the LDF data. The method developed allows one to examine the dynamics of amplitude oscillations in a wide frequency range (from 0.007 to 2 Hz) and to process both stationary and non-stationary short (6 min) signals. The capabilities of the method have been demonstrated by analyzing LDF signals registered in the state of rest and upon humeral occlusion. The paper shows the main advantage of the method proposed, which is the significant reduction of 'border effects', as compared to the traditional wavelet analysis. It was found that the low-frequency amplitudes obtained by adaptive wavelets are significantly higher than those obtained by non-adaptive ones. The method suggested would be useful for the analysis of low-frequency components of the short-living transitional processes under the conditions of functional tests. The method of adaptive wavelet filtering can be used to process stationary and non-stationary biomedical signals (cardiograms, encephalograms, myograms, etc), as well as signals studied in the other fields of science and engineering.
Edge Preserved Speckle Noise Reduction Using Integrated Fuzzy Filters.
Biradar, Nagashettappa; Dewal, M L; Rohit, Manoj Kumar
2014-01-01
Echocardiographic images are inherent with speckle noise which makes visual reading and analysis quite difficult. The multiplicative speckle noise masks finer details, necessary for diagnosis of abnormalities. A novel speckle reduction technique based on integration of geometric, wiener, and fuzzy filters is proposed and analyzed in this paper. The denoising applications of fuzzy filters are studied and analyzed along with 26 denoising techniques. It is observed that geometric filter retains noise and, to address this issue, wiener filter is embedded into the geometric filter during iteration process. The performance of geometric-wiener filter is further enhanced using fuzzy filters and the proposed despeckling techniques are called integrated fuzzy filters. Fuzzy filters based on moving average and median value are employed in the integrated fuzzy filters. The performances of integrated fuzzy filters are tested on echocardiographic images and synthetic images in terms of image quality metrics. It is observed that the performance parameters are highest in case of integrated fuzzy filters in comparison to fuzzy and geometric-fuzzy filters. The clinical validation reveals that the output images obtained using geometric-wiener, integrated fuzzy, nonlocal means, and details preserving anisotropic diffusion filters are acceptable. The necessary finer details are retained in the denoised echocardiographic images.
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-01-01
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved. PMID:27420062
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-01-01
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved. PMID:27420062
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-07-12
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.
Adaptation of filtered back-projection to compton imaging with non-uniform azimuthal geometry
NASA Astrophysics Data System (ADS)
Lee, Hyounggun; Lee, Taewoong; Lee, Wonho
2016-05-01
For Compton image reconstruction, analytic reconstruction methods such as filtered backprojection have been used for real-time imaging. The conventional filtered back-projection method assumes a uniformly distributed azimuthal response in the detector system. In this study, we applied filtered back-projection to the experimental data from detector systems with limited azimuthal angle coverage ranges and estimated the limitations of the analytic reconstruction methods when applied to these systems. For the system with a uniform azimuthal response, the images reconstructed by using filtered back-projection showed better angular resolutions than the images obtained by using simple back-projection did. However, when filtered back-projection was applied to reconstruct Compton images based on measurements performed by using Compton cameras with limited response geometries, the reconstructed images exhibited artifacts caused by the geometrical limitations. Our proposed method employs the Compton camera's rotation to overcome the angular response limitations; when the rotation method was applied in this study, the artifacts in the reconstructed images caused by angular response limitations were minimized. With this method, filtered back-projection can be applied to reconstruct real-time Compton images even when the radiation measurements are performed by using Compton cameras with non-uniform azimuthal response geometries.
Mihajlovic, Vojkan; Patki, Shrishail; Grundlehner, Bernard
2014-01-01
Designing and developing a comfortable and convenient EEG system for daily usage that can provide reliable and robust EEG signal, encompasses a number of challenges. Among them, the most ambitious is the reduction of artifacts due to body movements. This paper studies the effect of head movement artifacts on the EEG signal and on the dry electrode-tissue impedance (ETI), monitored continuously using the imec's wireless EEG headset. We have shown that motion artifacts have huge impact on the EEG spectral content in the frequency range lower than 20 Hz. Coherence and spectral analysis revealed that ETI is not capable of describing disturbances at very low frequencies (below 2 Hz). Therefore, we devised a motion artifact reduction (MAR) method that uses a combination of a band-pass filtering and multi-channel adaptive filtering (AF), suitable for real-time MAR. This method was capable of substantially reducing artifacts produced by head movements.
NASA Astrophysics Data System (ADS)
Yano, Ken'ichi; Ohara, Eiichi; Horihata, Satoshi; Aoki, Takaaki; Nishimoto, Yutaka
A robot that supports independent living by assisting with eating and other activities which use the operator's own hand would be helpful for people suffering from tremors of the hand or any other body part. The proposed system using adaptive filter estimates tremor frequencies with a time-varying property and individual differences online. In this study, the estimated frequency is used to adjusting the tremor suppression filter which insulates the voluntary motion signal from the sensor signal containing tremor components. These system are integrated into the control system of the Meal-Assist Robot. As a result, the developed system makes it possible for the person with a tremor to manipulate the supporting robot without causing operability to deteriorate and without hazards due to improper operation.
NASA Astrophysics Data System (ADS)
Fayadh, Rashid A.; Malek, F.; Fadhil, Hilal A.; Aldhaibani, Jaafar A.; Salman, M. K.; Abdullah, Farah Salwani
2015-05-01
For high data rate propagation in wireless ultra-wideband (UWB) communication systems, the inter-symbol interference (ISI), multiple-access interference (MAI), and multiple-users interference (MUI) are influencing the performance of the wireless systems. In this paper, the rake-receiver was presented with the spread signal by direct sequence spread spectrum (DS-SS) technique. The adaptive rake-receiver structure was shown with adjusting the receiver tap weights using least mean squares (LMS), normalized least mean squares (NLMS), and affine projection algorithms (APA) to support the weak signals by noise cancellation and mitigate the interferences. To minimize the data convergence speed and to reduce the computational complexity by the previous algorithms, a well-known approach of partial-updates (PU) adaptive filters were employed with algorithms, such as sequential-partial, periodic-partial, M-max-partial, and selective-partial updates (SPU) in the proposed system. The simulation results of bit error rate (BER) versus signal-to-noise ratio (SNR) are illustrated to show the performance of partial-update algorithms that have nearly comparable performance with the full update adaptive filters. Furthermore, the SPU-partial has closed performance to the full-NLMS and full-APA while the M-max-partial has closed performance to the full-LMS updates algorithms.
NASA Astrophysics Data System (ADS)
Aridgides, Tom; Libera, Peter; Fernandez, Manuel F.; Dobeck, Gerald J.
1996-05-01
An automatic, robust, adaptive clutter suppression, mine detection and classification processing string has been developed and applied to side-scan sonar imagery data. The overall processing string includes data pre-processing, adaptive clutter filtering (ACF), 2D normalization, detection, feature extraction, and classification processing blocks. The data pre-processing block contains automatic gain control and data decimation processing. The ACF technique designs a 2D adaptive range-crossrange linear FIR filter which is optimal in the Least Squares sense, simultaneously suppressing the background clutter while preserving an average peak target signature (normalized shape) computed a priori using training set data. A multiple reference ACF algorithm version was utilized to account for multiple target shapes (due to different mine types, multiple target aspect angles, etc.). The detection block consists of thresholding, clustering of exceedances and limiting their number, and a secondary thresholding process. Following feature extraction, the classification block applies a novel transformation to the data, which orthogonalizes the features and enables an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule. The utility of the overall processing string was demonstrated with two side-scan sonar data sets. The ACF/feature orthogonalization based LLRT mine classification processing string provided average probability of correct mine classification and false alarm rate performance similar to that obtained when utilizing an expert sonar operator.
NASA Astrophysics Data System (ADS)
Aridgides, Tom; Fernandez, Manuel F.; Dobeck, Gerald J.
1997-07-01
An automatic, robust, adaptive clutter suppression, predetection level fusion, sea mine detection and classification processing string has been developed and applied to shallow water side-scan sonar imagery data. The overall processing string includes pre-processing string includes pre-processing, adaptive clutter filtering (ACF), 2D normalization, detection, feature extraction and classification processing blocks. The pre-processing block contains automatic gain control, data decimation and data alignment processing. The ACF is a multi-dimensional adaptive linear FIR filter, optimal in the least squares sense, for simultaneous background clutter suppression and preservation of an average peak target signature. After data alignment, using a 3D ACF enables simultaneous multiple frequency data fusion and clutter suppression in the composite frequency-range-crossrange domain. Following 2D normalization, the detection consists of thresholding, clustering of exceedances and limiting their number. Finally, features are extracted and a orthogonalization transformation is applied to the data, enabling an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule. The utility of the overall processing string was demonstrated with two side-scan sonar data sets. The ACF, feature orthogonalization, LLRT-based classification processing string provided average probability of correct mine classification and false alarm rate performance exceeding the one obtained when utilizing an expert sonar operator. The overall processing string can be easily implemented in real-time using COTS technology.
NASA Astrophysics Data System (ADS)
Sheng-Hui, Rong; Hui-Xin, Zhou; Han-Lin, Qin; Rui, Lai; Kun, Qian
2016-05-01
Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.
Switching among pulse-generation regimes in passively mode-locked fibre laser by adaptive filtering
NASA Astrophysics Data System (ADS)
Peng, Junsong; Boscolo, Sonia
2016-04-01
We show both numerically and experimentally that dispersion management can be realized by manipulating the dispersion of a filter in a passively mode-locked fibre laser. A programmable filter the dispersion of which can be software configured is employed in the laser. Solitons, stretched-pulses, and dissipative solitons can be targeted reliably by controlling the filter transmission function only, while the length of fibres is fixed in the laser. This technique shows remarkable advantages in controlling operation regimes in ultrafast fibre lasers, in contrast to the traditional technique in which dispersion management is achieved by optimizing the relative length of fibres with opposite-sign dispersion. Our versatile ultrafast fibre laser will be attractive for applications requiring different pulse profiles such as in optical signal processing and optical communications.
Sun, W Y
1993-04-01
This thesis solves the problem of finding the optimal linear noise-reduction filter for linear tomographic image reconstruction. The optimization is data dependent and results in minimizing the mean-square error of the reconstructed image. The error is defined as the difference between the result and the best possible reconstruction. Applications for the optimal filter include reconstructions of positron emission tomographic (PET), X-ray computed tomographic, single-photon emission tomographic, and nuclear magnetic resonance imaging. Using high resolution PET as an example, the optimal filter is derived and presented for the convolution backprojection, Moore-Penrose pseudoinverse, and the natural-pixel basis set reconstruction methods. Simulations and experimental results are presented for the convolution backprojection method.
The Volterra-Wiener approach in neuronal modeling.
Mitsis, Georgios D
2011-01-01
Systems identification is being used increasingly in quantitative neurophysiology, including the auditory, visual and somatosensory systems. In this context, the Volterra-Wiener approach, which is an important branch of nonlinear systems identification, has met with considerable success in neuronal systems modeling, as these systems often exhibit complex nonlinear behavior. The Volterra-Wiener approach provides a comprehensive data-driven framework that does not place any a priori assumptions on the system structure. Therefore, it can approximate highly complex nonlinear mappings provided that experimental protocols are carefully designed in order to meet the requirements of the corresponding estimation procedure. In the present paper, we present a brief overview of Volterra-Wiener models and methodologies for their estimation as they relate to modeling neuronal systems. We also examine a specific example from a mechanoreceptor system. PMID:22255685
NASA Astrophysics Data System (ADS)
Yao, Jianjun; Di, Duotao; Jiang, Guilin; Gao, Shuang
2012-10-01
Electro-hydraulic servo shaking table usually requires good control performance for acceleration replication. The poles of the electro-hydraulic servo shaking table are placed by three-variable control method using pole placement theory. The system frequency band is thus extended and the system stability is also enhanced. The phase delay and amplitude attenuation phenomenon occurs in electro-hydraulic servo shaking table corresponding to an acceleration sinusoidal input. The method for phase delay and amplitude attenuation elimination based on LMS adaptive filtering algorithm is proposed here. The task is accomplished by adjusting the weights using LMS adaptive filtering algorithm when there exits phase delay and amplitude attenuation between the input and its corresponding acceleration response. The reference input is weighted in such a way that it makes the system output track the input efficiently. The weighted input signal is inputted to the control system such that the output phase delay and amplitude attenuation are all cancelled. The above concept is used as a basis for the development of amplitude-phase regulation (APR) algorithm. The method does not need to estimate the system model and has good real-time performance. Experimental results demonstrate the efficiency and validity of the proposed APR control scheme.
NASA Astrophysics Data System (ADS)
Tehsin, Sara; Rehman, Saad; Awan, Ahmad B.; Chaudry, Qaiser; Abbas, Muhammad; Young, Rupert; Asif, Afia
2016-04-01
Sensitivity to the variations in the reference image is a major concern when recognizing target objects. A combinational framework of correlation filters and logarithmic transformation has been previously reported to resolve this issue alongside catering for scale and rotation changes of the object in the presence of distortion and noise. In this paper, we have extended the work to include the influence of different logarithmic bases on the resultant correlation plane. The meaningful changes in correlation parameters along with contraction/expansion in the correlation plane peak have been identified under different scenarios. Based on our research, we propose some specific log bases to be used in logarithmically transformed correlation filters for achieving suitable tolerance to different variations. The study is based upon testing a range of logarithmic bases for different situations and finding an optimal logarithmic base for each particular set of distortions. Our results show improved correlation and target detection accuracies.
Comparison of various schema of filter adaptivity for the tracking of maneuvering targets
NASA Astrophysics Data System (ADS)
Jouan, Alexandre; Bosse, Eloi; Simard, Marc-Alain; Shahbazian, Elisa
1998-09-01
Tracking maneuvering targets is a complex problem which has generated a great deal of effort over the past several years. It has now been well established that in terms of tracking accuracy, the Interacting Multiple Model (IMM) algorithm, where state estimates are mixed, performs significantly better for maneuvering targets than other types of filters. However, the complexity of the IMM algorithm can prohibit its use in these applications of which similar algorithms cannot provide the necessary accuracy and which can ont afford the computational load of IMM algorithm. This paper presents the evaluation of the tracking accuracy of a multiple model track filter using three different constant-velocity models running in parallel and a maneuver detector. The output estimate is defined by selecting the model whose likelihood function is lower than a target maneuver threshold.
Adaptive multi-scale total variation minimization filter for low dose CT imaging
NASA Astrophysics Data System (ADS)
Zamyatin, Alexander; Katsevich, Gene; Krylov, Roman; Shi, Bibo; Yang, Zhi
2014-03-01
In this work we revisit TV filter and propose an improved version that is tailored to diagnostic CT purposes. We revise TV cost function, which results in symmetric gradient function that leads to more natural noise texture. We apply a multi-scale approach to resolve noise grain issue in CT images. We examine noise texture, granularity, and loss of low contrast in the test images. We also discuss potential acceleration by Nesterov and Conjugate Gradient methods.
NASA Astrophysics Data System (ADS)
Songer, Jocelyn E.; Eatock, Ruth Anne
2011-11-01
The mammalian saccule detects head tilt and low-frequency head accelerations as well as higher-frequency bone vibrations and sounds. It has two different hair cell types, I and II, dispersed throughout two morphologically distinct regions, the striola and extrastriola. Afferents from the two zones have distinct response dynamics which may arise partly from zonal differences in hair cell properties. We find that type II hair cells in the rat saccular epithelium adapt with a time course appropriate for influencing afferent responses to head motions. Moreover, striolar type II hair cells adapted by a greater extent than extrastriolar type II hair cells and had greater phase leads in the mid-frequency range (5-50 Hz). These differences suggest that hair cell transduction may contribute to zonal differences in the adaptation of vestibular afferents to head motions.
NASA Astrophysics Data System (ADS)
Wang, Xin; Wu, Linhui; Yi, Xi; Zhang, Limin; Gao, Feng; Zhao, Huijuan
2014-03-01
According to the morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic-rate images of fluorophore can provide diagnostic information for tumor differentiation, and especially have the potential for staging of tumors. In this paper, fluorescence diffuse optical tomography method is firstly used to acquire metabolism-related time-course images of the fluorophore concentration. Based on a two-compartment model comprised of plasma and extracelluar-extravascular space, we next propose an adaptive-EKF framework to estimate the pharmacokinetic-rate images. With the aid of a forgetting factor, the adaptive-EKF compensate the inaccuracy initial values and emphasize the effect of the current data in order to realize a better online estimation compared with the conventional EKF. We use simulate data to evaluate the performance of the proposed methodology. The results suggest that the adaptive-EKF can obtain preferable pharmacokinetic-rate images than the conventional EKF with higher quantitativeness and noise robustness.
Fluctuations and information filtering in coupled populations of spiking neurons with adaptation.
Deger, Moritz; Schwalger, Tilo; Naud, Richard; Gerstner, Wulfram
2014-12-01
Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.
Fluctuations and information filtering in coupled populations of spiking neurons with adaptation
NASA Astrophysics Data System (ADS)
Deger, Moritz; Schwalger, Tilo; Naud, Richard; Gerstner, Wulfram
2014-12-01
Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.
Adaptive Filter for Automatic Identification of Multiple Faults in a Noisy OTDR Profile
NASA Astrophysics Data System (ADS)
von der Weid, Jean Pierre; Souto, Mario H.; Garcia, Joaquim D.; Amaral, Gustavo C.
2016-07-01
We present a novel methodology able to distinguish meaningful level shifts from typical signal fluctuations. A two-stage regularization filtering can accurately identify the location of the significant level-shifts with an efficient parameter-free algorithm. The developed methodology demands low computational effort and can easily be embedded in a dedicated processing unit. Our case studies compare the new methodology with current available ones and show that it is the most adequate technique for fast detection of multiple unknown level-shifts in a noisy OTDR profile.
A tunable electrochromic fabry-perot filter for adaptive optics applications.
Blaich, Jonathan David; Kammler, Daniel R.; Ambrosini, Andrea; Sweatt, William C.; Verley, Jason C.; Heller, Edwin J.; Yelton, William Graham
2006-10-01
The potential for electrochromic (EC) materials to be incorporated into a Fabry-Perot (FP) filter to allow modest amounts of tuning was evaluated by both experimental methods and modeling. A combination of chemical vapor deposition (CVD), physical vapor deposition (PVD), and electrochemical methods was used to produce an ECFP film stack consisting of an EC WO{sub 3}/Ta{sub 2}O{sub 5}/NiO{sub x}H{sub y} film stack (with indium-tin-oxide electrodes) sandwiched between two Si{sub 3}N{sub 4}/SiO{sub 2} dielectric reflector stacks. A process to produce a NiO{sub x}H{sub y} charge storage layer that freed the EC stack from dependence on atmospheric humidity and allowed construction of this complex EC-FP stack was developed. The refractive index (n) and extinction coefficient (k) for each layer in the EC-FP film stack was measured between 300 and 1700 nm. A prototype EC-FP filter was produced that had a transmission at 500 nm of 36%, and a FWHM of 10 nm. A general modeling approach that takes into account the desired pass band location, pass band width, required transmission and EC optical constants in order to estimate the maximum tuning from an EC-FP filter was developed. Modeling shows that minor thickness changes in the prototype stack developed in this project should yield a filter with a transmission at 600 nm of 33% and a FWHM of 9.6 nm, which could be tuned to 598 nm with a FWHM of 12.1 nm and a transmission of 16%. Additional modeling shows that if the EC WO{sub 3} absorption centers were optimized, then a shift from 600 nm to 598 nm could be made with a FWHM of 11.3 nm and a transmission of 20%. If (at 600 nm) the FWHM is decreased to 1 nm and transmission maintained at a reasonable level (e.g. 30%), only fractions of a nm of tuning would be possible with the film stack considered in this study. These tradeoffs may improve at other wavelengths or with EC materials different than those considered here. Finally, based on our limited investigation and material set
Mie light-scattering granulometer with adaptive numerical filtering. I. Theory.
Hespel, L; Delfour, A
2000-12-20
A search procedure based on a least-squares method including a regularization scheme constructed from numerical filtering is presented. This method, with the addition of a nephelometer, can be used to determine the particle-size distributions of various scattering media (aerosols, fogs, rocket exhausts, motor plumes) from angular static light-scattering measurements. For retrieval of the distribution function, the experimental data are matched with theoretical patterns derived from Mie theory. The method is numerically investigated with simulated data, and the performance of the inverse procedure is evaluated. The results show that the retrieved distribution function is quite reliable, even for strong levels of noise.
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-01-01
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation. PMID:25985165
NASA Astrophysics Data System (ADS)
Neuhäuser, Markus; Krackow, Sven
2007-02-01
The neonatal incidence rate of Down syndrome (DS) is well-known to accelerate strongly with maternal age. This non-linearity renders mere accumulation of defects at recombination during prolonged first meiotic prophase implausible as an explanation for DS rate increase with maternal age, but might be anticipated from chromosomal drive (CD) for trisomy 21. Alternatively, as there is selection against genetically disadvantaged embryos, the screening system that eliminates embryos with trisomy 21 might decay with maternal age. In this paper, we provide the first evidence for relaxed filtering stringency (RFS) to represent an adaptive maternal response that could explain accelerating DS rates with maternal age. Using historical data, we show that the proportion of aberrant live births decrease with increased family size in older mothers, that inter-birth intervals are longer before affected neonates than before normal ones, and that primiparae exhibit elevated levels of DS incidence at higher age. These findings are predicted by adaptive RFS but cannot be explained by the currently available alternative non-adaptive hypotheses, including CD. The identification of the relaxation control mechanism and therapeutic restoration of a stringent screen may have considerable medical implications.
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-01-01
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation. PMID:25985165
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-05-13
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.
An Adaptive Particle Filtering Approach to Tracking Modes in a Varying Shallow Ocean Environment
Candy, J V
2011-03-22
The shallow ocean environment is ever changing mostly due to temperature variations in its upper layers (< 100m) directly affecting sound propagation throughout. The need to develop processors that are capable of tracking these changes implies a stochastic as well as an 'adaptive' design. The stochastic requirement follows directly from the multitude of variations created by uncertain parameters and noise. Some work has been accomplished in this area, but the stochastic nature was constrained to Gaussian uncertainties. It has been clear for a long time that this constraint was not particularly realistic leading a Bayesian approach that enables the representation of any uncertainty distribution. Sequential Bayesian techniques enable a class of processors capable of performing in an uncertain, nonstationary (varying statistics), non-Gaussian, variable shallow ocean. In this paper adaptive processors providing enhanced signals for acoustic hydrophonemeasurements on a vertical array as well as enhanced modal function estimates are developed. Synthetic data is provided to demonstrate that this approach is viable.
NASA Astrophysics Data System (ADS)
Flad, David; Beck, Andrea; Munz, Claus-Dieter
2016-05-01
Scale-resolving simulations of turbulent flows in complex domains demand accurate and efficient numerical schemes, as well as geometrical flexibility. For underresolved situations, the avoidance of aliasing errors is a strong demand for stability. For continuous and discontinuous Galerkin schemes, an effective way to prevent aliasing errors is to increase the quadrature precision of the projection operator to account for the non-linearity of the operands (polynomial dealiasing, overintegration). But this increases the computational costs extensively. In this work, we present a novel spatially and temporally adaptive dealiasing strategy by projection filtering. We show this to be more efficient for underresolved turbulence than the classical overintegration strategy. For this novel approach, we discuss the implementation strategy and the indicator details, show its accuracy and efficiency for a decaying homogeneous isotropic turbulence and the transitional Taylor-Green vortex and compare it to the original overintegration approach and a state of the art variational multi-scale eddy viscosity formulation.
NASA Astrophysics Data System (ADS)
Makowski, Ryszard; Zimroz, Radoslaw
2013-07-01
A procedure for feature extraction using adaptive Schur filter for damage detection in rolling element bearings is proposed in the paper. Damaged bearings produce impact signals (shocks) related with local change (loss) of stiffness in pairs: inner/outer race-rolling element. If significant disturbances do not occur (i.e. signal to noise ratio is sufficient), diagnostics is not very complicated and usually envelope analysis is used. Unfortunately, in most industrial examples, these impulsive contributions in vibration are completely masked by noise or other high energy sources. Moreover, impulses may have time varying amplitudes caused by transmission path, load and properties of noise changing in time. Thus, in order to extract time varying signal of interest, the solution would be an adaptive one. The proposed approach is based on the normalized exact least-square time-variant lattice filter (adaptive Schur filter). It is characterized by an extremely fast start-up performance, excellent convergence behavior, and fast parameter tracking capability, making this approach interesting. Schur adaptive filter consists of P sections, estimating, among others, time-varying reflection coefficients (RCs). In this paper it is proposed to use RCs and their derivatives as diagnostic features. However, it is not convenient to analyze simultaneously P signals for P sections, so instead of these, weighted sum of derivatives of RCs can be used. The key question is how to find these weight values for summation procedure. An original contributions are: application of Schur filter to bearings vibration processing, proposal of several features that can be used for detection and mentioned procedure of weighted summation of signal from sections of Schur filter. The method of signal processing is well-adapted for analysis of the non-stationary time-series, so it sounds very promising for diagnostics of machines working in time varying load/speed conditions.
Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images
Zhao, Haiying; Liu, Yong; Xie, Xiaojia; Liao, Yiyi; Liu, Xixi
2016-01-01
Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms. PMID:27399704
Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images.
Zhao, Haiying; Liu, Yong; Xie, Xiaojia; Liao, Yiyi; Liu, Xixi
2016-01-01
Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms. PMID:27399704
Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images.
Zhao, Haiying; Liu, Yong; Xie, Xiaojia; Liao, Yiyi; Liu, Xixi
2016-01-01
Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms.
Tsanas, Athanasios; Zañartu, Matías; Little, Max A.; Fox, Cynthia; Ramig, Lorraine O.; Clifford, Gari D.
2014-01-01
There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F0) of speech signals. This study examines ten F0 estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F0 in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F0 estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F0 estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F0 estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F0 estimation is required. PMID:24815269
Tsanas, Athanasios; Zañartu, Matías; Little, Max A; Fox, Cynthia; Ramig, Lorraine O; Clifford, Gari D
2014-05-01
There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F(0)) of speech signals. This study examines ten F(0) estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F(0) in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F(0) estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F(0) estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F(0) estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F(0) estimation is required. PMID:24815269
NASA Technical Reports Server (NTRS)
Starks, Scott; Abdel-Hafeez, Saleh; Usevitch, Bryan
1997-01-01
This paper discusses the implementation of a fuzzy logic system using an ASICs design approach. The approach is based upon combining the inherent advantages of symmetric triangular membership functions and fuzzy singleton sets to obtain a novel structure for fuzzy logic system application development. The resulting structure utilizes a fuzzy static RAM to store the rule-base and the end-points of the triangular membership functions. This provides advantages over other approaches in which all sampled values of membership functions for all universes must be stored. The fuzzy coprocessor structure implements the fuzzification and defuzzification processes through a two-stage parallel pipeline architecture which is capable of executing complex fuzzy computations in less than 0.55us with an accuracy of more than 95%, thus making it suitable for a wide range of applications. Using the approach presented in this paper, a fuzzy logic rule-base can be directly downloaded via a host processor to an onchip rule-base memory with a size of 64 words. The fuzzy coprocessor's design supports up to 49 rules for seven fuzzy membership functions associated with each of the chip's two input variables. This feature allows designers to create fuzzy logic systems without the need for additional on-board memory. Finally, the paper reports on simulation studies that were conducted for several adaptive filter applications using the least mean squared adaptive algorithm for adjusting the knowledge rule-base.
Background adaptive division filtering for hand-held ground penetrating radar
NASA Astrophysics Data System (ADS)
Lee, Matthew A.; Anderson, Derek T.; Ball, John E.; White, Julie L.
2016-05-01
The challenge in detecting explosive hazards is that there are multiple types of targets buried at different depths in a highlycluttered environment. A wide array of target and clutter signatures exist, which makes detection algorithm design difficult. Such explosive hazards are typically deployed in past and present war zones and they pose a grave threat to the safety of civilians and soldiers alike. This paper focuses on a new image enhancement technique for hand-held ground penetrating radar (GPR). Advantages of the proposed technique is it runs in real-time and it does not require the radar to remain at a constant distance from the ground. Herein, we evaluate the performance of the proposed technique using data collected from a U.S. Army test site, which includes targets with varying amounts of metal content, placement depths, clutter and times of day. Receiver operating characteristic (ROC) curve-based results are presented for the detection of shallow, medium and deeply buried targets. Preliminary results are very encouraging and they demonstrate the usefulness of the proposed filtering technique.
Xu, Yuan; Chen, Xiyuan; Li, Qinghua
2014-01-01
As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.
Chen, Xiyuan; Li, Qinghua
2014-01-01
As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF. PMID:24693225
Svenson, Björn; Larsson, Lars; Båth, Magnus
2016-01-01
Objective The purpose of the present study was to investigate the potential of using advanced external adaptive image processing for maintaining image quality while reducing exposure in dental panoramic storage phosphor plate (SPP) radiography. Materials and methods Thirty-seven SPP radiographs of a skull phantom were acquired using a Scanora panoramic X-ray machine with various tube load, tube voltage, SPP sensitivity and filtration settings. The radiographs were processed using General Operator Processor (GOP) technology. Fifteen dentists, all within the dental radiology field, compared the structural image quality of each radiograph with a reference image on a 5-point rating scale in a visual grading characteristics (VGC) study. The reference image was acquired with the acquisition parameters commonly used in daily operation (70 kVp, 150 mAs and sensitivity class 200) and processed using the standard process parameters supplied by the modality vendor. Results All GOP-processed images with similar (or higher) dose as the reference image resulted in higher image quality than the reference. All GOP-processed images with similar image quality as the reference image were acquired at a lower dose than the reference. This indicates that the external image processing improved the image quality compared with the standard processing. Regarding acquisition parameters, no strong dependency of the image quality on the radiation quality was seen and the image quality was mainly affected by the dose. Conclusions The present study indicates that advanced external adaptive image processing may be beneficial in panoramic radiography for increasing the image quality of SPP radiographs or for reducing the exposure while maintaining image quality. PMID:26478956
NASA Technical Reports Server (NTRS)
1974-01-01
Communications equipment for use with the Skylab project is examined to show compliance with contract requirements. The items of equipment considered are: (1) communications carrier assemblies, (2) filter bypass adapter assemblies, and (3) sub-assemblies, parts, and repairs. Additional information is provided concerning contract requirements, test requirements, and failure investigation actions.
Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals.
Dadashi, Farzin; Crettenand, Florent; Millet, Grégoire P; Seifert, Ludovic; Komar, John; Aminian, Kamiar
2013-01-01
This study introduces a novel approach for automatic temporal phase detection and inter-arm coordination estimation in front-crawl swimming using inertial measurement units (IMUs). We examined the validity of our method by comparison against a video-based system. Three waterproofed IMUs (composed of 3D accelerometer, 3D gyroscope) were placed on both forearms and the sacrum of the swimmer. We used two underwater video cameras in side and frontal views as our reference system. Two independent operators performed the video analysis. To test our methodology, seven well-trained swimmers performed three 300 m trials in a 50 m indoor pool. Each trial was in a different coordination mode quantified by the index of coordination. We detected different phases of the arm stroke by employing orientation estimation techniques and a new adaptive change detection algorithm on inertial signals. The difference of 0.2 ± 3.9% between our estimation and video-based system in assessment of the index of coordination was comparable to experienced operators' difference (1.1 ± 3.6%). The 95% limits of agreement of the difference between the two systems in estimation of the temporal phases were always less than 7.9% of the cycle duration. The inertial system offers an automatic easy-to-use system with timely feedback for the study of swimming.
Insect-Inspired Self-Motion Estimation with Dense Flow Fields—An Adaptive Matched Filter Approach
Strübbe, Simon; Stürzl, Wolfgang; Egelhaaf, Martin
2015-01-01
The control of self-motion is a basic, but complex task for both technical and biological systems. Various algorithms have been proposed that allow the estimation of self-motion from the optic flow on the eyes. We show that two apparently very different approaches to solve this task, one technically and one biologically inspired, can be transformed into each other under certain conditions. One estimator of self-motion is based on a matched filter approach; it has been developed to describe the function of motion sensitive cells in the fly brain. The other estimator, the Koenderink and van Doorn (KvD) algorithm, was derived analytically with a technical background. If the distances to the objects in the environment can be assumed to be known, the two estimators are linear and equivalent, but are expressed in different mathematical forms. However, for most situations it is unrealistic to assume that the distances are known. Therefore, the depth structure of the environment needs to be determined in parallel to the self-motion parameters and leads to a non-linear problem. It is shown that the standard least mean square approach that is used by the KvD algorithm leads to a biased estimator. We derive a modification of this algorithm in order to remove the bias and demonstrate its improved performance by means of numerical simulations. For self-motion estimation it is beneficial to have a spherical visual field, similar to many flying insects. We show that in this case the representation of the depth structure of the environment derived from the optic flow can be simplified. Based on this result, we develop an adaptive matched filter approach for systems with a nearly spherical visual field. Then only eight parameters about the environment have to be memorized and updated during self-motion. PMID:26308839
Insect-Inspired Self-Motion Estimation with Dense Flow Fields--An Adaptive Matched Filter Approach.
Strübbe, Simon; Stürzl, Wolfgang; Egelhaaf, Martin
2015-01-01
The control of self-motion is a basic, but complex task for both technical and biological systems. Various algorithms have been proposed that allow the estimation of self-motion from the optic flow on the eyes. We show that two apparently very different approaches to solve this task, one technically and one biologically inspired, can be transformed into each other under certain conditions. One estimator of self-motion is based on a matched filter approach; it has been developed to describe the function of motion sensitive cells in the fly brain. The other estimator, the Koenderink and van Doorn (KvD) algorithm, was derived analytically with a technical background. If the distances to the objects in the environment can be assumed to be known, the two estimators are linear and equivalent, but are expressed in different mathematical forms. However, for most situations it is unrealistic to assume that the distances are known. Therefore, the depth structure of the environment needs to be determined in parallel to the self-motion parameters and leads to a non-linear problem. It is shown that the standard least mean square approach that is used by the KvD algorithm leads to a biased estimator. We derive a modification of this algorithm in order to remove the bias and demonstrate its improved performance by means of numerical simulations. For self-motion estimation it is beneficial to have a spherical visual field, similar to many flying insects. We show that in this case the representation of the depth structure of the environment derived from the optic flow can be simplified. Based on this result, we develop an adaptive matched filter approach for systems with a nearly spherical visual field. Then only eight parameters about the environment have to be memorized and updated during self-motion.
Description of Wiener bounds of multicomponent composites by barycentric coordinates.
Peiponen, Kai-Erik; Gornov, Evgeny
2006-07-15
Wiener bounds for effective complex permittivity of multicomponent composites are treated by use of barycentric coordinates, a convex hull, and conformal mapping in a complex plane. Depending on the complexity of the multiphase system, the bounds provide singly or multiply connected regions that can be used in estimating the limits of the effective permittivity of the composite. The present modeling is important, e.g., in estimating spectral properties of nanocomposites in engineering and nanomedicine and in terahertz-based security imaging.
Description of Wiener bounds of multicomponent composites by barycentric coordinates
NASA Astrophysics Data System (ADS)
Peiponen, Kai-Erik; Gornov, Evgeny
2006-07-01
Wiener bounds for effective complex permittivity of multicomponent composites are treated by use of barycentric coordinates, a convex hull, and conformal mapping in a complex plane. Depending on the complexity of the multiphase system, the bounds provide singly or multiply connected regions that can be used in estimating the limits of the effective permittivity of the composite. The present modeling is important, e.g., in estimating spectral properties of nanocomposites in engineering and nanomedicine and in terahertz-based security imaging.
Barrett, Harrison H.; Furenlid, Lars R.; Freed, Melanie; Hesterman, Jacob Y.; Kupinski, Matthew A.; Clarkson, Eric; Whitaker, Meredith K.
2008-01-01
Adaptive imaging systems alter their data-acquisition configuration or protocol in response to the image information received. An adaptive pinhole single-photon emission computed tomography (SPECT) system might acquire an initial scout image to obtain preliminary information about the radiotracer distribution and then adjust the configuration or sizes of the pinholes, the magnifications, or the projection angles in order to improve performance. This paper briefly describes two small-animal SPECT systems that allow this flexibility and then presents a framework for evaluating adaptive systems in general, and adaptive SPECT systems in particular. The evaluation is in terms of the performance of linear observers on detection or estimation tasks. Expressions are derived for the ideal linear (Hotelling) observer and the ideal linear (Wiener) estimator with adaptive imaging. Detailed expressions for the performance figures of merit are given, and possible adaptation rules are discussed. PMID:18541485
A Field-Theoretic Approach to the Wiener Sausage
NASA Astrophysics Data System (ADS)
Nekovar, S.; Pruessner, G.
2016-05-01
The Wiener Sausage, the volume traced out by a sphere attached to a Brownian particle, is a classical problem in statistics and mathematical physics. Initially motivated by a range of field-theoretic, technical questions, we present a single loop renormalised perturbation theory of a stochastic process closely related to the Wiener Sausage, which, however, proves to be exact for the exponents and some amplitudes. The field-theoretic approach is particularly elegant and very enjoyable to see at work on such a classic problem. While we recover a number of known, classical results, the field-theoretic techniques deployed provide a particularly versatile framework, which allows easy calculation with different boundary conditions even of higher momenta and more complicated correlation functions. At the same time, we provide a highly instructive, non-trivial example for some of the technical particularities of the field-theoretic description of stochastic processes, such as excluded volume, lack of translational invariance and immobile particles. The aim of the present work is not to improve upon the well-established results for the Wiener Sausage, but to provide a field-theoretic approach to it, in order to gain a better understanding of the field-theoretic obstacles to overcome.
Two methods for identifying Wiener cascades having noninvertible static nonlinearities.
Korenberg, M J; Hunter, I W
1999-01-01
Two methods are proposed for identifying the component elements of a Wiener cascade that is comprised of a dynamic linear element (L) followed by a static nonlinearity (N). Both methods avoid potential problems of instability in a procedure presented by Paulin [M. G. Paulin, Biol. Cybern. 69: 67-76, 1993], which itself is a modification of a method described earlier by Hunter and Korenberg [I. W. Hunter and M. J. Korenberg, Biol. Cybern. 55: 135-144, 1996]. The latter method is a rapidly convergent iterative procedure that produces accurate estimates of the L and N elements from short data records, provided that the static nonlinearity N is invertible. Subsequently, Paulin introduced a modification that removed this limitation and enabled identification of Wiener cascades with nonmonotonic static nonlinearities. However, Paulin presented his modification employing an autoregressive moving average (ARMA) model representation for the dynamic linear element. To remove the possibility that the estimated ARMA model could be unstable, we recast the procedure by utilizing instead a rapid method for finding an impulse response representation for the dynamic linear element. However, in this form the procedure did not have good convergence properties, so we introduced two key ideas, both of which provide effective alternatives for identifying Wiener cascades whether or not the static nonlinearities therein are invertible. The new procedures are illustrated on challenging examples involving high-degree polynomial static nonlinearities, of odd or even symmetry, a high-pass linear element, and output noise corruption of 50%.
NASA Technical Reports Server (NTRS)
Venosa, Elettra; Vermeire, Bert; Alakija, Cameron; Harris, Fred; Strobel, David; Sheehe, Charles J.; Krunz, Marwan
2017-01-01
In the last few years, radio technologies for unmanned aircraft vehicle (UAV) have advanced very rapidly. The increasing need to fly unmanned aircraft systems (UAS) in the national airspace system (NAS) to perform missions of vital importance to national security, defense, and science has pushed ahead the design and implementation of new radio platforms. However, a lot still has to be done to improve those radios in terms of performance and capabilities. In addition, an important aspect to account for is hardware cost and the feasibility to implement these radios using commercial off-the-shelf (COTS) components. UAV radios come with numerous technical challenges and their development involves contributions at different levels of the design. Cognitive algorithms need to be developed in order to perform agile communications using appropriate frequency allocation while maintaining safe and efficient operations in the NAS and, digital reconfigurable architectures have to be designed in order to ensure a prompt response to environmental changes. Command and control (C2) communications have to be preserved during "standard" operations while crew operations have to be minimized. It is clear that UAV radios have to be software-defined systems, where size, weight and power consumption (SWaP) are critical parameters. This paper provides preliminary results of the efforts performed to design a fully digital radio architecture as part of a NASA Phase I STTR. In this paper, we will explain the basic idea and technical principles behind our dynamic/adaptive frequency hopping radio for UAVs. We will present our Simulink model of the dynamic FH radio transmitter design for UAV communications and show simulation results and FPGA system analysis.
Nikolic, Nina; Böcker, Reinhard; Kostic-Kravljanac, Ljiljana; Nikolic, Miroslav
2014-01-01
Questions Effects of soil on vegetation patterns are commonly obscured by other environmental factors; clear and general relationships are difficult to find. How would community assembly processes be affected by a substantial change in soil characteristics when all other relevant factors are held constant? In particular, can we identify some functional adaptations which would underpin such soil-induced vegetation response? Location Eastern Serbia: fields partially damaged by long-term and large-scale fluvial deposition of sulphidic waste from a Cu mine; subcontinental/submediterranean climate. Methods We analysed the multivariate response of cereal weed assemblages (including biomass and foliar analyses) to a strong man-made soil gradient (from highly calcareous to highly acidic, nutrient-poor soils) over short distances (field scale). Results The soil gradient favoured a substitution of calcicoles by calcifuges, and an increase in abundance of pseudometallophytes, with preferences for Atlantic climate, broad geographical distribution, hemicryptophytic life form, adapted to low-nutrient and acidic soils, with lower concentrations of Ca, and very narrow range of Cu concentrations in leaves. The trends of abundance of the different ecological groups of indicator species along the soil gradient were systematically reflected in the maintenance of leaf P concentrations, and strong homeostasis in biomass N:P ratio. Conclusion Using annual weed vegetation at the field scale as a fairly simple model, we demonstrated links between gradients in soil properties (pH, nutrient availability) and floristic composition that are normally encountered over large geographic distances. We showed that leaf nutrient status, in particular the maintenance of leaf P concentrations and strong homeostasis of biomass N:P ratio, underpinned a clear functional response of vegetation to mineral stress. These findings can help to understand assembly processes leading to unusual, novel combinations
Adaptive whitening of the electromyogram to improve amplitude estimation.
Clancy, E A; Farry, K A
2000-06-01
Previous research showed that whitening the surface electromyogram (EMG) can improve EMG amplitude estimation (where EMG amplitude is defined as the time-varying standard deviation of the EMG). However, conventional whitening via a linear filter seems to fail at low EMG amplitude levels, perhaps due to additive background noise in the measured EMG. This paper describes an adaptive whitening technique that overcomes this problem by cascading a nonadaptive whitening filter, an adaptive Wiener filter, and an adaptive gain correction. These stages can be calibrated from two, five second duration, constant-angle, constant-force contractions, one at a reference level [e.g., 50% maximum voluntary contraction (MVC)] and one at 0% MVC. In experimental studies, subjects used real-time EMG amplitude estimates to track a uniform-density, band-limited random target. With a 0.25-Hz bandwidth target, either adaptive whitening or multiple-channel processing reduced the tracking error roughly half-way to the error achieved using the dynamometer signal as the feedback. At the 1.00-Hz bandwidth, all of the EMG processors had errors equivalent to that of the dynamometer signal, reflecting that errors in this task were dominated by subjects' inability to track targets at this bandwidth. Increases in the additive noise level, smoothing window length, and tracking bandwidth diminish the advantages of whitening. PMID:10833845
Adaptive bilateral filter for image denoising and its application to in-vitro Time-of-Flight data
NASA Astrophysics Data System (ADS)
Seitel, Alexander; dos Santos, Thiago R.; Mersmann, Sven; Penne, Jochen; Groch, Anja; Yung, Kwong; Tetzlaff, Ralf; Meinzer, Hans-Peter; Maier-Hein, Lena
2011-03-01
Image-guided therapy systems generally require registration of pre-operative planning data with the patient's anatomy. One common approach to achieve this is to acquire intra-operative surface data and match it to surfaces extracted from the planning image. Although increasingly popular for surface generation in general, the novel Time-of-Flight (ToF) technology has not yet been applied in this context. This may be attributed to the fact that the ToF range images are subject to considerable noise. The contribution of this study is two-fold. Firstly, we present an adaption of the well-known bilateral filter for denoising ToF range images based on the noise characteristics of the camera. Secondly, we assess the quality of organ surfaces generated from ToF range data with and without bilateral smoothing using corresponding high resolution CT data as ground truth. According to an evaluation on five porcine organs, the root mean squared (RMS) distance between the denoised ToF data points and the reference computed tomography (CT) surfaces ranged from 3.0 mm (lung) to 9.0 mm (kidney). This corresponds to an error-reduction of up to 36% compared to the error of the original ToF surfaces.
Luo, Yong; Wu, Wenqi; Babu, Ravindra; Tang, Kanghua; Luo, Bing
2012-01-01
COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System). Since the ultra-tight GPS/INS (Inertial Navigation System) integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF), and INS's accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load. PMID:23012564
Luo, Yong; Wu, Wenqi; Babu, Ravindra; Tang, Kanghua; Luo, Bing
2012-01-01
COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System). Since the ultra-tight GPS/INS (Inertial Navigation System) integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF), and INS's accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load.
NASA Astrophysics Data System (ADS)
Hannequin, Pascal Paul
2015-06-01
Noise reduction in photon-counting images remains challenging, especially at low count levels. We have developed an original procedure which associates two complementary filters using a Wiener-derived approach. This approach combines two statistically adaptive filters into a dual-weighted (DW) filter. The first one, a statistically weighted adaptive (SWA) filter, replaces the central pixel of a sliding window with a statistically weighted sum of its neighbors. The second one, a statistical and heuristic noise extraction (extended) (SHINE-Ext) filter, performs a discrete cosine transformation (DCT) using sliding blocks. Each block is reconstructed using its significant components which are selected using tests derived from multiple linear regression (MLR). The two filters are weighted according to Wiener theory. This approach has been validated using a numerical phantom and a real planar Jaszczak phantom. It has also been illustrated using planar bone scintigraphy and myocardial single-photon emission computed tomography (SPECT) data. Performances of filters have been tested using mean normalized absolute error (MNAE) between the filtered images and the reference noiseless or high-count images. Results show that the proposed filters quantitatively decrease the MNAE in the images and then increase the signal-to-noise Ratio (SNR). This allows one to work with lower count images. The SHINE-Ext filter is well suited to high-size images and low-variance areas. DW filtering is efficient for low-size images and in high-variance areas. The relative proportion of eliminated noise generally decreases when count level increases. In practice, SHINE filtering alone is recommended when pixel spacing is less than one-quarter of the effective resolution of the system and/or the size of the objects of interest. It can also be used when the practical interest of high frequencies is low. In any case, DW filtering will be preferable. The proposed filters have been applied to nuclear
Whittemore, Stephen Richard
2013-09-10
Imaging systems include a detector and a spatial light modulator (SLM) that is coupled so as to control image intensity at the detector based on predetermined detector limits. By iteratively adjusting SLM element values, image intensity at one or all detector elements or portions of an imaging detector can be controlled to be within limits. The SLM can be secured to the detector at a spacing such that the SLM is effectively at an image focal plane. In some applications, the SLM can be adjusted to impart visible or hidden watermarks to images or to reduce image intensity at one or a selected set of detector elements so as to reduce detector blooming
NASA Technical Reports Server (NTRS)
Lai, Jonathan Y.
1994-01-01
This dissertation focuses on the signal processing problems associated with the detection of hazardous windshears using airborne Doppler radar when weak weather returns are in the presence of strong clutter returns. In light of the frequent inadequacy of spectral-processing oriented clutter suppression methods, we model a clutter signal as multiple sinusoids plus Gaussian noise, and propose adaptive filtering approaches that better capture the temporal characteristics of the signal process. This idea leads to two research topics in signal processing: (1) signal modeling and parameter estimation, and (2) adaptive filtering in this particular signal environment. A high-resolution, low SNR threshold maximum likelihood (ML) frequency estimation and signal modeling algorithm is devised and proves capable of delineating both the spectral and temporal nature of the clutter return. Furthermore, the Least Mean Square (LMS) -based adaptive filter's performance for the proposed signal model is investigated, and promising simulation results have testified to its potential for clutter rejection leading to more accurate estimation of windspeed thus obtaining a better assessment of the windshear hazard.
NASA Astrophysics Data System (ADS)
Wu, Shang-Teh; Lian, Sing-Han; Chen, Sheng-Han
2015-07-01
For a low-stiffness beam driven by a ball-screw stage, the lateral vibrations cannot be adequately controlled by a collocated compensator based on rotary-encoder feedback alone. Acceleration signals at the tip of the flexible beam are measured for active vibration control in addition to the collocated compensator. A second-order bandpass filter (a line enhancer) and two notch filters are included in the acceleration-feedback loop to raise modal dampings for the first and the second flexible modes without exciting higher-frequency resonances. A novel adaptation algorithm is devised to tune the center frequencies of the notch filters in real time. It consists of a second-order low-pass filter, a second-order bandpass filter and a phase detector. Improvement of the control system is elaborated progressively with the root-locus and bode-plot analyses, along with a physical interpretation. Extensive testings are conducted on an experimental device to verify the effectiveness of the control method.
Randriamparany, T; Kouakou, K V; Michaud, V; Fernández-Pinero, J; Gallardo, C; Le Potier, M-F; Rabenarivahiny, R; Couacy-Hymann, E; Raherimandimby, M; Albina, E
2016-08-01
The performance of Whatman 3-MM filter papers for the collection, drying, shipment and long-term storage of blood at ambient temperature, and for the detection of African swine fever virus and antibodies was assessed. Conventional and real-time PCR, viral isolation and antibody detection by ELISA were performed on paired samples (blood/tissue versus dried-blood 3-MM filter papers) collected from experimentally infected pigs and from farm pigs in Madagascar and Côte d'Ivoire. 3-MM filter papers were used directly in the conventional and real-time PCR without previous extraction of nucleic acids. Tests that performed better with 3-MM filter papers were in descending order: virus isolation, real-time UPL PCR and conventional PCR. The analytical sensitivity of real-time UPL PCR on filter papers was similar to conventional testing (virus isolation or conventional PCR) on organs or blood. In addition, blood-dried filter papers were tested in ELISA for antibody detection and the observed sensitivity was very close to conventional detection on serum samples and gave comparable results. Filter papers were stored up to 9 months at 20-25°C and for 2 months at 37°C without significant loss of sensitivity for virus genome detection. All tests on 3-MM filter papers had 100% specificity compared to the gold standards. Whatman 3-MM filter papers have the advantage of being cheap and of preserving virus viability for future virus isolation and characterization. In this study, Whatman 3-MM filter papers proved to be a suitable support for the collection, storage and use of blood in remote areas of tropical countries without the need for a cold chain and thus provide new possibilities for antibody testing and virus isolation.
Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping
2014-01-01
High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method. PMID:25479331
Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping
2014-01-01
High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method. PMID:25479331
Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping
2014-12-03
High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.
Croy, Ilona; Olgun, Selda; Mueller, Laura; Schmidt, Anna; Muench, Marcus; Hummel, Cornelia; Gisselmann, Guenter; Hatt, Hanns; Hummel, Thomas
2015-12-01
Selective processing of environmental stimuli improves processing capacity and allows adaptive modulation of behavior. The thalamus provides an effective filter of central sensory information processing. As olfactory projections, however, largely bypass the thalamus, other filter mechanisms must consequently have evolved for the sense of smell. We investigated whether specific anosmia - the inability to perceive a specific odor whereas detection of other substances is unaffected - represents an effective peripheral filter of olfactory information processing. In contrast to previous studies, we showed in a sample of 1600 normosmic subjects, that specific anosmia is by no means a rare phenomenon. Instead, while the affected odor is highly individual, the general probability of occurrence of specific anosmia is close to 1. In addition, 25 subjects performed daily olfactory training sessions with enhanced exposure to their particular "missing" smells for the duration of three months. This resulted in a significant improvement of sensitivity towards the respective specific odors. We propose specific anosmia to occur as a rule, rather than an exception, in the sense of smell. The lack of perception of certain odors may constitute a flexible peripheral filter mechanism, which can be altered by exposure.
An inverse problem for weighted Paley-Wiener spaces
NASA Astrophysics Data System (ADS)
Bessonov, R. V.; Romanov, R. V.
2016-11-01
Let μ be a measure on the real line {{R}} such that {\\int }{{R}}\\tfrac{{{d}}μ (t)}{1+{t}2}\\lt ∞ and let a\\gt 0. Assume that the norms \\parallel f{\\parallel }{L2({{R}})} and \\parallel f{\\parallel }{L2(μ )} are comparable for functions f in the Paley-Wiener space {{PW}}a and that {{PW}}a is dense in {L}2(μ ). We reconstruct the canonical Hamiltonian system {JX}\\prime =z{ H }X such that μ is the spectral measure for this system. The work is supported by Russian Science Foundation Grant 14-21-00035 (theorem 1) and by Russian Science Foundation Grant 14-41-00010 (theorem 2).
An Extension of SIC Predictions to the Wiener Coactive Model.
Houpt, Joseph W; Townsend, James T
2011-06-01
The survivor interaction contrasts (SIC) is a powerful measure for distinguishing among candidate models of human information processing. One class of models to which SIC analysis can apply are the coactive, or channel summation, models of human information processing. In general, parametric forms of coactive models assume that responses are made based on the first passage time across a fixed threshold of a sum of stochastic processes. Previous work has shown that that the SIC for a coactive model based on the sum of Poisson processes has a distinctive down-up-down form, with an early negative region that is smaller than the later positive region. In this note, we demonstrate that a coactive process based on the sum of two Wiener processes has the same SIC form.
Baldovin-Stella stochastic volatility process and Wiener process mixtures
NASA Astrophysics Data System (ADS)
Peirano, P. P.; Challet, D.
2012-08-01
Starting from inhomogeneous time scaling and linear decorrelation between successive price returns, Baldovin and Stella recently proposed a powerful and consistent way to build a model describing the time evolution of a financial index. We first make it fully explicit by using Student distributions instead of power law-truncated Lévy distributions and show that the analytic tractability of the model extends to the larger class of symmetric generalized hyperbolic distributions and provide a full computation of their multivariate characteristic functions; more generally, we show that the stochastic processes arising in this framework are representable as mixtures of Wiener processes. The basic Baldovin and Stella model, while mimicking well volatility relaxation phenomena such as the Omori law, fails to reproduce other stylized facts such as the leverage effect or some time reversal asymmetries. We discuss how to modify the dynamics of this process in order to reproduce real data more accurately.
Tian, Ya; Wei, Hongxing; Tan, Jindong
2013-03-01
High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. In order to achieve real-time ambulatory human motion tracking with low-cost MARG (magnetic, angular rate, and gravity) sensors, a computationally efficient and robust algorithm for orientation estimation is critical. This paper presents an analytically derived method for an adaptive-gain complementary filter based on the convergence rate from the Gauss-Newton optimization algorithm (GNA) and the divergence rate from the gyroscope, which is referred as adaptive-gain orientation filter (AGOF) in this paper. The AGOF has the advantages of one iteration calculation to reduce the computing load and accurate estimation of gyroscope measurement error. Moreover, for handling magnetic distortions especially in indoor environments and movements with excessive acceleration, adaptive measurement vectors and a reference vector for earth's magnetic field selection schemes are introduced to help the GNA find more accurate direction of gyroscope error. The features of this approach include the accurate estimation of the gyroscope bias to correct the instantaneous gyroscope measurements and robust estimation in conditions of fast motions and magnetic distortions. Experimental results are presented to verify the performance of the proposed method, which shows better accuracy of orientation estimation than several well-known methods.
Tian, Ya; Tan, Jindong
2012-01-01
High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. This paper presents an analytically derived method for an adaptive-gain complementary filter based on the convergence rate from the Gauss-Newton optimization algorithm (GNA) and the divergence rate from the gyroscope, which is referred as Adaptive-Gain Orientation Filter (AGOF) in this paper. The AGOF has the advantages of one iteration calculation to reduce the computing load and accurate estimation of gyroscope measurement error. Moreover, for handling magnetic distortions especially in indoor environments and movements with excessive acceleration, adaptive measurement vectors and a reference vector for Earth's magnetic field selection schemes are introduced to help the GNA find more accurate direction of gyroscope error. Experimental results are presented to verify the performance of the proposed method, which shows better accuracy of orientation estimation than several well-known methods.
On representation of fracture profiles by fractional integrals of a Wiener process
NASA Technical Reports Server (NTRS)
Kunin, B.; Gorelik, M.
1991-01-01
An enhancement of the crack diffusion model of brittle fracture is proposed. In the model, certain probabilities are evaluated as averages over the set of all virtual crack paths, and the latter are modeled as realization of a Wiener process. However, commonly observed fracture profiles are smoother. In fact, one can show that their fractal dimension is smaller than the value 3/2 for a Wiener process. Accordingly, it is proposed to represent fracture profiles by graphs of fractional integrals of a Wiener process. This allows one to employ additional fractographic information obtained from fracture profiles, e.g. their fractal dimension, in a predictive formalism, evaluating probabilities of failure.
Applications of Savitzky-Golay Filter for Seismic Random Noise Reduction
NASA Astrophysics Data System (ADS)
Liu, Yanping; Dang, Bo; Li, Yue; Lin, Hongbo; Ma, Haitao
2016-02-01
This article utilizes Savitzky-Golay (SG) filter to eliminate seismic random noise. This is a novel method for seismic random noise reduction in which SG filter adopts piecewise weighted polynomial via least-squares estimation. Therefore, effective smoothing is achieved in extracting the original signal from noise environment while retaining the shape of the signal as close as possible to the original one. Although there are lots of classical methods such as Wiener filtering and wavelet denoising applied to eliminate seismic random noise, the SG filter outperforms them in approximating the true signal. SG filter will obtain a good tradeoff in waveform smoothing and valid signal preservation under suitable conditions. These are the appropriate window size and the polynomial degree. Through examples from synthetic seismic signals and field seismic data, we demonstrate the good performance of SG filter by comparing it with the Wiener filtering and wavelet denoising methods.
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.
NASA Astrophysics Data System (ADS)
Bargatze, L. F.
2015-12-01
Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted
NASA Technical Reports Server (NTRS)
Miamee, A. G.
1988-01-01
It is shown that the algorithms for determining the generating function and prediction error matrix of multivariate stationary stochastic processes developed by Wiener and Masani (1957), and later by Masani (1960) will work in some more general setting.
Wiener Chaos expansions and numerical solutions of randomly forced equations of fluid mechanics
Hou, Thomas Y. . E-mail: hou@ama.caltech.edu; Luo Wuan; Rozovskii, Boris; Zhou Haomin
2006-08-10
In this paper, we propose a numerical method based on Wiener Chaos expansion and apply it to solve the stochastic Burgers and Navier-Stokes equations driven by Brownian motion. The main advantage of the Wiener Chaos approach is that it allows for the separation of random and deterministic effects in a rigorous and effective manner. The separation principle effectively reduces a stochastic equation to its associated propagator, a system of deterministic equations for the coefficients of the Wiener Chaos expansion. Simple formulas for statistical moments of the stochastic solution are presented. These formulas only involve the solutions of the propagator. We demonstrate that for short time solutions the numerical methods based on the Wiener Chaos expansion are more efficient and accurate than those based on the Monte Carlo simulations.
Laplace asymptotic expansions of conditional Wiener integrals and generalized Mehler kernel formulas
NASA Astrophysics Data System (ADS)
Davies, Ian; Truman, Aubrey
1982-11-01
Imitating Schilder's results for Wiener integrals rigorous Laplace asymptotic expansions are proven for conditional Wiener integrals. Applications are given for deriving generalized Mehler kernel formulas, up to arbitrarily high orders in powers of ℏ, for exp{-TH(ℏ)/ℏ}(x, y), T>0 where H(ℏ)=[(-ℏ2/2)Δ1+V], Δ1 being the one-dimensional Laplacian, V being a real-valued potential V∈C∞(R), bounded below, together with its second derivative.
Wiener filtering of successive overlapping sections of an X-ray reconstruction
NASA Technical Reports Server (NTRS)
Reed, I. S.; Glenn, W. V., Jr.; Kwoh, Y. S.; Chang, C. M.; Truong, T. K.
1980-01-01
In this paper, a technique is developed to improve the image quality of an object in three-dimensional reconstruction by a weighted average of successive overlapping two-dimensional sections of the object. It is demonstrated that the signal-to-noise ratio of a two-dimensional picture can be improved by roughly a factor of 2 for typical X-ray beam shapes.
Grace Signal Filtering as a Means of Determining Equivalent Water Thickness in Poland
NASA Astrophysics Data System (ADS)
Biryło, Monika; Nastula, Jolanta
2012-01-01
In the paper an Equivalent Water Thickness (EWT) determination as a way of observing gravity variations is described. Since raw data acquired directly from Gravity Recovery and Climate Experiment - GRACE satellites is unsuitable for analysis due to stripes occurrence, a filtering algorithm must be used. In this paper, authors are testing two isotropic (Gauss, CNES/GRGS) filters and two anisotropic filters (Wiener- -Kolomogorov, ANS). Correlation, amplitude ratio, and modification were determined as well as maps were generated.
Remotely serviced filter and housing
Ross, Maurice J.; Zaladonis, Larry A.
1988-09-27
A filter system for a hot cell comprises a housing adapted for input of air or other gas to be filtered, flow of the air through a filter element, and exit of filtered air. The housing is tapered at the top to make it easy to insert a filter cartridge using an overhead crane. The filter cartridge holds the filter element while the air or other gas is passed through the filter element. Captive bolts in trunnion nuts are readily operated by electromechanical manipulators operating power wrenches to secure and release the filter cartridge. The filter cartridge is adapted to make it easy to change a filter element by using a master-slave manipulator at a shielded window station.
NASA Astrophysics Data System (ADS)
Kwon, Hyeokjun; Oh, Sechang; Varadan, Vijay K.
2012-04-01
, a filter including a moving weighted factor, peak to peak detection, and interpolation techniques. In addition, this paper introduces an adaptive filter in order to extract clear ECG signal by using extracted baseline noise signal and measured signal from sensor.
The discrete Kalman filtering approach for seismic signals deconvolution
Kurniadi, Rizal; Nurhandoko, Bagus Endar B.
2012-06-20
Seismic signals are a convolution of reflectivity and seismic wavelet. One of the most important stages in seismic data processing is deconvolution process; the process of deconvolution is inverse filters based on Wiener filter theory. This theory is limited by certain modelling assumptions, which may not always valid. The discrete form of the Kalman filter is then used to generate an estimate of the reflectivity function. The main advantage of Kalman filtering is capability of technique to handling continually time varying models and has high resolution capabilities. In this work, we use discrete Kalman filter that it was combined with primitive deconvolution. Filtering process works on reflectivity function, hence the work flow of filtering is started with primitive deconvolution using inverse of wavelet. The seismic signals then are obtained by convoluting of filtered reflectivity function with energy waveform which is referred to as the seismic wavelet. The higher frequency of wavelet gives smaller wave length, the graphs of these results are presented.
Remotely serviced filter and housing
Ross, M.J.; Zaladonis, L.A.
1987-07-22
A filter system for a hot cell comprises a housing adapted for input of air or other gas to be filtered, flow of the air through a filter element, and exit of filtered air. The housing is tapered at the top to make it easy to insert a filter cartridge holds the filter element while the air or other gas is passed through the filter element. Captive bolts in trunnion nuts are readily operated by electromechanical manipulators operating power wrenches to secure and release the filter cartridge. The filter cartridge is adapted to make it easy to change a filter element by using a master-slave manipulator at a shielded window station. 6 figs.
On the Extremal Wiener Polarity Index of Hückel Graphs
Wang, Hongzhuan
2016-01-01
Graphs are used to model chemical compounds and drugs. In the graphs, each vertex represents an atom of molecule and edges between the corresponding vertices are used to represent covalent bounds between atoms. The Wiener polarity index Wp(G) of a graph G is the number of unordered pairs of vertices u, v of G such that the distance between u and v is equal to 3. The trees and unicyclic graphs with perfect matching, of which all vertices have degrees not greater than three, are referred to as the Hückel trees and unicyclic Hückel graphs, respectively. In this paper, we first consider the smallest and the largest Wiener polarity index among all Hückel trees on 2n vertices and characterize the corresponding extremal graphs. Then we obtain an upper and lower bound for the Wiener polarity index of unicyclic Hückel graphs on 2n vertices. PMID:27247613
Guon, Jerold
1976-04-13
A sintered filter trap is adapted for insertion in a gas stream of sodium vapor to condense and deposit sodium thereon. The filter is heated and operated above the melting temperature of sodium, resulting in a more efficient means to remove sodium particulates from the effluent inert gas emanating from the surface of a liquid sodium pool. Preferably the filter leaves are precoated with a natrophobic coating such as tetracosane.
Ffrench, P A; Zeidler, J H; Ku, W H
1997-01-01
Two-dimensional (2-D) adaptive filtering is a technique that can be applied to many image processing applications. This paper will focus on the development of an improved 2-D adaptive lattice algorithm (2-D AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images. The two improvements proposed here are increased flexibility in the calculation of the reflection coefficients and a 2-D method to update the correlations used in the 2-D AL algorithm. The 2-D AL algorithm is shown to predict correlated clutter in image data and the resulting filter is compared with an ideal Wiener-Hopf filter. The results of the clutter removal will be compared to previously published ones for a 2-D least mean square (LMS) algorithm. 2-D AL is better able to predict spatially varying clutter than the 2-D LMS algorithm, since it converges faster to new image properties. Examples of these improvements are shown for a spatially varying 2-D sinusoid in white noise and simulated clouds. The 2-D LMS and 2-D AL algorithms are also shown to enhance a mammogram image for the detection of small microcalcifications and stellate lesions.
NASA Technical Reports Server (NTRS)
Miamee, A. G.
1988-01-01
The algorithms developed by Wiener and Masani (1957 and 1958) and Masani (1960) for the characterization of a class of multivariate stationary stochastic processes are investigated analytically. The algorithms permit the determination of (1) the generating function, (2) the prediction-error matrix, and (3) an autoregressive representation of the linear least-squares predictor. A number of theorems and lemmas are proved, and it is shown that the range of validity of the algorithms can be extended significantly beyond that given by Wiener and Masani.
Nonlinear Attitude Filtering Methods
NASA Technical Reports Server (NTRS)
Markley, F. Landis; Crassidis, John L.; Cheng, Yang
2005-01-01
This paper provides a survey of modern nonlinear filtering methods for attitude estimation. Early applications relied mostly on the extended Kalman filter for attitude estimation. Since these applications, several new approaches have been developed that have proven to be superior to the extended Kalman filter. Several of these approaches maintain the basic structure of the extended Kalman filter, but employ various modifications in order to provide better convergence or improve other performance characteristics. Examples of such approaches include: filter QUEST, extended QUEST, the super-iterated extended Kalman filter, the interlaced extended Kalman filter, and the second-order Kalman filter. Filters that propagate and update a discrete set of sigma points rather than using linearized equations for the mean and covariance are also reviewed. A two-step approach is discussed with a first-step state that linearizes the measurement model and an iterative second step to recover the desired attitude states. These approaches are all based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance. Other approaches that do not require this assumption are reviewed, including particle filters and a Bayesian filter based on a non-Gaussian, finite-parameter probability density function on SO(3). Finally, the predictive filter, nonlinear observers and adaptive approaches are shown. The strengths and weaknesses of the various approaches are discussed.
NASA Astrophysics Data System (ADS)
Kamran, M. Ahmad; Hong, Keum-Shik
2013-10-01
Objective. Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique that measures brain activities by using near-infrared light of 650-950 nm wavelength. The major advantages of fNIRS are its low cost, portability, and good temporal resolution as a plausible solution to real-time imaging. Recent research has shown the great potential of fNIRS as a tool for brain-computer interfaces. Approach. This paper presents the first novel technique for fNIRS-based modelling of brain activities using the linear parameter-varying (LPV) method and adaptive signal processing. The output signal of each channel is assumed to be an output of an LPV system with unknown coefficients that are optimally estimated by the affine projection algorithm. The parameter vector is assumed to be Gaussian. Main results. The general linear model (GLM) is very popular and is a commonly used method for the analysis of functional MRI data, but it has certain limitations in the case of optical signals. The proposed model is more efficient in the sense that it allows the user to define more states. Moreover, unlike most previous models, it is online. The present results, showing improvement, were verified by random finger-tapping tasks in extensive experiments. We used 24 states, which can be reduced or increased depending on the cost of computation and requirements. Significance. The t-statistics were employed to determine the activation maps and to verify the significance of the results. Comparison of the proposed technique and two existing GLM-based algorithms shows an improvement in the estimation of haemodynamic response. Additionally, the convergence of the proposed algorithm is shown by error reduction in consecutive iterations.
Han, Houzeng; Xu, Tianhe; Wang, Jian
2016-01-01
Precise Point Positioning (PPP) makes use of the undifferenced pseudorange and carrier phase measurements with ionospheric-free (IF) combinations to achieve centimeter-level positioning accuracy. Conventionally, the IF ambiguities are estimated as float values. To improve the PPP positioning accuracy and shorten the convergence time, the integer phase clock model with between-satellites single-difference (BSSD) operation is used to recover the integer property. However, the continuity and availability of stand-alone PPP is largely restricted by the observation environment. The positioning performance will be significantly degraded when GPS operates under challenging environments, if less than five satellites are present. A commonly used approach is integrating a low cost inertial sensor to improve the positioning performance and robustness. In this study, a tightly coupled (TC) algorithm is implemented by integrating PPP with inertial navigation system (INS) using an Extended Kalman filter (EKF). The navigation states, inertial sensor errors and GPS error states are estimated together. The troposphere constrained approach, which utilizes external tropospheric delay as virtual observation, is applied to further improve the ambiguity-fixed height positioning accuracy, and an improved adaptive filtering strategy is implemented to improve the covariance modelling considering the realistic noise effect. A field vehicular test with a geodetic GPS receiver and a low cost inertial sensor was conducted to validate the improvement on positioning performance with the proposed approach. The results show that the positioning accuracy has been improved with inertial aiding. Centimeter-level positioning accuracy is achievable during the test, and the PPP/INS TC integration achieves a fast re-convergence after signal outages. For troposphere constrained solutions, a significant improvement for the height component has been obtained. The overall positioning accuracies of the height
Han, Houzeng; Xu, Tianhe; Wang, Jian
2016-01-01
Precise Point Positioning (PPP) makes use of the undifferenced pseudorange and carrier phase measurements with ionospheric-free (IF) combinations to achieve centimeter-level positioning accuracy. Conventionally, the IF ambiguities are estimated as float values. To improve the PPP positioning accuracy and shorten the convergence time, the integer phase clock model with between-satellites single-difference (BSSD) operation is used to recover the integer property. However, the continuity and availability of stand-alone PPP is largely restricted by the observation environment. The positioning performance will be significantly degraded when GPS operates under challenging environments, if less than five satellites are present. A commonly used approach is integrating a low cost inertial sensor to improve the positioning performance and robustness. In this study, a tightly coupled (TC) algorithm is implemented by integrating PPP with inertial navigation system (INS) using an Extended Kalman filter (EKF). The navigation states, inertial sensor errors and GPS error states are estimated together. The troposphere constrained approach, which utilizes external tropospheric delay as virtual observation, is applied to further improve the ambiguity-fixed height positioning accuracy, and an improved adaptive filtering strategy is implemented to improve the covariance modelling considering the realistic noise effect. A field vehicular test with a geodetic GPS receiver and a low cost inertial sensor was conducted to validate the improvement on positioning performance with the proposed approach. The results show that the positioning accuracy has been improved with inertial aiding. Centimeter-level positioning accuracy is achievable during the test, and the PPP/INS TC integration achieves a fast re-convergence after signal outages. For troposphere constrained solutions, a significant improvement for the height component has been obtained. The overall positioning accuracies of the height
Han, Houzeng; Xu, Tianhe; Wang, Jian
2016-07-08
Precise Point Positioning (PPP) makes use of the undifferenced pseudorange and carrier phase measurements with ionospheric-free (IF) combinations to achieve centimeter-level positioning accuracy. Conventionally, the IF ambiguities are estimated as float values. To improve the PPP positioning accuracy and shorten the convergence time, the integer phase clock model with between-satellites single-difference (BSSD) operation is used to recover the integer property. However, the continuity and availability of stand-alone PPP is largely restricted by the observation environment. The positioning performance will be significantly degraded when GPS operates under challenging environments, if less than five satellites are present. A commonly used approach is integrating a low cost inertial sensor to improve the positioning performance and robustness. In this study, a tightly coupled (TC) algorithm is implemented by integrating PPP with inertial navigation system (INS) using an Extended Kalman filter (EKF). The navigation states, inertial sensor errors and GPS error states are estimated together. The troposphere constrained approach, which utilizes external tropospheric delay as virtual observation, is applied to further improve the ambiguity-fixed height positioning accuracy, and an improved adaptive filtering strategy is implemented to improve the covariance modelling considering the realistic noise effect. A field vehicular test with a geodetic GPS receiver and a low cost inertial sensor was conducted to validate the improvement on positioning performance with the proposed approach. The results show that the positioning accuracy has been improved with inertial aiding. Centimeter-level positioning accuracy is achievable during the test, and the PPP/INS TC integration achieves a fast re-convergence after signal outages. For troposphere constrained solutions, a significant improvement for the height component has been obtained. The overall positioning accuracies of the height
NASA Astrophysics Data System (ADS)
Amian, M.; Setarehdan, S. Kamaledin; Yousefi, H.
2014-09-01
Functional Near infrared spectroscopy (fNIRS) is a newly noninvasive way to measure oxy hemoglobin and deoxy hemoglobin concentration changes of human brain. Relatively safe and affordable than other functional imaging techniques such as fMRI, it is widely used for some special applications such as infant examinations and pilot's brain monitoring. In such applications, fNIRS data sometimes suffer from undesirable movements of subject's head which called motion artifact and lead to a signal corruption. Motion artifact in fNIRS data may result in fallacy of concluding or diagnosis. In this work we try to reduce these artifacts by a novel Kalman filtering algorithm that is based on an autoregressive moving average (ARMA) model for fNIRS system. Our proposed method does not require to any additional hardware and sensor and also it does not need to whole data together that once were of ineluctable necessities in older algorithms such as adaptive filter and Wiener filtering. Results show that our approach is successful in cleaning contaminated fNIRS data.
Wiener index on rows of unit cells of the face-centred cubic lattice.
Mujahed, Hamzeh; Nagy, Benedek
2016-03-01
The Wiener index of a connected graph, known as the `sum of distances', is the first topological index used in chemistry to sum the distances between all unordered pairs of vertices of a graph. The Wiener index, sometimes called the Wiener number, is one of the indices associated with a molecular graph that correlates physical and chemical properties of the molecule, and has been studied for various kinds of graphs. In this paper, the graphs of lines of unit cells of the face-centred cubic lattice are investigated. This lattice is one of the simplest, the most symmetric and the most usual, cubic crystal lattices. Its graphs contain face centres of the unit cells and other vertices, called cube vertices. Closed formulae are obtained to calculate the sum of shortest distances between pairs of cube vertices, between cube vertices and face centres and between pairs of face centres. Based on these formulae, their sum, the Wiener index of a face-centred cubic lattice with unit cells connected in a row graph, is computed.
Choi, Jae Gil Chang, Seung Jun
2013-04-15
In this paper we derive a Cameron-Storvick theorem for the analytic Feynman integral of functionals on product abstract Wiener space B{sup 2}. We then apply our result to obtain an evaluation formula for the analytic Feynman integral of unbounded functionals on B{sup 2}. We also present meaningful examples involving functionals which arise naturally in quantum mechanics.
Wiener index on rows of unit cells of the face-centred cubic lattice.
Mujahed, Hamzeh; Nagy, Benedek
2016-03-01
The Wiener index of a connected graph, known as the `sum of distances', is the first topological index used in chemistry to sum the distances between all unordered pairs of vertices of a graph. The Wiener index, sometimes called the Wiener number, is one of the indices associated with a molecular graph that correlates physical and chemical properties of the molecule, and has been studied for various kinds of graphs. In this paper, the graphs of lines of unit cells of the face-centred cubic lattice are investigated. This lattice is one of the simplest, the most symmetric and the most usual, cubic crystal lattices. Its graphs contain face centres of the unit cells and other vertices, called cube vertices. Closed formulae are obtained to calculate the sum of shortest distances between pairs of cube vertices, between cube vertices and face centres and between pairs of face centres. Based on these formulae, their sum, the Wiener index of a face-centred cubic lattice with unit cells connected in a row graph, is computed. PMID:26919376
NASA Astrophysics Data System (ADS)
Seitz, F.; Kirschner, S.; Neubersch, D.
2012-09-01
The geophysical interpretation of observed time series of Earth rotation parameters (ERP) is commonly based on numerical models that describe and balance variations of angular momentum in various subsystems of the Earth. Naturally, models are dependent on geometrical, rheological and physical parameters. Many of these are weakly determined from other models or observations. In our study we present an adaptive Kalman filter approach for the improvement of parameters of the dynamic Earth system model DyMEG which acts as a simulator of ERP. In particular we focus on the improvement of the pole tide Love number k2. In the frame of a sensitivity analysis k2 has been identified as one of the most crucial parameters of DyMEG since it directly influences the modeled Chandler oscillation. At the same time k2 is one of the most uncertain parameters in the model. Our simulations with DyMEG cover a period of 60 years after which a steady state of k2 is reached. The estimate for k2, accounting for the anelastic response of the Earth's mantle and the ocean, is 0.3531 + 0.0030i. We demonstrate that the application of the improved parameter k2 in DyMEG leads to significantly better results for polar motion than the original value taken from the Conventions of the International Earth Rotation and Reference Systems Service (IERS).
NASA Astrophysics Data System (ADS)
Seitz, F.; Kirschner, S.; Neubersch, D.
2012-12-01
Earth rotation has been monitored using space geodetic techniques since many decades. The geophysical interpretation of observed time series of Earth rotation parameters (ERP) polar motion and length-of-day is commonly based on numerical models that describe and balance variations of angular momentum in various subsystems of the Earth. Naturally, models are dependent on geometrical, rheological and physical parameters. Many of these are weakly determined from other models or observations. In our study we present an adaptive Kalman filter approach for the improvement of parameters of the dynamic Earth system model DyMEG which acts as a simulator of ERP. In particular we focus on the improvement of the pole tide Love number k2. In the frame of a sensitivity analysis k2 has been identified as one of the most crucial parameters of DyMEG since it directly influences the modeled Chandler oscillation. At the same time k2 is one of the most uncertain parameters in the model. Our simulations with DyMEG cover a period of 60 years after which a steady state of k2 is reached. The estimate for k2, accounting for the anelastic response of the Earth's mantle and the ocean, is 0.3531 + 0.0030i. We demonstrate that the application of the improved parameter k2 in DyMEG leads to significantly better results for polar motion than the original value taken from the Conventions of the International Earth Rotation and Reference Systems Service (IERS).
NASA Astrophysics Data System (ADS)
Aridgides, Tom; Fernández, Manuel
2010-04-01
An improved automatic target recognition (ATR) processing string has been developed. The overall processing string consists of pre-processing, subimage adaptive clutter filtering, detection, feature extraction, optimal subset feature selection, feature orthogonalization and classification processing blocks. The objects that are classified by three distinct ATR strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. These three ATR processing strings were individually developed and tuned by researchers from different companies. The utility of the overall processing strings and their fusion was demonstrated with an extensive side-looking sonar dataset. In this paper we describe a new processing improvement: six additional classification features are extracted, using primarily target shadow information and a feature extraction window whose length is now made variable as a function of range. This new ATR processing improvement resulted in a 3:1 reduction in false alarms. Two advanced fusion algorithms are subsequently applied: First, a nonlinear Volterra expansion (2nd order) feature-LLRT fusion algorithm is employed. Second, a repeated application of a subset Volterra feature selection / feature orthogonalization / LLRT fusion block is utilized. It is shown that cascaded Volterra feature- LLRT fusion of the ATR processing strings outperforms baseline "summing" and single-stage Volterra feature-LLRT fusion algorithms, yielding significant improvements over the best single ATR processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate.
NASA Astrophysics Data System (ADS)
Takeuchi, Tsubasa; Mita, Akira
2015-04-01
Recently damage detection methods based on measured vibration data for structural health monitoring (SHM) have been intensively studied. In order to decrease the number of required sensors, however, most of their methods focus only on single dimensional systems, in spite that there are some cases that torsional vibration greatly affect for structural damage. Although some studies consider multiple dimensional systems using frame structures, usually they need lots of sensors and calculation is time-consuming. Therefore, the balance between the cost and the particularity is very important for SHM system. In this paper, a method to localize the damaged area of multi-story buildings considering torsional components is proposed to detect the damage simply and particularly. This method focuses on shift in the center of rigidity caused by induced damage. The damaged quadrant of a certain story is identified comparing story eccentric distances of before and after damage-inducing seismic events. An adaptive extended Kalman filter (AEKF) is utilized to identify unknown structural parameters. Using a model which has four columns in each floor, several cases are considered in the verification study to disclose the capability of our proposed method.
Szabo, T.; Pierpaoli, E.; Pipino, A.; Dong, F.; Gunn, J. E-mail: pierpaol@usc.edu
2011-07-20
We present a new cluster catalog extracted from the Sloan Digital Sky Survey Data Release 6 (SDSS DR6) using an adaptive matched filter (AMF) cluster finder. We identify 69,173 galaxy clusters in the redshift range 0.045 {<=} z < 0.78 in 8420 deg{sup 2} of the sky. We provide angular position, redshift, richness, core, and virial radii estimates for these clusters, as well as an error analysis for each of these quantities. We also provide a catalog of more than 205,000 galaxies representing the three brightest galaxies in the r band which are possible brightest cluster galaxy (BCG) candidates. We show basic properties of the BCG candidates and study how their luminosity scales in redshift and cluster richness. We compare our catalog with the maxBCG and GMBCG catalogs, as well as with that of Wen et al. We match between 30% and 50% of clusters between catalogs over all overlapping redshift ranges. We find that the percentage of matches increases with the richness for all catalogs. We cross match the AMF catalog with available X-ray data in the same area of the sky and find 539 matches, 119 of which with temperature measurements. We present scaling relations between optical and X-ray properties and cluster center comparison. We find that both {Lambda}{sub 200} and R{sub 200} correlate well with both L{sub X} and T{sub X} , with no significant difference in trend if we restrict the matches to flux-limited X-ray samples.
On the improvement of Wiener attack on RSA with small private exponent.
Wu, Mu-En; Chen, Chien-Ming; Lin, Yue-Hsun; Sun, Hung-Min
2014-01-01
RSA system is based on the hardness of the integer factorization problem (IFP). Given an RSA modulus N = pq, it is difficult to determine the prime factors p and q efficiently. One of the most famous short exponent attacks on RSA is the Wiener attack. In 1997, Verheul and van Tilborg use an exhaustive search to extend the boundary of the Wiener attack. Their result shows that the cost of exhaustive search is 2r + 8 bits when extending the Weiner's boundary r bits. In this paper, we first reduce the cost of exhaustive search from 2r + 8 bits to 2r + 2 bits. Then, we propose a method named EPF. With EPF, the cost of exhaustive search is further reduced to 2r - 6 bits when we extend Weiner's boundary r bits. It means that our result is 2(14) times faster than Verheul and van Tilborg's result. Besides, the security boundary is extended 7 bits.
Liang, Xiao; Wang, Linshan; Wang, Yangfan; Wang, Ruili
2016-09-01
In this paper, we focus on the long time behavior of the mild solution to delayed reaction-diffusion Hopfield neural networks (DRDHNNs) driven by infinite dimensional Wiener processes. We analyze the existence, uniqueness, and stability of this system under the local Lipschitz function by constructing an appropriate Lyapunov-Krasovskii function and utilizing the semigroup theory. Some easy-to-test criteria affecting the well-posedness and stability of the networks, such as infinite dimensional noise and diffusion effect, are obtained. The criteria can be used as theoretic guidance to stabilize DRDHNNs in practical applications when infinite dimensional noise is taken into consideration. Meanwhile, considering the fact that the standard Brownian motion is a special case of infinite dimensional Wiener process, we undertake an analysis of the local Lipschitz condition, which has a wider range than the global Lipschitz condition. Two samples are given to examine the availability of the results in this paper. Simulations are also given using the MATLAB.
The Vertex Version of Weighted Wiener Number for Bicyclic Molecular Structures.
Gao, Wei; Wang, Weifan
2015-01-01
Graphs are used to model chemical compounds and drugs. In the graphs, each vertex represents an atom of molecule and edges between the corresponding vertices are used to represent covalent bounds between atoms. We call such a graph, which is derived from a chemical compound, a molecular graph. Evidence shows that the vertex-weighted Wiener number, which is defined over this molecular graph, is strongly correlated to both the melting point and boiling point of the compounds. In this paper, we report the extremal vertex-weighted Wiener number of bicyclic molecular graph in terms of molecular structural analysis and graph transformations. The promising prospects of the application for the chemical and pharmacy engineering are illustrated by theoretical results achieved in this paper. PMID:26640513
On the Improvement of Wiener Attack on RSA with Small Private Exponent
Chen, Chien-Ming; Lin, Yue-Hsun
2014-01-01
RSA system is based on the hardness of the integer factorization problem (IFP). Given an RSA modulus N = pq, it is difficult to determine the prime factors p and q efficiently. One of the most famous short exponent attacks on RSA is the Wiener attack. In 1997, Verheul and van Tilborg use an exhaustive search to extend the boundary of the Wiener attack. Their result shows that the cost of exhaustive search is 2r + 8 bits when extending the Weiner's boundary r bits. In this paper, we first reduce the cost of exhaustive search from 2r + 8 bits to 2r + 2 bits. Then, we propose a method named EPF. With EPF, the cost of exhaustive search is further reduced to 2r − 6 bits when we extend Weiner's boundary r bits. It means that our result is 214 times faster than Verheul and van Tilborg's result. Besides, the security boundary is extended 7 bits. PMID:24982974
The Vertex Version of Weighted Wiener Number for Bicyclic Molecular Structures
Gao, Wei; Wang, Weifan
2015-01-01
Graphs are used to model chemical compounds and drugs. In the graphs, each vertex represents an atom of molecule and edges between the corresponding vertices are used to represent covalent bounds between atoms. We call such a graph, which is derived from a chemical compound, a molecular graph. Evidence shows that the vertex-weighted Wiener number, which is defined over this molecular graph, is strongly correlated to both the melting point and boiling point of the compounds. In this paper, we report the extremal vertex-weighted Wiener number of bicyclic molecular graph in terms of molecular structural analysis and graph transformations. The promising prospects of the application for the chemical and pharmacy engineering are illustrated by theoretical results achieved in this paper. PMID:26640513
Modeling of complex-valued wiener systems using B-spline neural network.
Hong, Xia; Chen, Sheng
2011-05-01
In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches. PMID:21550875
Blanco Massani, M; Molina, V; Sanchez, M; Renaud, V; Eisenberg, P; Vignolo, G
2014-05-16
Bacteriocins from lactic acid bacteria have potential as natural food preservatives. In this study two active (synthetic and gluten) films were obtained by the incorporation of lactocin 705 and lactocin AL705, bacteriocins produced by Lactobacillus curvatus CRL705 with antimicrobial activity against spoilage lactic acid bacteria and Listeria. Antimicrobial film effectiveness was determined in Wieners inoculated with Lactobacillus plantarum CRL691 and Listeria innocua 7 (10(4)CFU/g) stored at 5°C during 45days. Active and control (absence of bacteriocins) packages were prepared and bacterial counts in selective media were carried out. Visual inspection and pH measurement of Wieners were also performed. Typical growth of both inoculated microorganisms was observed in control packages which reached 10(6)-10(7)CFU/g at the end of storage period. In the active packages, L. innocua 7 was effectively inhibited (2.5 log cycles reduction at day 45), while L. plantarum CRL691 was only slightly inhibited (0.5 log cycles) up to the second week of storage, then counts around 10(6)-10(7)CFU/g were reached. Changes in pH values from 6.3 to 5.8 were produced and gas formation was observed in active and control packages. The low inhibitory effectiveness against lactic acid bacteria is in correlation with the low activity observed for lactocin 705 in the presence of fat; Wieners fat content (20-30%) may adversely affect antimicrobial activity. This study supports the feasibility of using polymers activated with L. curvatus CRL705 bacteriocins to control Listeria on the surface of Wieners and highlights the importance of evaluating antimicrobial packaging systems for each particular food application.
Song, Jae Hee; Kim, Choye; Yoo, Yangmo
2015-03-01
Effective vein visualization is clinically important for various point-of-care applications, such as needle insertion. It can be achieved by utilizing ultrasound imaging or by applying infrared laser excitation and monitoring its absorption. However, while these approaches can be used for vein visualization, they are not suitable for point-of-care applications because of their cost, time, and accessibility. In this paper, a new vein visualization method based on multispectral Wiener estimation is proposed and its real-time implementation on a smart phone is presented. In the proposed method, a conventional RGB camera on a commercial smart phone (i.e., Galaxy Note 2, Samsung Electronics Inc., Suwon, Korea) is used to acquire reflectance information from veins. Wiener estimation is then applied to extract the multispectral information from the veins. To evaluate the performance of the proposed method, an experiment was conducted using a color calibration chart (ColorChecker Classic, X-rite, Grand Rapids, MI, USA) and an average root-mean-square error of 12.0% was obtained. In addition, an in vivo subcutaneous vein imaging experiment was performed to explore the clinical performance of the smart phone-based Wiener estimation. From the in vivo experiment, the veins at various sites were successfully localized using the reconstructed multispectral images and these results were confirmed by ultrasound B-mode and color Doppler images. These results indicate that the presented multispectral Wiener estimation method can be used for visualizing veins using a commercial smart phone for point-of-care applications (e.g., vein puncture guidance). PMID:24691170
Real Paley-Wiener theorems for the Koornwinder-Swarttouw q-Hankel transform
NASA Astrophysics Data System (ADS)
Abreu, Luís Daniel
2007-10-01
We derive two real Paley-Wiener theorems in the setting of quantum calculus. The first uses techniques due to Tuan and Zayed [V.K. Tuan, A.I. Zayed, Paley-Wiener-type theorems for a class of integral transforms, J. Math. Anal. Appl. 266 (1) (2002) 200-226] in order to describe the image of the space under Koornwinder and Swarttouw q-Hankel transform [T.H. Koornwinder, REF. Swarttouw, On q-analogues of the Fourier and Hankel transforms, TransE Amer. Math. Soc. 333 (1) (1992) 445-461] and contains as a special case a description of the domain of the q-sampling theorem associated with the q-Hankel transform [L.D. Abreu, A q-sampling theorem related to the q-Hankel transform, Proc. Amer. Math. Soc. 133 (4) (2005) 1197-1203]. The second characterizes the image of compactly supported q-smooth functions under a rescaled version of the q-Hankel transform and is a q-analogue of a recent result due to Andersen [N.B. Andersen, Real Paley-Wiener theorems for the Hankel transform, J. Fourier Anal. Appl. 12 (1) (2006) 17-25].
Bergman, W.
1985-01-09
An electric disk filter provides a high efficiency at high temperature. A hollow outer filter of fibrous stainless steel forms the ground electrode. A refractory filter material is placed between the outer electrode and the inner electrically isolated high voltage electrode. Air flows through the outer filter surfaces through the electrified refractory filter media and between the high voltage electrodes and is removed from a space in the high voltage electrode.
Bergman, Werner
1986-01-01
An electric disk filter provides a high efficiency at high temperature. A hollow outer filter of fibrous stainless steel forms the ground electrode. A refractory filter material is placed between the outer electrode and the inner electrically isolated high voltage electrode. Air flows through the outer filter surfaces through the electrified refractory filter media and between the high voltage electrodes and is removed from a space in the high voltage electrode.
Son, Sung Sil; Choo, Ki Seok; Jeon, Ung Bae; Jeon, Gye Rok; Nam, Kyung Jin; Kim, Tae Un; Yeom, Jeong A; Hwang, Jae Yeon; Jeong, Dong Wook; Lim, Soo Jin
2015-06-01
To retrospectively evaluate the image quality of CT angiography (CTA) reconstructed by model-based iterative reconstruction (MBIR) and to compare this with images obtained by filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASIR) in newborns and infants with congenital heart disease (CHD). Thirty-seven children (age 4.8 ± 3.7 months; weight 4.79 ± 0.47 kg) with suspected CHD underwent CTA on a 64detector MDCT without ECG gating (80 kVp, 40 mA using tube current modulation). Total dose length product was recorded in all patients. Images were reconstructed using FBP, ASIR, and MBIR. Objective image qualities (density, noise) were measured in the great vessels and heart chambers. The contrast-to-noise ratio (CNR) was calculated by measuring the density and noise of myocardial walls. Two radiologists evaluated images for subjective noise, diagnostic confidence, and sharpness at the level prior to the first branch of the main pulmonary artery. Images were compared with respect to reconstruction method, and reconstruction times were measured. Images from all patients were diagnostic, and the effective dose was 0.22 mSv. The objective image noise of MBIR was significantly lower than those of FBP and ASIR in the great vessels and heart chambers (P < 0.05); however, with respect to attenuations in the four chambers, ascending aorta, descending aorta, and pulmonary trunk, no statistically significant difference was observed among the three methods (P > 0.05). Mean CNR values were 8.73 for FBP, 14.54 for ASIR, and 22.95 for MBIR. In addition, the subjective image noise of MBIR was significantly lower than those of the others (P < 0.01). Furthermore, while FBP had the highest score for image sharpness, ASIR had the highest score for diagnostic confidence (P < 0.05), and mean reconstruction times were 5.1 ± 2.3 s for FBP and ASIR and 15.1 ± 2.4 min for MBIR. While CTA with MBIR in newborns and infants with CHD can reduce image noise and
NASA Technical Reports Server (NTRS)
1993-01-01
The Aquaspace H2OME Guardian Water Filter, available through Western Water International, Inc., reduces lead in water supplies. The filter is mounted on the faucet and the filter cartridge is placed in the "dead space" between sink and wall. This filter is one of several new filtration devices using the Aquaspace compound filter media, which combines company developed and NASA technology. Aquaspace filters are used in industrial, commercial, residential, and recreational environments as well as by developing nations where water is highly contaminated.
Generalized Selection Weighted Vector Filters
NASA Astrophysics Data System (ADS)
Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.
2004-12-01
This paper introduces a class of nonlinear multichannel filters capable of removing impulsive noise in color images. The here-proposed generalized selection weighted vector filter class constitutes a powerful filtering framework for multichannel signal processing. Previously defined multichannel filters such as vector median filter, basic vector directional filter, directional-distance filter, weighted vector median filters, and weighted vector directional filters are treated from a global viewpoint using the proposed framework. Robust order-statistic concepts and increased degree of freedom in filter design make the proposed method attractive for a variety of applications. Introduced multichannel sigmoidal adaptation of the filter parameters and its modifications allow to accommodate the filter parameters to varying signal and noise statistics. Simulation studies reported in this paper indicate that the proposed filter class is computationally attractive, yields excellent performance, and is able to preserve fine details and color information while efficiently suppressing impulsive noise. This paper is an extended version of the paper by Lukac et al. presented at the 2003 IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03) in Grado, Italy.
ERIC Educational Resources Information Center
Klemetson, S. L.
1978-01-01
Presents the 1978 literature review of wastewater treatment. The review is concerned with biological filters, and it covers: (1) trickling filters; (2) rotating biological contractors; and (3) miscellaneous reactors. A list of 14 references is also presented. (HM)
Electronic filters, hearing aids and methods
NASA Technical Reports Server (NTRS)
Engebretson, A. Maynard (Inventor); O'Connell, Michael P. (Inventor); Zheng, Baohua (Inventor)
1991-01-01
An electronic filter for an electroacoustic system. The system has a microphone for generating an electrical output from external sounds and an electrically driven transducer for emitting sound. Some of the sound emitted by the transducer returns to the microphone means to add a feedback contribution to its electical output. The electronic filter includes a first circuit for electronic processing of the electrical output of the microphone to produce a filtered signal. An adaptive filter, interconnected with the first circuit, performs electronic processing of the filtered signal to produce an adaptive output to the first circuit to substantially offset the feedback contribution in the electrical output of the microphone, and the adaptive filter includes means for adapting only in response to polarities of signals supplied to and from the first circuit. Other electronic filters for hearing aids, public address systems and other electroacoustic systems, as well as such systems, and methods of operating them are also disclosed.
Sutton, J.B.; Torrey, J.V.P.
1958-08-26
A process is described for reconditioning fused alumina filters which have become clogged by the accretion of bismuth phosphate in the filter pores, The method consists in contacting such filters with faming sulfuric acid, and maintaining such contact for a substantial period of time.
NASA Technical Reports Server (NTRS)
1987-01-01
A compact, lightweight electrolytic water filter generates silver ions in concentrations of 50 to 100 parts per billion in the water flow system. Silver ions serve as effective bactericide/deodorizers. Ray Ward requested and received from NASA a technical information package on the Shuttle filter, and used it as basis for his own initial development, a home use filter.
Comparison of Deconvolution Filters for Photoacoustic Tomography
Van de Sompel, Dominique; Sasportas, Laura S.; Jokerst, Jesse V.; Gambhir, Sanjiv S.
2016-01-01
In this work, we compare the merits of three temporal data deconvolution methods for use in the filtered backprojection algorithm for photoacoustic tomography (PAT). We evaluate the standard Fourier division technique, the Wiener deconvolution filter, and a Tikhonov L-2 norm regularized matrix inversion method. Our experiments were carried out on subjects of various appearances, namely a pencil lead, two man-made phantoms, an in vivo subcutaneous mouse tumor model, and a perfused and excised mouse brain. All subjects were scanned using an imaging system with a rotatable hemispherical bowl, into which 128 ultrasound transducer elements were embedded in a spiral pattern. We characterized the frequency response of each deconvolution method, compared the final image quality achieved by each deconvolution technique, and evaluated each method’s robustness to noise. The frequency response was quantified by measuring the accuracy with which each filter recovered the ideal flat frequency spectrum of an experimentally measured impulse response. Image quality under the various scenarios was quantified by computing noise versus resolution curves for a point source phantom, as well as the full width at half maximum (FWHM) and contrast-to-noise ratio (CNR) of selected image features such as dots and linear structures in additional imaging subjects. It was found that the Tikhonov filter yielded the most accurate balance of lower and higher frequency content (as measured by comparing the spectra of deconvolved impulse response signals to the ideal flat frequency spectrum), achieved a competitive image resolution and contrast-to-noise ratio, and yielded the greatest robustness to noise. While the Wiener filter achieved a similar image resolution, it tended to underrepresent the lower frequency content of the deconvolved signals, and hence of the reconstructed images after backprojection. In addition, its robustness to noise was poorer than that of the Tikhonov filter. The
Review and implementation of cure models based on first hitting times for Wiener processes.
Balka, Jeremy; Desmond, Anthony F; McNicholas, Paul D
2009-06-01
The development of models and methods for cure rate estimation has recently burgeoned into an important subfield of survival analysis. Much of the literature focuses on the standard mixture model. Recently, process-based models have been suggested. We focus on several models based on first passage times for Wiener processes. Whitmore and others have studied these models in a variety of contexts. Lee and Whitmore (Stat Sci 21(4):501-513, 2006) give a comprehensive review of a variety of first hitting time models and briefly discuss their potential as cure rate models. In this paper, we study the Wiener process with negative drift as a possible cure rate model but the resulting defective inverse Gaussian model is found to provide a poor fit in some cases. Several possible modifications are then suggested, which improve the defective inverse Gaussian. These modifications include: the inverse Gaussian cure rate mixture model; a mixture of two inverse Gaussian models; incorporation of heterogeneity in the drift parameter; and the addition of a second absorbing barrier to the Wiener process, representing an immunity threshold. This class of process-based models is a useful alternative to the standard model and provides an improved fit compared to the standard model when applied to many of the datasets that we have studied. Implementation of this class of models is facilitated using expectation-maximization (EM) algorithms and variants thereof, including the gradient EM algorithm. Parameter estimates for each of these EM algorithms are given and the proposed models are applied to both real and simulated data, where they perform well.
Rodgers, John C.; McFarland, Andrew R.; Ortiz, Carlos A.
1995-01-01
A quick-change filter cartridge. In sampling systems for measurement of airborne materials, a filter element is introduced into the sampled airstream such that the aerosol constituents are removed and deposited on the filter. Fragile sampling media often require support in order to prevent rupture during sampling, and careful mounting and sealing to prevent misalignment, tearing, or creasing which would allow the sampled air to bypass the filter. Additionally, handling of filter elements may introduce cross-contamination or exposure of operators to toxic materials. Moreover, it is desirable to enable the preloading of filter media into quick-change cartridges in clean laboratory environments, thereby simplifying and expediting the filter-changing process in the field. The quick-change filter cartridge of the present invention permits the application of a variety of filter media in many types of instruments and may also be used in automated systems. The cartridge includes a base through which a vacuum can be applied to draw air through the filter medium which is located on a porous filter support and held there by means of a cap which forms an airtight seal with the base. The base is also adapted for receiving absorbing media so that both particulates and gas-phase samples may be trapped for investigation, the latter downstream of the aerosol filter.
Stochastic system identification of skin properties: linear and wiener static nonlinear methods.
Chen, Yi; Hunter, Ian W
2012-10-01
Wiener static nonlinear system identification was used to study the linear dynamics and static nonlinearities in the response of skin and underlying tissue under indentation in vivo. A device capable of measuring the dynamic mechanical properties of bulk skin tissue was developed and it incorporates a custom-built Lorentz force actuator that measures the dynamic compliance between the input force (<12 N) and the output displacement (<20 mm). A simple linear stochastic system identification technique produced a variance accounted for (VAF) of 75-81% and Wiener static nonlinear techniques increased the VAF by 5%. Localized linear techniques increased the VAF to 85-95% with longer tests. Indentation experiments were conducted on 16 test subjects to determine device sensitivity and repeatability. Using the device, the coefficient of variation of test metrics was found to be as low as 2% for a single test location. The measured tissue stiffness was 300 N/m near the surface and 4.5 kN/m for high compression. The damping ranged from 5 to 23 N s/m. The bulk skin properties were also shown to vary significantly with gender and body mass index. The device and techniques used in this research can be applied to consumer product analysis, medical diagnosis and tissue research.
2013-01-01
In the paper, we consider the problem of pricing options in wide classes of Lévy processes. We propose a general approach to the numerical methods based on a finite difference approximation for the generalized Black-Scholes equation. The goal of the paper is to incorporate the Wiener-Hopf factorization into finite difference methods for pricing options in Lévy models with jumps. The method is applicable for pricing barrier and American options. The pricing problem is reduced to the sequence of linear algebraic systems with a dense Toeplitz matrix; then the Wiener-Hopf factorization method is applied. We give an important probabilistic interpretation based on the infinitely divisible distributions theory to the Laurent operators in the correspondent factorization identity. Notice that our algorithm has the same complexity as the ones which use the explicit-implicit scheme, with a tridiagonal matrix. However, our method is more accurate. We support the advantage of the new method in terms of accuracy and convergence by using numerical experiments. PMID:24489518
Analysis of responses to noise in the ventral cochlear nucleus using Wiener kernels.
Recio-Spinoso, Alberto; van Dijk, Pim
2006-01-01
Responses to noise were recorded in ventral cochlear nucleus (VCN) neurons of anesthetized chinchillas and cats, then analyzed using Wiener-kernel theory. First-order kernels, which are proportional to reverse-correlation functions, of primary-like (PL) and primary-like with notch (PLN) neurons having low characteristic frequency (CF) are similar to those obtained in auditory nerve fibers (ANFs). Such kernels consist of lightly damped transient oscillations with frequency equal to the neuron's CF. The first-order kernel of high-CF PL and PLN neurons displays no evidence of tuning to CF. Second-order kernels of the aforementioned VCN neuron types also resemble those in the nerve, irrespective of CF. In general, first- and second-order Wiener kernels of chopper neurons are similar to those obtained in high-CF ANFs. This is likely the consequence of the poor phase-locking capabilities to near-CF tones exhibited by chopper neurons. By analyzing second-order kernels using singular-value decomposition, it was possible to estimate group delays for the entire neuronal population, regardless of the neuron's type or CF. This was done by analyzing the highest-ranking singular vector (FSV). Amplitude values of FSVs in chopper neurons in the cat are substantially larger than in high-spontaneous ANFs. PMID:16644154
Teaching temporal coherence and the Wiener Khintchin theorem at a senior/graduate level
NASA Astrophysics Data System (ADS)
Chavel, Pierre H.; Jonathan, Jean-Michel C.
2002-05-01
One important issue in teaching interferences is that two separate wavelengths usually do not interfere: any interference pattern is the spectral integral of all interference patterns of all monochromatic components. Although optical detectors are quadratic in nature, crossed terms involving two different frequencies in the expression of an interference pattern vanish. More precisely, while in non stationary signals such as ultrashort pulses two wavelengths can give rise to beating phenomena, this does not happen with the usual thermal light beams. The phenomenon is directly connected with the Wiener Khintchin theorem, and therefore with the principle of Fourier transform spectroscopy. In an introductory course, oversimplification leading to frustrating physical and mathematical deficiencies is hardly avoidable. In this communication, we suggest an introduction of this question at a senior/graduate level. Numerical simulations are used to provide an intuitive understanding of the phenomena. If the Wiener Khintchin theorem is introduced by defining the power spectrum from the infinite time limit of the ordinary Fourier transform of a Gaussian windowed version of the signal, the mathematics are simple and the method offers a clear connection with the operation of real (i.e. finite time) detectors analyzing interference fringes.
Stability of continuous-time quantum filters with measurement imperfections
NASA Astrophysics Data System (ADS)
Amini, H.; Pellegrini, C.; Rouchon, P.
2014-07-01
The fidelity between the state of a continuously observed quantum system and the state of its associated quantum filter, is shown to be always a submartingale. The observed system is assumed to be governed by a continuous-time Stochastic Master Equation (SME), driven simultaneously by Wiener and Poisson processes and that takes into account incompleteness and errors in measurements. This stability result is the continuous-time counterpart of a similar stability result already established for discrete-time quantum systems and where the measurement imperfections are modelled by a left stochastic matrix.
Haldipur, G.B.; Dilmore, W.J.
1992-09-01
A vertical vessel is described having a lower inlet and an upper outlet enclosure separated by a main horizontal tube sheet. The inlet enclosure receives the flue gas from a boiler of a power system and the outlet enclosure supplies cleaned gas to the turbines. The inlet enclosure contains a plurality of particulate-removing clusters, each having a plurality of filter units. Each filter unit includes a filter clean-gas chamber defined by a plate and a perforated auxiliary tube sheet with filter tubes suspended from each tube sheet and a tube connected to each chamber for passing cleaned gas to the outlet enclosure. The clusters are suspended from the main tube sheet with their filter units extending vertically and the filter tubes passing through the tube sheet and opening in the outlet enclosure. The flue gas is circulated about the outside surfaces of the filter tubes and the particulate is absorbed in the pores of the filter tubes. Pulses to clean the filter tubes are passed through their inner holes through tubes free of bends which are aligned with the tubes that pass the clean gas. 18 figs.
Haldipur, Gaurang B.; Dilmore, William J.
1992-01-01
A vertical vessel having a lower inlet and an upper outlet enclosure separated by a main horizontal tube sheet. The inlet enclosure receives the flue gas from a boiler of a power system and the outlet enclosure supplies cleaned gas to the turbines. The inlet enclosure contains a plurality of particulate-removing clusters, each having a plurality of filter units. Each filter unit includes a filter clean-gas chamber defined by a plate and a perforated auxiliary tube sheet with filter tubes suspended from each tube sheet and a tube connected to each chamber for passing cleaned gas to the outlet enclosure. The clusters are suspended from the main tube sheet with their filter units extending vertically and the filter tubes passing through the tube sheet and opening in the outlet enclosure. The flue gas is circulated about the outside surfaces of the filter tubes and the particulate is absorbed in the pores of the filter tubes. Pulses to clean the filter tubes are passed through their inner holes through tubes free of bends which are aligned with the tubes that pass the clean gas.
Montagnini, Leone; Tabacchi, Marco Elio; Termini, Settimo
2016-01-01
Is Biophysics an interdisciplinary science? In order to answer this rhetorical question, it can be useful to look back at history of disciplines, as well as that of the scientific institutions helping their development. In this contribution some aspects of the unusual hodgepodge of concepts involving Biophysics, the legacy of Cybernetics, cognitive science and the central figure of Norbert Wiener are presented and discussed.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 9 Animals and Animal Products 2 2014-01-01 2014-01-01 false Beans with frankfurters in sauce... STANDARDS OF IDENTITY OR COMPOSITION Canned, Frozen, or Dehydrated Meat Food Products § 319.309 Beans with frankfurters in sauce, sauerkraut with wieners and juice, and similar products. “Beans with Frankfurters...
Code of Federal Regulations, 2011 CFR
2011-01-01
... 9 Animals and Animal Products 2 2011-01-01 2011-01-01 false Beans with frankfurters in sauce... STANDARDS OF IDENTITY OR COMPOSITION Canned, Frozen, or Dehydrated Meat Food Products § 319.309 Beans with frankfurters in sauce, sauerkraut with wieners and juice, and similar products. “Beans with Frankfurters...
Code of Federal Regulations, 2012 CFR
2012-01-01
... 9 Animals and Animal Products 2 2012-01-01 2012-01-01 false Beans with frankfurters in sauce... STANDARDS OF IDENTITY OR COMPOSITION Canned, Frozen, or Dehydrated Meat Food Products § 319.309 Beans with frankfurters in sauce, sauerkraut with wieners and juice, and similar products. “Beans with Frankfurters...
Code of Federal Regulations, 2010 CFR
2010-01-01
... 9 Animals and Animal Products 2 2010-01-01 2010-01-01 false Beans with frankfurters in sauce... STANDARDS OF IDENTITY OR COMPOSITION Canned, Frozen, or Dehydrated Meat Food Products § 319.309 Beans with frankfurters in sauce, sauerkraut with wieners and juice, and similar products. “Beans with Frankfurters...
Code of Federal Regulations, 2013 CFR
2013-01-01
... 9 Animals and Animal Products 2 2013-01-01 2013-01-01 false Beans with frankfurters in sauce... STANDARDS OF IDENTITY OR COMPOSITION Canned, Frozen, or Dehydrated Meat Food Products § 319.309 Beans with frankfurters in sauce, sauerkraut with wieners and juice, and similar products. “Beans with Frankfurters...
NASA Astrophysics Data System (ADS)
Soliz, P.; Nemeth, S. C.; Erry, G. R. G.; Otten, L. J.; Yang, S. Y.
Aim: The objective of this project was to apply an image restoration methodology based on wavefront measurements obtained with a Shack-Hartmann sensor and evaluating the restored image quality based on medical criteria.Methods: Implementing an adaptive optics (AO) technique, a fundus imager was used to achieve low-order correction to images of the retina. The high-order correction was provided by deconvolution. A Shack-Hartmann wavefront sensor measures aberrations. The wavefront measurement is the basis for activating a deformable mirror. Image restoration to remove remaining aberrations is achieved by direct deconvolution using the point spread function (PSF) or a blind deconvolution. The PSF is estimated using measured wavefront aberrations. Direct application of classical deconvolution methods such as inverse filtering, Wiener filtering or iterative blind deconvolution (IBD) to the AO retinal images obtained from the adaptive optical imaging system is not satisfactory because of the very large image size, dificulty in modeling the system noise, and inaccuracy in PSF estimation. Our approach combines direct and blind deconvolution to exploit available system information, avoid non-convergence, and time-consuming iterative processes. Results: The deconvolution was applied to human subject data and resulting restored images compared by a trained ophthalmic researcher. Qualitative analysis showed significant improvements. Neovascularization can be visualized with the adaptive optics device that cannot be resolved with the standard fundus camera. The individual nerve fiber bundles are easily resolved as are melanin structures in the choroid. Conclusion: This project demonstrated that computer-enhanced, adaptive optic images have greater detail of anatomical and pathological structures.
The Laplace method for small deviations of Gaussian processes of Wiener type
NASA Astrophysics Data System (ADS)
Fatalov, V. R.
2005-04-01
Results on the exact asymptotics of the probabilities \\displaystyle \\mathsf P\\biggl\\{\\,\\int_0^1\\vert\\xi(t)\\vert^p\\,dt \\le\\varepsilon^p\\biggr\\},\\qquad\\varepsilon\\to 0,for p>0 are proved for two Gaussian processes \\xi(t): the Wiener process and the Brownian bridge. The method of study is the Laplace method in Banach spaces and the approach to the probabilities of small deviations based on the theory of large deviations for the occupation time. The calculations are carried out for the cases p=1 and p=2 as a result of solving the extremal problem for the action functional and studying the corresponding Schrödinger equations.
Watson, B.L.; Aeby, I.
1980-08-26
An adaptive data compression device for compressing data is described. The device has a frequency content, including a plurality of digital filters for analyzing the content of the data over a plurality of frequency regions, a memory, and a control logic circuit for generating a variable rate memory clock corresponding to the analyzed frequency content of the data in the frequency region and for clocking the data into the memory in response to the variable rate memory clock.
Watson, Bobby L.; Aeby, Ian
1982-01-01
An adaptive data compression device for compressing data having variable frequency content, including a plurality of digital filters for analyzing the content of the data over a plurality of frequency regions, a memory, and a control logic circuit for generating a variable rate memory clock corresponding to the analyzed frequency content of the data in the frequency region and for clocking the data into the memory in response to the variable rate memory clock.
Electronic filters, hearing aids and methods
NASA Technical Reports Server (NTRS)
Engebretson, A. Maynard (Inventor)
1995-01-01
An electronic filter for an electroacoustic system. The system has a microphone for generating an electrical output from external sounds and an electrically driven transducer for emitting sound. Some of the sound emitted by the transducer returns to the microphone means to add a feedback contribution to its electrical output. The electronic filter includes a first circuit for electronic processing of the electrical output of the microphone to produce a first signal. An adaptive filter, interconnected with the first circuit, performs electronic processing of the first signal to produce an adaptive output to the first circuit to substantially offset the feedback contribution in the electrical output of the microphone, and the adaptive filter includes means for adapting only in response to polarities of signals supplied to and from the first circuit. Other electronic filters for hearing aids, public address systems and other electroacoustic systems, as well as such systems and methods of operating them are also disclosed.
Kuban, D.P.; Singletary, B.H.; Evans, J.H.
A plurality of holding tubes are respectively mounted in apertures in a partition plate fixed in a housing receiving gas contaminated with particulate material. A filter cartridge is removably held in each holding tube, and the cartridges and holding tubes are arranged so that gas passes through apertures therein and across the the partition plate while particulate material is collected in the cartridges. Replacement filter cartridges are respectively held in holding canisters mounted on a support plate which can be secured to the aforesaid housing, and screws mounted on said canisters are arranged to push replacement cartridges into the cartridge holding tubes and thereby eject used cartridges therefrom.
NASA Technical Reports Server (NTRS)
1988-01-01
Seeking to find a more effective method of filtering potable water that was highly contaminated, Mike Pedersen, founder of Western Water International, learned that NASA had conducted extensive research in methods of purifying water on board manned spacecraft. The key is Aquaspace Compound, a proprietary WWI formula that scientifically blends various types of glandular activated charcoal with other active and inert ingredients. Aquaspace systems remove some substances; chlorine, by atomic adsorption, other types of organic chemicals by mechanical filtration and still others by catalytic reaction. Aquaspace filters are finding wide acceptance in industrial, commercial, residential and recreational applications in the U.S. and abroad.
NASA Astrophysics Data System (ADS)
Kawalec-Latała, Ewa
2014-03-01
Most of underground hydrocarbon storage are located in depleted natural gas reservoirs. Seismic survey is the most economical source of detailed subsurface information. The inversion of seismic section for obtaining pseudoacoustic impedance section gives the possibility to extract detailed subsurface information. The seismic wavelet parameters and noise briefly influence the resolution. Low signal parameters, especially long signal duration time and the presence of noise decrease pseudoimpedance resolution. Drawing out from measurement or modelled seismic data approximation of distribution of acoustic pseuoimpedance leads us to visualisation and images useful to stratum homogeneity identification goal. In this paper, the improvement of geologic section image resolution by use of minimum entropy deconvolution method before inversion is applied. The author proposes context and adaptive transformation of images and edge detection methods as a way to increase the effectiveness of correct interpretation of simulated images. In the paper, the edge detection algorithms using Sobel, Prewitt, Robert, Canny operators as well as Laplacian of Gaussian method are emphasised. Wiener filtering of image transformation improving rock section structure interpretation pseudoimpedance matrix on proper acoustic pseudoimpedance value, corresponding to selected geologic stratum. The goal of the study is to develop applications of image transformation tools to inhomogeneity detection in salt deposits.
NASA Technical Reports Server (NTRS)
Shelton, G. B. (Inventor)
1977-01-01
A notch filter for the selective attenuation of a narrow band of frequencies out of a larger band was developed. A helical resonator is connected to an input circuit and an output circuit through discrete and equal capacitors, and a resistor is connected between the input and the output circuits.
NASA Astrophysics Data System (ADS)
Basharov, A. M.
2012-09-01
It is shown that the effective Hamiltonian representation, as it is formulated in author's papers, serves as a basis for distinguishing, in a broadband environment of an open quantum system, independent noise sources that determine, in terms of the stationary quantum Wiener and Poisson processes in the Markov approximation, the effective Hamiltonian and the equation for the evolution operator of the open system and its environment. General stochastic differential equations of generalized Langevin (non-Wiener) type for the evolution operator and the kinetic equation for the density matrix of an open system are obtained, which allow one to analyze the dynamics of a wide class of localized open systems in the Markov approximation. The main distinctive features of the dynamics of open quantum systems described in this way are the stabilization of excited states with respect to collective processes and an additional frequency shift of the spectrum of the open system. As an illustration of the general approach developed, the photon dynamics in a single-mode cavity without losses on the mirrors is considered, which contains identical intracavity atoms coupled to the external vacuum electromagnetic field. For some atomic densities, the photons of the cavity mode are "locked" inside the cavity, thus exhibiting a new phenomenon of radiation trapping and non-Wiener dynamics.
Degradation modeling of mid-power white-light LEDs by using Wiener process.
Huang, Jianlin; Golubović, Dušan S; Koh, Sau; Yang, Daoguo; Li, Xiupeng; Fan, Xuejun; Zhang, G Q
2015-07-27
The IES standard TM-21-11 provides a guideline for lifetime prediction of LED devices. As it uses average normalized lumen maintenance data and performs non-linear regression for lifetime modeling, it cannot capture dynamic and random variation of the degradation process of LED devices. In addition, this method cannot capture the failure distribution, although it is much more relevant in reliability analysis. Furthermore, the TM-21-11 only considers lumen maintenance for lifetime prediction. Color shift, as another important performance characteristic of LED devices, may also render significant degradation during service life, even though the lumen maintenance has not reached the critical threshold. In this study, a modified Wiener process has been employed for the modeling of the degradation of LED devices. By using this method, dynamic and random variations, as well as the non-linear degradation behavior of LED devices, can be easily accounted for. With a mild assumption, the parameter estimation accuracy has been improved by including more information into the likelihood function while neglecting the dependency between the random variables. As a consequence, the mean time to failure (MTTF) has been obtained and shows comparable result with IES TM-21-11 predictions, indicating the feasibility of the proposed method. Finally, the cumulative failure distribution was presented corresponding to different combinations of lumen maintenance and color shift. The results demonstrate that a joint failure distribution of LED devices could be modeled by simply considering their lumen maintenance and color shift as two independent variables. PMID:26367697
NASA Astrophysics Data System (ADS)
Kumar, Ujjwal; Prakash, Amit; Jain, V. K.
The present study attempts to provide an insight into the chaotic nature of air pollutants by applying the recent developments in the field of nonlinear dynamics. The Volterra-Wiener-Korenberg (VWK) series approach by Barahona and Poon [1996. Detection of nonlinear dynamics in short, noisy time series. Nature 381, 215-217] has been used to investigate the nonlinearity of O 3, NO, NO 2 and CO time series at two urban stations, namely—Hohenpeissenberg and Jungfraujoch. Nonlinearity has been detected in NO 2 and CO time series at both the stations. The numerical titration technique [Poon, C., Barahona, M., 2001. Titration of chaos with added noise. Proceedings of the National Academy of Sciences 98, 7107-7112] reveals that the dynamics of NO 2 and CO are indeed governed by deterministic chaos. Cao's method [Cao, L., 1997. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 110, 43-50] to determine the minimum embedding dimension further reveals that probably the dynamics of both NO 2 and CO time series are manifestations of high-dimensional chaos. It is interesting to note that similar chaotic characteristic of NO 2 and CO time series have been observed at both the sites indicating a possible universality in their chaotic nature in the ambient urban environment.
Asymptotic approximation of the Wiener-Hopf technique as applied to jet atomisation
NASA Astrophysics Data System (ADS)
Chen, X.-N.; Kirchgässner, K.
An approximate Wiener-Hopf (WH) technique is developed for solving problems involving fine spatial structure. As an example of the application of this method we investigate the atomisation of a liquid jet. The jet exits from a nozzle into an ambient fluid. Short interfacial waves become unstable and break into small particles. This problem is treated as a potential flow under the influence of capillary effects at the interface and the pressure fluctuation at the nozzle wall. Two simultaneous WH equations are obtained. To solve them, the singular parts in each equation are separated from the regular ones, that leads to a linear system of algebraic equations for the residues. The response-wave amplitudes are evaluated numerically and the instability diagram is presented. It is found that resonance occurs at double roots of the dispersion relation. For a given azimuthal number m, the double roots form two curves parametrised by the Weber number β. They merge at a certain critical point, where an even stronger resonance occurs. This finally selects the dominant modes. By gauging one parameter, namely the velocity ratio U, the theoretical prediction agrees quite well with experimental results of the jet atomisation.
The History Of The Kalman Filter
NASA Technical Reports Server (NTRS)
Mcgee, Leonard A.; Schmidt, Stanley F.
1991-01-01
Paper presents historical view of adaptation of Kalman filtering techniques to aerospace applications and eventually to fields as diverse as exploration for oil and control of powerplants. Describes scientific breakthroughs and reformulations that transformed Kalman filtering techniques into fundamental tool for analyzing and solving broad class of estimation problems.
Adaptive Image Denoising by Mixture Adaptation
NASA Astrophysics Data System (ADS)
Luo, Enming; Chan, Stanley H.; Nguyen, Truong Q.
2016-10-01
We propose an adaptive learning procedure to learn patch-based image priors for image denoising. The new algorithm, called the Expectation-Maximization (EM) adaptation, takes a generic prior learned from a generic external database and adapts it to the noisy image to generate a specific prior. Different from existing methods that combine internal and external statistics in ad-hoc ways, the proposed algorithm is rigorously derived from a Bayesian hyper-prior perspective. There are two contributions of this paper: First, we provide full derivation of the EM adaptation algorithm and demonstrate methods to improve the computational complexity. Second, in the absence of the latent clean image, we show how EM adaptation can be modified based on pre-filtering. Experimental results show that the proposed adaptation algorithm yields consistently better denoising results than the one without adaptation and is superior to several state-of-the-art algorithms.
Passmore, Brandon Scott; Shaner, Eric Arthur; Barrick, Todd A.
2009-09-01
Metal films perforated with subwavelength hole arrays have been show to demonstrate an effect known as Extraordinary Transmission (EOT). In EOT devices, optical transmission passbands arise that can have up to 90% transmission and a bandwidth that is only a few percent of the designed center wavelength. By placing a tunable dielectric in proximity to the EOT mesh, one can tune the center frequency of the passband. We have demonstrated over 1 micron of passive tuning in structures designed for an 11 micron center wavelength. If a suitable midwave (3-5 micron) tunable dielectric (perhaps BaTiO{sub 3}) were integrated with an EOT mesh designed for midwave operation, it is possible that a fast, voltage tunable, low temperature filter solution could be demonstrated with a several hundred nanometer passband. Such an element could, for example, replace certain components in a filter wheel solution.
NASA Technical Reports Server (NTRS)
1982-01-01
A compact, lightweight electrolytic water sterilizer available through Ambassador Marketing, generates silver ions in concentrations of 50 to 100 parts per billion in water flow system. The silver ions serve as an effective bactericide/deodorizer. Tap water passes through filtering element of silver that has been chemically plated onto activated carbon. The silver inhibits bacterial growth and the activated carbon removes objectionable tastes and odors caused by addition of chlorine and other chemicals in municipal water supply. The three models available are a kitchen unit, a "Tourister" unit for portable use while traveling and a refrigerator unit that attaches to the ice cube water line. A filter will treat 5,000 to 10,000 gallons of water.
NASA Technical Reports Server (NTRS)
1987-01-01
Biomedical Optical Company of America's suntiger lenses eliminate more than 99% of harmful light wavelengths. NASA derived lenses make scenes more vivid in color and also increase the wearer's visual acuity. Distant objects, even on hazy days, appear crisp and clear; mountains seem closer, glare is greatly reduced, clouds stand out. Daytime use protects the retina from bleaching in bright light, thus improving night vision. Filtering helps prevent a variety of eye disorders, in particular cataracts and age related macular degeneration.
A Novel HEPA Filter Encapsulation Process
Gates-Anderson, D.; Kidd, S.; Attebery, R.; Belue, T.; Bowers, J.; Rogers, H.
2002-02-28
Waste management engineers at Lawrence Livermore National Laboratory have developed an innovative process for the treatment of contaminated HEPA filters. The In Situ Stabilization and Filter Encapsulation (IS SAFE) Process provides several advantages over existing HEPA filter treatment processes. Treatment is accomplished by filling a spent HEPA filter with a low viscosity resin that cures to form a solid monolith. Once a solid monolith has been formed, the HEPA filter has been transformed in two ways: (1) worker hazards/risks associated with handling the filter are eliminated and (2) the in situ encapsulated filter meets LDR requirements and can be disposed of in a permitted landfill. A patent has been filed for this process. The IS SAFE process will be applicable to hazardous, mixed, and low level radioactively contaminated spent filters. Spent HEPA filters are prepared for processing by attaching vacuum fittings to the top and bottom openings of the filter. These fittings are used to attach the filter to a vacuum system comprised of a vacuum pump, vacuum gauge, resin reservoir and associated tubing and fittings. Resin is delivered to the filter in a controlled fashion. The total processing time for a 50-cubic feet per minute (cfm) filter is approximately 25 minutes. The resin filled HEPA filter is allowed to cure at ambient temperature and pressure for at least 24 hours. The final product is a solid monolith with less that 0.1 percent by volume of void space and 100 percent of the filter media coated with resin. Proof of concept studies have been completed using 50, 135, and 1000 cfm closed face HEPA filters. During these studies, we were able to develop and demonstrate a resin delivery process that yielded a final product that was suitable for in ground disposal. Additional adaptations of the process may be required for specific application, but the process equipment, supplies, and methodology have been fully established for contact handled, closed face
Unscented Kalman Filter for Brain-Machine Interfaces
Li, Zheng; O'Doherty, Joseph E.; Hanson, Timothy L.; Lebedev, Mikhail A.; Henriquez, Craig S.; Nicolelis, Miguel A. L.
2009-01-01
Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation. PMID:19603074
Sato, H; Ellis, G W; Inoué, S
1975-12-01
Meiosis I metaphase spindles were isolated from oocytes of the sea-star Pisaster ochraceus by a method that produced no detectable net loss in spindle birefringence. Some of the spindles were fixed immediately and embedded and sectioned for electron microscopy. Others were laminated between gelatine pellicles in a perfusion chamber, then fixed and sequentially and reversibly imbibed with a series of media of increasing refractive indices. Electron microscopy showed little else besides microtubules in the isolates, and no other component present could account for the observed form birefringence. An Ambronn plot of the birefringent retardation measured during imbibition was a good least squares fit to a computer generated theoretical curve based on the Bragg-Pippard rederivation of the Wiener curve for form birefringence. The data were best fit by the curve for rodlet index (n1) = 1.512, rodlet volume fraction (f) = 0.0206, and coefficient of intrinsic birefringence = 4.7 X 10(-5). The value obtained for n1 is unequivocal and is virtually as good as the refractometer determinations of imbibing medium index on which it is based. The optically interactive volume of the microtubule subunit, calculated from our electron microscope determination of spindle microtubule distribution (106/mum2), 13 protofilaments per microtubules, an 8 nm repeat distance and our best value for f, is compatible with known subunit dimensions as determined by other means. We also report curves fitted to the results of Ambronn imbibition of Bouin's-fixed Lytechinus spindles and to the Noll and Weber muscle imbibition data.
AN APPLICATION OF THE WIENER HERMITE EXPANSION TO THE NONLINEAR EVOLUTION OF DARK MATTER
Sugiyama, N. S.; Futamase, T.
2012-12-01
We apply the Wiener Hermite (WH) expansion to the nonlinear evolution of the large-scale structure and obtain an approximate expression for the matter power spectrum in the full order of the expansion. This method allows us to expand any random function in terms of an orthonormal basis in the space of random functions in such a way that the first order of the expansion expresses the Gaussian distribution, and others are the deviations from Gaussianity. It is proved that the WH expansion is mathematically equivalent to the {Gamma}-expansion approach in the renormalized perturbation theory (RPT). While exponential behavior in the high-k limit has been proved for the mass density and velocity fluctuations of dark matter in the RPT, we prove the behavior again in the context of the WH expansion using the result of the standard perturbation theory (SPT). We propose a new approximate expression for the matter power spectrum which interpolates the low-k expression corresponding to the 1-loop level in SPT and the high-k expression obtained by taking a high-k limit of the WH expansion. The validity of our prescription is specifically verified by comparing with the 2-loop solutions of the SPT. The proposed power spectrum agrees with the result of the N-body simulation with accuracy better than 1% or 2% in a range of baryon acoustic oscillation scales, where the wave number is about k = 0.2-0.4 h Mpc{sup -1} at z = 0.5-3.0. This accuracy is comparable to or slightly less than the ones in the closure theory, the fractional difference of which from the N-body result is within 1%. One merit of our method is that the computational time is very short because only single and double integrals are involved in our solution.
Holmes, B.L.; Janney, M.A.
1995-12-31
Filters were formed from ceramic fibers, organic fibers, and a ceramic bond phase using a papermaking technique. The distribution of particulate ceramic bond phase was determined using a model silicon carbide system. As the ceramic fiber increased in length and diameter the distance between particles decreased. The calculated number of particles per area showed good agreement with the observed value. After firing, the papers were characterized using a biaxial load test. The strength of papers was proportional to the amount of bond phase included in the paper. All samples exhibited strain-tolerant behavior.
NASA Astrophysics Data System (ADS)
Börger, Klaus; Schmidt, Michael; Dettmering, Denise; Limberger, Marco; Erdogan, Eren; Seitz, Florian; Brandert, Sylvia; Görres, Barbara; Kersten, Wilhelm; Bothmer, Volker; Hinrichs, Johannes; Venzmer, Malte; Mrotzek, Niclas
2016-04-01
Today, the observations of space geodetic techniques are usually available with a rather low latency which applies to space missions observing the solar terrestrial environment, too. Therefore, we can use all these measurements in near real-time to compute and to provide ionosphere information, e.g. the vertical total electron content (VTEC). GSSAC and BGIC support a project aiming at a service for providing ionosphere information. This project is called OPTIMAP, meaning "Operational Tool for Ionosphere Mapping and Prediction"; the scientific work is mainly done by the German Geodetic Research Institute of the Technical University Munich (DGFI-TUM) and the Institute for Astrophysics of the University of Goettingen (IAG). The OPTIMAP strategy for providing ionosphere target quantities of high quality, such as VTEC or the electron density, includes mathematical approaches and tools allowing for the model adaptation to the real observational scenario as a significant improvement w.r.t. the traditional well-established methods. For example, OPTIMAP combines different observation types such as GNSS (GPS, GLONASS), Satellite Altimetry (Jason-2), DORIS as well as radio-occultation measurements (FORMOSAT#3/COSMIC). All these observations run into a Kalman-filter to compute global ionosphere maps, i.e. VTEC, for the current instant of time and as a forecast for a couple of subsequent days. Mathematically, the global VTEC is set up as a series expansion in terms of two-dimensional basis functions defined as tensor products of trigonometric B-splines for longitude and polynomial B-splines for latitude. Compared to the classical spherical harmonics, B-splines have a localizing character and, therefore, can handle an inhomogeneous data distribution properly. Finally, B-splines enable a so-called multi-resolution-representation (MRR) enabling the combination of global and regional modelling approaches. In addition to the geodetic measurements, Sun observations are pre
Rocket noise filtering system using digital filters
NASA Technical Reports Server (NTRS)
Mauritzen, David
1990-01-01
A set of digital filters is designed to filter rocket noise to various bandwidths. The filters are designed to have constant group delay and are implemented in software on a general purpose computer. The Parks-McClellan algorithm is used. Preliminary tests are performed to verify the design and implementation. An analog filter which was previously employed is also simulated.
The effect of sine-Wiener noises on transition in a genotype selection model with time delays
NASA Astrophysics Data System (ADS)
Juan Ning, Li; Liu, Pei
2016-09-01
A genotype selection system interplay with sine-Wiener noises and time delays is investigated. Stationary probability distribution function is obtained by numerical simulations. Results show that the multiplicative bounded noise can facilitate the gene separation, while the additive bounded noise suppresses the gene separation. Besides, local time delays α and β, being in gene transformation and gene heredity progress respectively, play opposite roles in the gene selection process. What is more interesting is that there is no transition during the process of gene select when time delays α = β (i.e., the system is subjected to global time delay).
Lattice filter for processing image data of three-dimensional protein nanocrystals.
van Genderen, E; Li, Y W; Nederlof, I; Abrahams, J P
2016-01-01
When 300 kV cryo-EM images at Scherzer focus are acquired from ∼ 100 nm thick three-dimensional protein nanocrystals using a Falcon 2 direct electron detector, Fourier transformation can reveal the crystalline lattice to surprisingly high resolutions, even though the images themselves seem to be devoid of any contrast. Here, it is reported how this lattice information can be enhanced by means of a wave finder in combination with Wiener-type maximum-likelihood filtering. This procedure paves the way towards full three-dimensional structure determination at high resolution for protein crystals. PMID:26894532
Extracting tissue deformation using Gabor filter banks
NASA Astrophysics Data System (ADS)
Montillo, Albert; Metaxas, Dimitris; Axel, Leon
2004-04-01
This paper presents a new approach for accurate extraction of tissue deformation imaged with tagged MR. Our method, based on banks of Gabor filters, adjusts (1) the aspect and (2) orientation of the filter"s envelope and adjusts (3) the radial frequency and (4) angle of the filter"s sinusoidal grating to extract information about the deformation of tissue. The method accurately extracts tag line spacing, orientation, displacement and effective contrast. Existing, non-adaptive methods often fail to recover useful displacement information in the proximity of tissue boundaries while our method works in the proximity of the boundaries. We also present an interpolation method to recover all tag information at a finer resolution than the filter bank parameters. Results are shown on simulated images of translating and contracting tissue.
NASA Astrophysics Data System (ADS)
Fatalov, V. R.
2013-12-01
We prove results on exact asymptotics as T\\to\\infty for the means \\mathsf{E}_{a,c}\\exp\\bigl\\{-\\int_0^T g(\\mathbf{w}(t))\\,dt\\bigr\\} and probabilities \\mathsf{P}_{a,c}\\bigl\\{\\frac1T\\int_0^Tg(\\mathbf{w}(t))\\,dt\\lt d\\bigr\\}, where \\mathbf{w}(t)=(w_1(t),\\dots,w_n(t)), t\\ge 0, is an n-dimensional Wiener process, g(x) is a positive continuous function (potential) satisfying certain conditions, d\\gt 0, and a,c\\in{R}^n are prescribed vectors. The results are obtained by a new method developed in this paper, the Laplace method for the occupation time of a multi-dimensional Wiener process. We consider examples of monomial and radial potentials and prove results on exact asymptotics of small deviations for the probabilities \\mathsf{P}_0\\bigl\\{\\int_0^1\\sum_{j=1}^n\\vert w_j(t)\\vert^p\\,dt\\lt\\varepsilon^p\\bigr\\} as \\varepsilon\\to 0 with a fixed p\\gt 0.
Glottal space segmentation from motion estimation and Gabor filtering.
Mendez, A; Ismaili Alaoui, E M; García, B; Ibn-Elhaj, E; Ruiz, I
2009-01-01
Obtaining the glottal space segmentation is essential to characterize morphological disorders of vocal folds. In this study, the tested images are been acquired by direct optical inspection of the glottis using an endoscope and most of them are very poor quality. The application of motion estimation is very useful to segment the vocal folds endoscopic videos without user interaction. This approach involves three process steps: 1) Wiener motion estimator--to shift the measurement the next frame regarding to the current frame, and look for similarities between them. The best matching will accurate a shift equal to the displacement vector of the object; 2) Segmentation using motion estimation results and applying Gabor filtering; 3) Experimental results to demonstrate that the proposed method is effective. Our proposal works correctly with 95% of database test videos and it shows a great advance in design, and in the nearby future, a complete method to diagnose vocal folds pathologies.
Adaptive processing for LANDSAT data
NASA Technical Reports Server (NTRS)
Crane, R. B.; Reyer, J. F.
1975-01-01
Analytical and test results on the use of adaptive processing on LANDSAT data are presented. The Kalman filter was used as a framework to contain different adapting techniques. When LANDSAT MSS data were used all of the modifications made to the Kalman filter performed the functions for which they were designed. It was found that adaptive processing could provide compensation for incorrect signature means, within limits. However, if the data were such that poor classification accuracy would be obtained when the correct means were used, then adaptive processing would not improve the accuracy and might well lower it even further.
Modifying NL-means to a universal filter
NASA Astrophysics Data System (ADS)
Sun, Zhonggui; Chen, Songcan
2012-11-01
Despite a state-of-the-art filter for removing Gaussian noise, non-local means filter (NLM), like its local counterpart (the mean filter), is no longer so effective in removing salt-pepper noise which is common in real world as well. By contrast, adaptive median filter (AMF) is concise and can remove this type of noise effectively. Inspired by the AMF filtering strategies, in this paper, we modify NLM to a novel non-local universal filter (UNLM) which can remove not only either of Gaussian noise and salt-pepper noise but also their mixture. Experiments on artificial and benchmark images validate its feasibility and effectiveness.
Radar data smoothing filter study
NASA Technical Reports Server (NTRS)
White, J. V.
1984-01-01
The accuracy of the current Wallops Flight Facility (WFF) data smoothing techniques for a variety of radars and payloads is examined. Alternative data reduction techniques are given and recommendations are made for improving radar data processing at WFF. A data adaptive algorithm, based on Kalman filtering and smoothing techniques, is also developed for estimating payload trajectories above the atmosphere from noisy time varying radar data. This algorithm is tested and verified using radar tracking data from WFF.
Miniaturized dielectric waveguide filters
NASA Astrophysics Data System (ADS)
Sandhu, Muhammad Y.; Hunter, Ian C.
2016-10-01
Design techniques for a new class of integrated monolithic high-permittivity ceramic waveguide filters are presented. These filters enable a size reduction of 50% compared to air-filled transverse electromagnetic filters with the same unloaded Q-factor. Designs for Chebyshev and asymmetric generalised Chebyshev filter and a diplexer are presented with experimental results for an 1800 MHz Chebyshev filter and a 1700 MHz generalised Chebyshev filter showing excellent agreement with theory.
Heeringa, Amarins Nieske; van Dijk, Pim
2016-01-01
Noise-induced tinnitus and hyperacusis are thought to correspond to a disrupted balance between excitation and inhibition in the central auditory system. Excitation and inhibition are often studied using pure tones; however, these responses do not reveal inhibition within the excitatory pass band. Therefore, we used a Wiener-kernel analysis, complemented with singular value decomposition (SVD), to investigate the immediate effects of acoustic trauma on excitation and inhibition in the inferior colliculus (IC). Neural responses were recorded from the IC of three anesthetized albino guinea pigs before and immediately after a one-hour bilateral exposure to an 11-kHz tone of 124 dB SPL. Neural activity was recorded during the presentation of a 1-h continuous 70 dB SPL Gaussian-noise stimulus. Spike trains were subjected to Wiener-kernel analysis in which the second-order kernel was decomposed into excitatory and inhibitory components using SVD. Hearing thresholds between 3 and 22 kHz were elevated (13-47 dB) immediately after acoustic trauma. The presence and frequency tuning of excitation and inhibition in units with a low characteristic frequency (CF; < 3 kHz) was not affected, inhibition disappeared whereas excitation was not affected in mid-CF units (3 < CF < 11 kHz), and both excitation and inhibition disappeared in high-CF units (CF > 11 kHz). This specific differentiation could not be identified by tone-evoked receptive-field analysis, in which inhibitory responses disappeared in all units, along with excitatory responses in high-CF units. This study is the first to apply Wiener-kernel analysis, complemented with SVD, to study the effects of acoustic trauma on spike trains derived from the IC. With this analysis, a reduction of inhibition and preservation of good response thresholds was shown in mid-CF units immediately after acoustic trauma. These neurons may mediate noise-induced tinnitus and/or hyperacusis. Moreover, an immediate profound high
ERIC Educational Resources Information Center
Regional Educational Laboratory Mid-Atlantic, 2013
2013-01-01
In this REL Mid-Atlantic webinar, Dr. Ross Wiener, Vice President and Executive Director of the Education and Society Program, Aspen Institute, discussed strategies for integrating the Common Core State Standards (CCSS) into teacher effectiveness systems, including ways in which the CCSS can support professional growth and inform teacher…
Multiple-filter approach to phase retrieval from modulus data.
Frieden, B R; Oh, C
1992-03-10
A new approach is given for the problem of reconstruction of phase from modulus data. A set of Wiener-filter functions is formed that multiply, in turn, displaced versions of the modulus data (in frequency space) such that the sum is a minimum L(2)-error norm solution for the object. The modulus data are permitted to contain both noise and signal (object) components. The required statistics are power spectra of the signal and noise, and correlations between modulus data at given frequencies and complex object spectral values at adjacent frequencies. In a numerical simulation, a 3 x 3 filter array is used to reconstruct any member of an object class consisting of 16 pictures of space shuttles in various combinations. The 16 pictures are used as a learning set to form the required power spectra and correlations mentioned above. Reconstructions are formed in the presence of data noise, data gaps, and filter-construction noise, in varying amounts. Results are encouraging in that the space shuttle images are always recognizable.
HEPA filter dissolution process
Brewer, K.N.; Murphy, J.A.
1994-02-22
A process is described for dissolution of spent high efficiency particulate air (HEPA) filters and then combining the complexed filter solution with other radioactive wastes prior to calcining the mixed and blended waste feed. The process is an alternate to a prior method of acid leaching the spent filters which is an inefficient method of treating spent HEPA filters for disposal. 4 figures.
Hepa filter dissolution process
Brewer, Ken N.; Murphy, James A.
1994-01-01
A process for dissolution of spent high efficiency particulate air (HEPA) filters and then combining the complexed filter solution with other radioactive wastes prior to calcining the mixed and blended waste feed. The process is an alternate to a prior method of acid leaching the spent filters which is an inefficient method of treating spent HEPA filters for disposal.
Recirculating electric air filter
Bergman, W.
1985-01-09
An electric air filter cartridge has a cylindrical inner high voltage electrode, a layer of filter material, and an outer ground electrode formed of a plurality of segments moveably connected together. The outer electrode can be easily opened to remove or insert filter material. Air flows through the two electrodes and the filter material and is exhausted from the center of the inner electrode.
Recirculating electric air filter
Bergman, Werner
1986-01-01
An electric air filter cartridge has a cylindrical inner high voltage eleode, a layer of filter material, and an outer ground electrode formed of a plurality of segments moveably connected together. The outer electrode can be easily opened to remove or insert filter material. Air flows through the two electrodes and the filter material and is exhausted from the center of the inner electrode.
Karain, Wael
2016-10-01
The dynamics of a protein and the water surrounding it are coupled via nonbonded energy interactions. This coupling can exhibit a complex, nonlinear, and nonstationary nature. The THz frequency spectrum for this interaction energy characterizes both the vibration spectrum of the water hydrogen bond network, and the frequency range of large amplitude modes of proteins. We use a Recurrence Plot based Wiener-Khinchin method RPWK to calculate this spectrum, and the results are compared to those determined using the classical auto-covariance-based Wiener-Khinchin method WK. The frequency spectra for the total nonbonded interaction energy extracted from molecular dynamics simulations between the β-Lactamase Inhibitory Protein BLIP, and water molecules within a 10 Å distance from the protein surface, are calculated at 150, 200, 250, and 310 K, respectively. Similar calculations are also performed for the nonbonded interaction energy between the residues 49ASP, 53TYR, and 142PHE in BLIP, with water molecules within 10 Å from each residue respectively at 150, 200, 250, and 310 K. A comparison of the results shows that RPWK performs better than WK, and is able to detect some frequency data points that WK fails to detect. This points to the importance of using methods capable of taking the complex nature of the protein-solvent energy landscape into consideration, and not to rely on standard linear methods. In general, RPWK can be a valuable addition to the analysis tools for protein molecular dynamics simulations. Proteins 2016; 84:1549-1557. © 2016 Wiley Periodicals, Inc.
Quantization noise filtering in ADPCM systems
NASA Astrophysics Data System (ADS)
Gibson, J. D.
1980-08-01
Differential pulse code modulation (DPCM) systems utilizing adaptive quantizers and fixed or adaptive predictors are effective methods for voice encoding at data rates of 9.6 to 40 kbits/s. The principal performance limitation on these systems is the presence of quantization noise in the receiver output and the predictor feedback loop. An approach to reducing the quantization noise using sequential filtering methods based on estimation theory concepts is described. Several different filter structures are presented and the efficacy of the approach is illustrated via system simulations using actual speech. Signal-to-quantization noise ratio, sound spectrograms, and subjective listening tests are used for system performance evaluations.
Design of order statistics filters using feedforward neural networks
NASA Astrophysics Data System (ADS)
Maslennikova, Yu. S.; Bochkarev, V. V.
2016-08-01
In recent years significant progress have been made in the development of nonlinear data processing techniques. Such techniques are widely used in digital data filtering and image enhancement. Many of the most effective nonlinear filters based on order statistics. The widely used median filter is the best known order statistic filter. Generalized form of these filters could be presented based on Lloyd's statistics. Filters based on order statistics have excellent robustness properties in the presence of impulsive noise. In this paper, we present special approach for synthesis of order statistics filters using artificial neural networks. Optimal Lloyd's statistics are used for selecting of initial weights for the neural network. Adaptive properties of neural networks provide opportunities to optimize order statistics filters for data with asymmetric distribution function. Different examples demonstrate the properties and performance of presented approach.
ARRANGEMENT FOR REPLACING FILTERS
Blomgren, R.A.; Bohlin, N.J.C.
1957-08-27
An improved filtered air exhaust system which may be continually operated during the replacement of the filters without the escape of unfiltered air is described. This is accomplished by hermetically sealing the box like filter containers in a rectangular tunnel with neoprene covered sponge rubber sealing rings coated with a silicone impregnated pneumatic grease. The tunnel through which the filters are pushed is normal to the exhaust air duct. A number of unused filters are in line behind the filters in use, and are moved by a hydraulic ram so that a fresh filter is positioned in the air duct. The used filter is pushed into a waiting receptacle and is suitably disposed. This device permits a rapid and safe replacement of a radiation contaminated filter without interruption to the normal flow of exhaust air.
Method of securing filter elements
Brown, Erik P.; Haslam, Jeffery L.; Mitchell, Mark A.
2016-10-04
A filter securing system including a filter unit body housing; at least one tubular filter element positioned in the filter unit body housing, the tubular filter element having a closed top and an open bottom; a dimple in either the filter unit body housing or the top of the tubular filter element; and a socket in either the filter unit body housing or the top of the tubular filter element that receives the dimple in either the filter unit body housing or the top of the tubular filter element to secure the tubular filter element to the filter unit body housing.
A novel filter bank for biotelemetry.
Karagözoglu, B
2001-03-01
In a multichannel biotelemetry system, signals taken from a patient are distributed along the available frequency range (bandwidth) of the system through frequency-division-multiplexing, and combined into a single composite signal. Biological signals that are limited to low frequencies (below 10 Hz) modulate the frequencies of respective sub-carriers. Other biological signals are carried in amplitude-modulated forms. It is recognized that recovering original signals from a composite signal at the receiver side is a technical challenge when a telemetry system with narrow bandwidth capacity is used, since such a system leaves little frequency spacing between information channels. A filter bank is therefore utilized for recovering biological signals that are transmitted. The filter bank contains filter units comprising switched-capacitor filter integrated circuits. The filters have two distinct and opposing outputs (band-stop (notch) and band-pass). Since most biological signals are at low frequencies, and modulated signals occupy a narrow band around the carrier, notch filters can be used to efficiently stop signals in the narrow frequency range. Once the interim channels are removed, other channels become well separated from each other, and band-pass filters can select them. In the proposed system, efficient filtering of closely packed channels is achieved, with low interference, from neighboring channels. The filter bank is applied to a system that carries four biological signals and a battery status indicator signal. Experimental results reinforce theoretical predictions that the filter bank successfully de-multiplexes closely packed information channels with low crosstalk between them. It is concluded that the proposed filter bank allows utilization of cost-effective multichannel biotelemetry systems that are designed around commercial audio devices, and that it can be readily adapted to a broad range of physiological recording requirements. PMID:11458572
Chiang, Ta-Kuan; Straub, Douglas L.; Dennis, Richard A.
2000-01-01
The present invention involves a porous rigid filter including a plurality of concentric filtration elements having internal flow passages and forming external flow passages there between. The present invention also involves a pressure vessel containing the filter for the removal of particulates from high pressure particulate containing gases, and further involves a method for using the filter to remove such particulates. The present filter has the advantage of requiring fewer filter elements due to the high surface area-to-volume ratio provided by the filter, requires a reduced pressure vessel size, and exhibits enhanced mechanical design properties, improved cleaning properties, configuration options, modularity and ease of fabrication.
Filter type gas sampler with filter consolidation
Miley, Harry S.; Thompson, Robert C.; Hubbard, Charles W.; Perkins, Richard W.
1997-01-01
Disclosed is an apparatus for automatically consolidating a filter or, more specifically, an apparatus for drawing a volume of gas through a plurality of sections of a filter, whereafter the sections are subsequently combined for the purpose of simultaneously interrogating the sections to detect the presence of a contaminant.
Filter type gas sampler with filter consolidation
Miley, H.S.; Thompson, R.C.; Hubbard, C.W.; Perkins, R.W.
1997-03-25
Disclosed is an apparatus for automatically consolidating a filter or, more specifically, an apparatus for drawing a volume of gas through a plurality of sections of a filter, where after the sections are subsequently combined for the purpose of simultaneously interrogating the sections to detect the presence of a contaminant. 5 figs.
Physically constrained maximum likelihood mode filtering.
Papp, Joseph C; Preisig, James C; Morozov, Andrey K
2010-04-01
Mode filtering is most commonly implemented using the sampled mode shapes or pseudoinverse algorithms. Buck et al. [J. Acoust. Soc. Am. 103, 1813-1824 (1998)] placed these techniques in the context of a broader maximum a posteriori (MAP) framework. However, the MAP algorithm requires that the signal and noise statistics be known a priori. Adaptive array processing algorithms are candidates for improving performance without the need for a priori signal and noise statistics. A variant of the physically constrained, maximum likelihood (PCML) algorithm [A. L. Kraay and A. B. Baggeroer, IEEE Trans. Signal Process. 55, 4048-4063 (2007)] is developed for mode filtering that achieves the same performance as the MAP mode filter yet does not need a priori knowledge of the signal and noise statistics. The central innovation of this adaptive mode filter is that the received signal's sample covariance matrix, as estimated by the algorithm, is constrained to be that which can be physically realized given a modal propagation model and an appropriate noise model. Shallow water simulation results are presented showing the benefit of using the PCML method in adaptive mode filtering.
HEPA Filter Vulnerability Assessment
GUSTAVSON, R.D.
2000-05-11
This assessment of High Efficiency Particulate Air (HEPA) filter vulnerability was requested by the USDOE Office of River Protection (ORP) to satisfy a DOE-HQ directive to evaluate the effect of filter degradation on the facility authorization basis assumptions. Within the scope of this assessment are ventilation system HEPA filters that are classified as Safety-Class (SC) or Safety-Significant (SS) components that perform an accident mitigation function. The objective of the assessment is to verify whether HEPA filters that perform a safety function during an accident are likely to perform as intended to limit release of hazardous or radioactive materials, considering factors that could degrade the filters. Filter degradation factors considered include aging, wetting of filters, exposure to high temperature, exposure to corrosive or reactive chemicals, and exposure to radiation. Screening and evaluation criteria were developed by a site-wide group of HVAC engineers and HEPA filter experts from published empirical data. For River Protection Project (RPP) filters, the only degradation factor that exceeded the screening threshold was for filter aging. Subsequent evaluation of the effect of filter aging on the filter strength was conducted, and the results were compared with required performance to meet the conditions assumed in the RPP Authorization Basis (AB). It was found that the reduction in filter strength due to aging does not affect the filter performance requirements as specified in the AB. A portion of the HEPA filter vulnerability assessment is being conducted by the ORP and is not part of the scope of this study. The ORP is conducting an assessment of the existing policies and programs relating to maintenance, testing, and change-out of HEPA filters used for SC/SS service. This document presents the results of a HEPA filter vulnerability assessment conducted for the River protection project as requested by the DOE Office of River Protection.
Cordierite silicon nitride filters
Sawyer, J.; Buchan, B. ); Duiven, R.; Berger, M. ); Cleveland, J.; Ferri, J. )
1992-02-01
The objective of this project was to develop a silicon nitride based crossflow filter. This report summarizes the findings and results of the project. The project was phased with Phase I consisting of filter material development and crossflow filter design. Phase II involved filter manufacturing, filter testing under simulated conditions and reporting the results. In Phase I, Cordierite Silicon Nitride (CSN) was developed and tested for permeability and strength. Target values for each of these parameters were established early in the program. The values were met by the material development effort in Phase I. The crossflow filter design effort proceeded by developing a macroscopic design based on required surface area and estimated stresses. Then the thermal and pressure stresses were estimated using finite element analysis. In Phase II of this program, the filter manufacturing technique was developed, and the manufactured filters were tested. The technique developed involved press-bonding extruded tiles to form a filter, producing a monolithic filter after sintering. Filters manufactured using this technique were tested at Acurex and at the Westinghouse Science and Technology Center. The filters did not delaminate during testing and operated and high collection efficiency and good cleanability. Further development in areas of sintering and filter design is recommended.
HEPA filter monitoring program
NASA Astrophysics Data System (ADS)
Kirchner, K. N.; Johnson, C. M.; Aiken, W. F.; Lucerna, J. J.; Barnett, R. L.; Jensen, R. T.
1986-07-01
The testing and replacement of HEPA filters, widely used in the nuclear industry to purify process air, are costly and labor-intensive. Current methods of testing filter performance, such as differential pressure measurement and scanning air monitoring, allow determination of overall filter performance but preclude detection of incipient filter failure such as small holes in the filters. Using current technology, a continual in-situ monitoring system was designed which provides three major improvements over current methods of filter testing and replacement. The improvements include: cost savings by reducing the number of intact filters which are currently being replaced unnecessarily; more accurate and quantitative measurement of filter performance; and reduced personnel exposure to a radioactive environment by automatically performing most testing operations.
L.V. Chekalov; Yu.I. Gromov; V.V. Chekalov
2007-05-15
Cleaning of TPP flue gases with bag filters capable of pulsed regeneration is examined. A new filtering element with a three-dimensional filtering material formed from a needle-broached cloth in which the filtration area, as compared with a conventional smooth bag, is increased by more than two times, is proposed. The design of a new FRMI type of modular filter is also proposed. A standard series of FRMI filters with a filtration area ranging from 800 to 16,000 m{sup 2} is designed for an output more than 1 million m{sub 3}/h of with respect to cleaned gas. The new bag filter permits dry collection of sulfur oxides from waste gases at TPP operating on high-sulfur coals. The design of the filter makes it possible to replace filter elements without taking the entire unit out of service.
A recursive filter for despeckling SAR images.
Subrahmanyam, G R K S; Rajagopalan, A N; Aravind, R
2008-10-01
This correspondence proposes a recursive algorithm for noise reduction in synthetic aperture radar imagery. Excellent despeckling in conjunction with feature preservation is achieved by incorporating a discontinuity-adaptive Markov random field prior within the unscented Kalman filter framework through importance sampling. The performance of this method is demonstrated on both synthetic and real examples.
Novel Backup Filter Device for Candle Filters
Bishop, B.; Goldsmith, R.; Dunham, G.; Henderson, A.
2002-09-18
The currently preferred means of particulate removal from process or combustion gas generated by advanced coal-based power production processes is filtration with candle filters. However, candle filters have not shown the requisite reliability to be commercially viable for hot gas clean up for either integrated gasifier combined cycle (IGCC) or pressurized fluid bed combustion (PFBC) processes. Even a single candle failure can lead to unacceptable ash breakthrough, which can result in (a) damage to highly sensitive and expensive downstream equipment, (b) unacceptably low system on-stream factor, and (c) unplanned outages. The U.S. Department of Energy (DOE) has recognized the need to have fail-safe devices installed within or downstream from candle filters. In addition to CeraMem, DOE has contracted with Siemens-Westinghouse, the Energy & Environmental Research Center (EERC) at the University of North Dakota, and the Southern Research Institute (SRI) to develop novel fail-safe devices. Siemens-Westinghouse is evaluating honeycomb-based filter devices on the clean-side of the candle filter that can operate up to 870 C. The EERC is developing a highly porous ceramic disk with a sticky yet temperature-stable coating that will trap dust in the event of filter failure. SRI is developing the Full-Flow Mechanical Safeguard Device that provides a positive seal for the candle filter. Operation of the SRI device is triggered by the higher-than-normal gas flow from a broken candle. The CeraMem approach is similar to that of Siemens-Westinghouse and involves the development of honeycomb-based filters that operate on the clean-side of a candle filter. The overall objective of this project is to fabricate and test silicon carbide-based honeycomb failsafe filters for protection of downstream equipment in advanced coal conversion processes. The fail-safe filter, installed directly downstream of a candle filter, should have the capability for stopping essentially all particulate
Poirier, M. R.; Burket, P. R.; Duignan, M. R.
2015-03-12
The Savannah River Site (SRS) is currently treating radioactive liquid waste with the Actinide Removal Process (ARP) and the Modular Caustic Side Solvent Extraction Unit (MCU). The low filter flux through the ARP has limited the rate at which radioactive liquid waste can be treated. Recent filter flux has averaged approximately 5 gallons per minute (gpm). Salt Batch 6 has had a lower processing rate and required frequent filter cleaning. Savannah River Remediation (SRR) has a desire to understand the causes of the low filter flux and to increase ARP/MCU throughput. In addition, at the time the testing started, SRR was assessing the impact of replacing the 0.1 micron filter with a 0.5 micron filter. This report describes testing of MST filterability to investigate the impact of filter pore size and MST particle size on filter flux and testing of filter enhancers to attempt to increase filter flux. The authors constructed a laboratory-scale crossflow filter apparatus with two crossflow filters operating in parallel. One filter was a 0.1 micron Mott sintered SS filter and the other was a 0.5 micron Mott sintered SS filter. The authors also constructed a dead-end filtration apparatus to conduct screening tests with potential filter aids and body feeds, referred to as filter enhancers. The original baseline for ARP was 5.6 M sodium salt solution with a free hydroxide concentration of approximately 1.7 M.3 ARP has been operating with a sodium concentration of approximately 6.4 M and a free hydroxide concentration of approximately 2.5 M. SRNL conducted tests varying the concentration of sodium and free hydroxide to determine whether those changes had a significant effect on filter flux. The feed slurries for the MST filterability tests were composed of simple salts (NaOH, NaNO_{2}, and NaNO_{3}) and MST (0.2 – 4.8 g/L). The feed slurry for the filter enhancer tests contained simulated salt batch 6 supernate, MST, and filter enhancers.
NASA Technical Reports Server (NTRS)
Nagle, H. T., Jr.
1972-01-01
A three part survey is made of the state-of-the-art in digital filtering. Part one presents background material including sampled data transformations and the discrete Fourier transform. Part two, digital filter theory, gives an in-depth coverage of filter categories, transfer function synthesis, quantization and other nonlinear errors, filter structures and computer aided design. Part three presents hardware mechanization techniques. Implementations by general purpose, mini-, and special-purpose computers are presented.
Sellers, Cheryl L.; Nordyke, Daniel S.; Crandell, Richard A.; Tomlins, Gregory; Fei, Dong; Panov, Alexander; Lane, William H.; Habeger, Craig F.
2008-12-09
According to an exemplary embodiment of the present disclosure, a system for removing matter from a filtering device includes a gas pressurization assembly. An element of the assembly is removably attachable to a first orifice of the filtering device. The system also includes a vacuum source fluidly connected to a second orifice of the filtering device.
Practical Active Capacitor Filter
NASA Technical Reports Server (NTRS)
Shuler, Robert L., Jr. (Inventor)
2005-01-01
A method and apparatus is described that filters an electrical signal. The filtering uses a capacitor multiplier circuit where the capacitor multiplier circuit uses at least one amplifier circuit and at least one capacitor. A filtered electrical signal results from a direct connection from an output of the at least one amplifier circuit.
Gates-Anderson, Dianne D.; Kidd, Scott D.; Bowers, John S.; Attebery, Ronald W.
2003-01-01
A low viscosity resin is delivered into a spent HEPA filter or other waste. The resin is introduced into the filter or other waste using a vacuum to assist in the mass transfer of the resin through the filter media or other waste.