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.
Adaptive clutter filter in 2-D color flow imaging based on in vivo I/Q signal.
Zhou, Xiaoming; Zhang, Congyao; Liu, Dong C
2014-01-01
Color flow imaging has been well applied in clinical diagnosis. For the high quality color flow images, clutter filter is important to separate the Doppler signals from blood and tissue. Traditional clutter filters, such as finite impulse response, infinite impulse response and regression filters, were applied, which are based on the hypothesis that the clutter signal is stationary or tissue moves slowly. However, in realistic clinic color flow imaging, the signals are non-stationary signals because of accelerated moving tissue. For most related papers, simulated RF signals are widely used without in vivo I/Q signal. Hence, in this paper, adaptive polynomial regression filter, which is down mixing with instantaneous clutter frequency, was proposed based on in vivo carotid I/Q signal in realistic color flow imaging. To get the best performance, the optimal polynomial order of polynomial regression filter and the optimal polynomial order for estimation of instantaneous clutter frequency respectively were confirmed. Finally, compared with the mean blood velocity and quality of 2-D color flow image, the experiment results show that adaptive polynomial regression filter, which is down mixing with instantaneous clutter frequency, can significantly enhance the mean blood velocity and get high quality 2-D color flow image.
Adaptive clutter rejection for ultrasound color Doppler imaging
NASA Astrophysics Data System (ADS)
Yoo, Yang Mo; Managuli, Ravi; Kim, Yongmin
2005-04-01
We have developed a new adaptive clutter rejection technique where an optimum clutter filter is dynamically selected according to the varying clutter characteristics in ultrasound color Doppler imaging. The selection criteria have been established based on the underlying clutter characteristics (i.e., the maximum instantaneous clutter velocity and the clutter power) and the properties of various candidate clutter filters (e.g., projection-initialized infinite impulse response and polynomial regression). We obtained an average improvement of 3.97 dB and 3.27 dB in flow signal-to-clutter-ratio (SCR) compared to the conventional and down-mixing methods, respectively. These preliminary results indicate that the proposed adaptive clutter rejection method could improve the sensitivity and accuracy in flow velocity estimation for ultrasound color Doppler imaging. For a 192 x 256 color Doppler image with an ensemble size of 10, the proposed method takes only 57.2 ms, which is less than the acquisition time. Thus, the proposed method could be implemented in modern ultrasound systems, while providing improved clutter rejection and more accurate velocity estimation in real time.
Signal processing techniques for clutter filtering and wind shear detection
NASA Technical Reports Server (NTRS)
Baxa, Ernest G., Jr.; Deshpande, Manohar D
1991-01-01
An extended Prony algorithm applicable to signal processing techniques for clutter filtering and windshear detection is discussed. The algorithm is based upon modelling the radar return as a time series, and appears to offer potential for improving hazard factor estimates in the presence of strong clutter returns.
Subaperture clutter filter with CFAR signal detection
Ormesher, Richard C.; Naething, Richard M.
2016-08-30
The various technologies presented herein relate to the determination of whether a received signal comprising radar clutter further comprises a communication signal. The communication signal can comprise of a preamble, a data symbol, communication data, etc. A first portion of the radar clutter is analyzed to determine a radar signature of the first portion of the radar clutter. A second portion of the radar clutter can be extracted based on the radar signature of the first portion. Following extraction, any residual signal can be analyzed to retrieve preamble data, etc. The received signal can be based upon a linear frequency modulation (e.g., a chirp modulation) whereby the chirp frequency can be determined and the frequency of transmission of the communication signal can be based accordingly thereon. The duration and/or bandwidth of the communication signal can be a portion of the duration and/or the bandwidth of the radar clutter.
Clutter suppression using recursive and nonrecursive MTI filters
NASA Astrophysics Data System (ADS)
Liu, Bede
1988-05-01
The author calculates the effectiveness of clutter suppression of a moving-target-indicator (MTI) filter in tandem with a fast-Fourier-transform (FFT) Doppler filter bank, taking into account the transient response of the MTI filter. Both recursive and nonrecursive filters are considered. The analysis is extended to the high-pulse-repetition-frequency (PRF) case with clutter fold over. The results can be used to select key design parameters, including the MTI filter, the window size, and the initial transient segment to be discarded. Numerical examples are included.
Clutter filtering influence on blood velocity estimation using speckle tracking.
Fadnes, Solveig; Bjærum, Steinar; Torp, Hans; Lovstakken, Lasse
2015-12-01
Blood speckle tracking has shown potential for solving the angle-dependency limitation in color flow imaging. However, as clutter filtering is still Doppler-based, flow velocities at near-perpendicular beam-to-flow angles can be severely attenuated. It is shown that the clutter filter also alters the speckle appearance through a decrease in the lateral imaging bandwidth, leading to poorer lateral resolution and thus tracking performance. Interestingly, at perpendicular beam-to-flow angles lateral band-pass characteristics are inferred, and the resulting lateral amplitude modulation could help improve tracking estimates. Simulations and flow phantom experiments showed that substantially improved results could be achieved by utilizing time-variant clutter filters (e.g., polynomial regression filters) despite the inherent decorrelation inferred by these filters, but only for higher ensemble sizes (N > 36). We found that, compared with color flow imaging, speckle tracking could yield consistent estimates well below the clutter filter cutoff, but with a higher variance attributed to the low signalto- noise ratio inferred by filter attenuation. Overall, provided that a low f-number and high ensemble lengths (N approx. > 36) can be used, speckle tracking can consistently provide angle- independent flow velocity estimates, limited only by a lower bound on the flow velocity itself.
Adaptive clutter rejection for 3D color Doppler imaging: preliminary clinical study.
Yoo, Yang Mo; Sikdar, Siddhartha; Karadayi, Kerem; Kolokythas, Orpheus; Kim, Yongmin
2008-08-01
In three-dimensional (3D) ultrasound color Doppler imaging (CDI), effective rejection of flash artifacts caused by tissue motion (clutter) is important for improving sensitivity in visualizing blood flow in vessels. Since clutter characteristics can vary significantly during volume acquisition, a clutter rejection technique that can adapt to the underlying clutter conditions is desirable for 3D CDI. We have previously developed an adaptive clutter rejection (ACR) method, in which an optimum filter is dynamically selected from a set of predesigned clutter filters based on the measured clutter characteristics. In this article, we evaluated the ACR method with 3D in vivo data acquired from 37 kidney transplant patients clinically indicated for a duplex ultrasound examination. We compared ACR against a conventional clutter rejection method, down-mixing (DM), using a commonly-used flow signal-to-clutter ratio (SCR) and a new metric called fractional residual clutter area (FRCA). The ACR method was more effective in removing the flash artifacts while providing higher sensitivity in detecting blood flow in the arcuate arteries and veins in the parenchyma of transplanted kidneys. ACR provided 3.4 dB improvement in SCR over the DM method (11.4 +/- 1.6 dB versus 8.0 +/- 2.0 dB, p < 0.001) and had lower average FRCA values compared with the DM method (0.006 +/- 0.003 versus 0.036 +/- 0.022, p < 0.001) for all study subjects. These results indicate that the new ACR method is useful for removing nonstationary tissue motion while improving the image quality for visualizing 3D vascular structure in 3D CDI.
Effect of jamming on efficiency of anti-clutter polarimetric filter
NASA Astrophysics Data System (ADS)
Michel, G. E.; Durand, J. C.; Carrara, B.
The degradation caused by two different types of confusion jamming on the efficiency of an anticlutter adaptive polarimetric filter is described. The considered radar receives incident fields in two channels with orthogonal polarizations (1 and 2), and transmitting is carried out with polarization 1. The effect of jamming is evaluated through the degradation factor (the ratio of the residual powers at the output of the filter with and without jamming). The constant polarization jammer (CPJ) located in an antenna sidelobe has the same effect as an equivalent virtual jammer (EVJ) with the same polarization degree, with its power weighted by the antenna gains ratio, and whose polarization is a function of the polarization of the jammed lobe. In the absence of clutter, the fully polarized CPJ is completely rejected. In the presence of clutter, the fully polarized CPJ causes a rejection degradation characterized by the degradation factor, which depends on the angle between the clutter and the EVJ polarizations in the POINCARE ball.
New adaptive clutter rejection for ultrasound color Doppler imaging: in vivo study.
Yoo, Yang Mo; Kim, Yongmin
2010-03-01
Clutter rejection is essential for accurate flow estimation in ultrasound color Doppler imaging. In this article, we present a new adaptive clutter rejection (ACR) technique where an optimum filter is dynamically selected depending upon the underlying clutter characteristics (e.g., tissue acceleration and power). We compared the performance of the ACR method with other adaptive methods, i.e., down-mixing (DM) and adaptive clutter filtering (ACF), using in vivo data acquired from the kidney, liver and common carotid artery. With the kidney data, the ACR method provided an average improvement of 3.05 dB and 1.7 dB in flow signal-to-clutter ratio (SCR) compared with DM and ACF, respectively. With the liver data, SCR was improved by 2.75 dB and 1.8 dB over DM and ACF while no significant improvement with ACR was found in the common carotid artery data. Thus, the proposed adaptive method could provide more accurate flow estimation by improving clutter rejection in abdominal ultrasound color Doppler imaging pending validation.
A GPU-Parallelized Eigen-Based Clutter Filter Framework for Ultrasound Color Flow Imaging.
Chee, Adrian J Y; Yiu, Billy Y S; Yu, Alfred C H
2017-01-01
Eigen-filters with attenuation response adapted to clutter statistics in color flow imaging (CFI) have shown improved flow detection sensitivity in the presence of tissue motion. Nevertheless, its practical adoption in clinical use is not straightforward due to the high computational cost for solving eigendecompositions. Here, we provide a pedagogical description of how a real-time computing framework for eigen-based clutter filtering can be developed through a single-instruction, multiple data (SIMD) computing approach that can be implemented on a graphical processing unit (GPU). Emphasis is placed on the single-ensemble-based eigen-filtering approach (Hankel singular value decomposition), since it is algorithmically compatible with GPU-based SIMD computing. The key algebraic principles and the corresponding SIMD algorithm are explained, and annotations on how such algorithm can be rationally implemented on the GPU are presented. Real-time efficacy of our framework was experimentally investigated on a single GPU device (GTX Titan X), and the computing throughput for varying scan depths and slow-time ensemble lengths was studied. Using our eigen-processing framework, real-time video-range throughput (24 frames/s) can be attained for CFI frames with full view in azimuth direction (128 scanlines), up to a scan depth of 5 cm ( λ pixel axial spacing) for slow-time ensemble length of 16 samples. The corresponding CFI image frames, with respect to the ones derived from non-adaptive polynomial regression clutter filtering, yielded enhanced flow detection sensitivity in vivo, as demonstrated in a carotid imaging case example. These findings indicate that the GPU-enabled eigen-based clutter filtering can improve CFI flow detection performance in real time.
A GPU-Parallelized Eigen-Based Clutter Filter Framework for Ultrasound Color Flow Imaging.
Chee, Adrian; Yiu, Billy; Yu, Alfred
2016-09-07
Eigen-filters with attenuation response adapted to clutter statistics in color flow imaging (CFI) have shown improved flow detection sensitivity in the presence of tissue motion. Nevertheless, its practical adoption in clinical use is not straightforward due to the high computational cost for solving eigen-decompositions. Here, we provide a pedagogical description of how a real-time computing framework for eigen-based clutter filtering can be developed through a single-instruction, multiple data (SIMD) computing approach that can be implemented on a graphical processing unit (GPU). Emphasis is placed on the single-ensemble-based eigen-filtering approach (Hankel-SVD) since it is algorithmically compatible with GPU-based SIMD computing. The key algebraic principles and the corresponding SIMD algorithm are explained, and annotations on how such algorithm can be rationally implemented on the GPU are presented. Real-time efficacy of our framework was experimentally investigated on a single GPU device (GTX Titan X), and the computing throughput for varying scan depths and slow-time ensemble lengths were studied. Using our eigenprocessing framework, real-time video-range throughput (24 fps) can be attained for CFI frames with full-view in azimuth direction (128 scanlines), up to a scan depth of 5 cm (λ pixel axial spacing) for slow-time ensemble length of 16 samples. The corresponding CFI image frames, with respect to the ones derived from non-adaptive polynomial regression clutter filtering, yielded enhanced flow detection sensitivity in vivo, as demonstrated in a carotid imaging case example. These findings indicate that GPU-enabled eigen-based clutter filtering can improve CFI flow detection performance in real time.
Adaptive multispectral image processing for the detection of targets in terrain clutter
NASA Astrophysics Data System (ADS)
Hoff, Lawrence E.; Zeidler, James R.; Yerkes, Christopher R.
1992-08-01
In passive detection of small infrared targets in image data, we are faced with the difficult task of enhancing some characteristic of the target or signal while suppressing the clutter or background image noise. We reported that an effective means by which targets may be identified is to exploit characteristics which exist between scenes measured in different bands in the long wave infrared region of the electromagnetic spectrum. These methods are broadly termed multispectral techniques. In this paper we present a method by which a two- dimensional least-mean square adaptive filter is used to distinguish between target and clutter using multispectral techniques.
NASA Technical Reports Server (NTRS)
Jamora, Dennis A.
1993-01-01
Ground clutter interference is a major problem for airborne pulse Doppler radar operating at low altitudes in a look-down mode. With Doppler zero set at the aircraft ground speed, ground clutter rejection filtering is typically accomplished using a high-pass filter with real valued coefficients and a stopband notch centered at zero Doppler. Clutter spectra from the NASA Wind Shear Flight Experiments of l991-1992 show that the dominant clutter mode can be located away from zero Doppler, particularly at short ranges dominated by sidelobe returns. Use of digital notch filters with complex valued coefficients so that the stopband notch can be located at any Doppler frequency is investigated. Several clutter mode tracking algorithms are considered to estimate the Doppler frequency location of the dominant clutter mode. From the examination of night data, when a dominant clutter mode away from zero Doppler is present, complex filtering is able to significantly increase clutter rejection over use of a notch filter centered at zero Doppler.
NASA Astrophysics Data System (ADS)
Shin, Junseob; Huang, Lianjie
2016-04-01
Frequency-space prediction filtering (FXPF), also known as FX deconvolution, is a technique originally developed for random noise attenuation in seismic imaging. FXPF attempts to reduce random noise in seismic data by modeling only real signals that appear as linear or quasilinear events in the aperture domain. In medical ultrasound imaging, channel radio frequency (RF) signals from the main lobe appear as horizontal events after receive delays are applied while acoustic clutter signals from off-axis scatterers and electronic noise do not. Therefore, FXPF is suitable for preserving only the main-lobe signals and attenuating the unwanted contributions from clutter and random noise in medical ultrasound imaging. We adapt FXPF to ultrasound imaging, and evaluate its performance using simulated data sets from a point target and an anechoic cyst. Our simulation results show that using only 5 iterations of FXPF achieves contrast-to-noise ratio (CNR) improvements of 67 % in a simulated noise-free anechoic cyst and 228 % in a simulated anechoic cyst contaminated with random noise of 15 dB signal-to-noise ratio (SNR). Our findings suggest that ultrasound imaging with FXPF attenuates contributions from both acoustic clutter and random noise and therefore, FXPF has great potential to improve ultrasound image contrast for better visualization of important anatomical structures and detection of diseased conditions.
Geunyong Park; Sunmi Yeo; Jae Jin Lee; Changhan Yoon; Hyun-Woo Koh; Hyungjoon Lim; Youngtae Kim; Hwan Shim; Yangmo Yoo
2014-01-01
Effective rejection of time-varying clutter originating from slowly moving vessels and surrounding tissues is important for depicting hemodynamics in ultrasound color Doppler imaging (CDI). In this paper, a new adaptive clutter rejection method based on spectral analysis (ACR-SA) is presented for suppressing nonstationary clutter. In ACR-SA, tissue and flow characteristics are analyzed by singular value decomposition and tissue acceleration of backscattered Doppler signals to determine an appropriate clutter filter from a set of clutter filters. To evaluate the ACR-SA method, 20 frames of complex baseband data were acquired by a commercial ultrasound system equipped with a research package (Accuvix V10, Samsung Medison, Seoul, Korea) using a 3.5-MHz convex array probe by introducing tissue movements to the flow phantom (Gammex 1425 A LE, Gammex, Middleton, WI, USA). In addition, 20 frames of in vivo abdominal data from five volunteers were captured. From the phantom experiment, the ACR-SA method provided 2.43 dB (p <; 0.001) and 1.09 dB ( ) improvements in flow signal-to-clutter ratio (SCR) compared to static (STA) and down-mixing (ACR-DM) methods. Similarly, it showed smaller values in fractional residual clutter area (FRCA) compared to the STA and ACR-DM methods (i.e., 2.3% versus 5.4% and 3.7%, respectively, ). The consistent improvements in SCR from the proposed ACR-SA method were obtained with the in vivo abdominal data (i.e., 4.97 dB and 3.39 dB over STA and ACR-DM, respectively). The ACR-SA method showed less than 1% FRCA values for all in vivo abdominal data. These results indicate that the proposed ACR-SA method can improve image quality in CDI by providing enhanced rejection of nonstationary clutter.
Spatial Prediction Filtering of Acoustic Clutter and Random Noise in Medical Ultrasound Imaging.
Shin, Junseob; Huang, Lianjie
2017-02-01
One of the major challenges in array-based medical ultrasound imaging is the image quality degradation caused by sidelobes and off-axis clutter, which is an inherent limitation of the conventional delay-and-sum (DAS) beamforming operating on a finite aperture. Ultrasound image quality is further degraded in imaging applications involving strong tissue attenuation and/or low transmit power. In order to effectively suppress acoustic clutter from off-axis targets and random noise in a robust manner, we introduce in this paper a new adaptive filtering technique called frequency-space (F-X) prediction filtering or FXPF, which was first developed in seismic imaging for random noise attenuation. Seismologists developed FXPF based on the fact that linear and quasilinear events or wavefronts in the time-space (T-X) domain are manifested as a superposition of harmonics in the frequency-space (F-X) domain, which can be predicted using an auto-regressive (AR) model. We describe the FXPF technique as a spectral estimation or a direction-of-arrival problem, and explain why adaptation of this technique into medical ultrasound imaging is beneficial. We apply our new technique to simulated and tissue-mimicking phantom data. Our results demonstrate that FXPF achieves CNR improvements of 26% in simulated noise-free anechoic cyst, 109% in simulated anechoic cyst contaminated with random noise of 15 dB SNR, and 93% for experimental anechoic cyst from a custom-made tissue-mimicking phantom. Our findings suggest that FXPF is an effective technique to enhance ultrasound image contrast and has potential to improve the visualization of clinically important anatomical structures and diagnosis of diseased conditions.
NASA Astrophysics Data System (ADS)
Zhao, X. F.; Huang, S. X.; Wang, D. X.
2012-11-01
This paper addresses the problem of estimating range-varying parameters of the height-dependent refractivity over the sea surface from radar sea clutter. In the forward simulation, the split-step Fourier parabolic equation (PE) is used to compute the radar clutter power in the complex refractive environments. Making use of the inherent Markovian structure of the split-step Fourier PE solution, the refractivity from clutter (RFC) problem is formulated within a nonlinear recursive Bayesian state estimation framework. Particle filter (PF), which is a technique for implementing a recursive Bayesian filter by Monte Carlo simulations, is used to track range-varying characteristics of the refractivity profiles. Basic ideas of employing PF to solve RFC problem are introduced. Both simulation and real data results are presented to confirm the feasibility of PF-RFC performances.
NASA Astrophysics Data System (ADS)
Zhao, X. F.; Huang, S. X.
2012-08-01
This paper addresses the problem of estimating range-varying parameters of the height-dependent refractivity over the sea surface from radar sea clutter. In the forward simulation, the split-step Fourier parabolic equation (PE) is used to compute the radar clutter power in the complex refractive environments. Making use of the inherent Markovian structure of the split-step Fourier PE solution, the refractivity from clutter (RFC) problem is formulated within a nonlinear recursive Bayesian state estimation framework. Particle filter (PF) that is a technique for implementing a recursive Bayesian filter by Monte Carlo simulations is used to track range-varying characteristics of the refractivity profiles. Basic ideas of employing PF to solve RFC problem are introduced. Both simulation and real data results are presented to check up the feasibility of PF-RFC performances.
Adaptive clutter rejection for ultrasound color flow imaging based on recursive eigendecomposition.
You, Wei; Wang, Yuanyuan
2009-10-01
In the conventional eigenfilter used to reject clutter components of ultrasound color flow imaging, input samples are required to be statistically stationary. However, clutter movements may vary over the depth of the imaged area, which makes the eigenfilter less efficient. In the current study, a novel clutter rejection method is proposed based on the recursive eigendecomposition algorithm. In this method, the current eigenvector matrix of the ultrasound echo correlation matrix, which will be used to construct the clutter subspace, is determined by previous eigenvector matrices and the current input. After the estimated clutter signal is obtained by projecting the original input into the clutter space, each filtered output is eventually obtained by subtracting the estimated clutter signal from the original input. This procedure is iterated for each sample volume along the depth. During the updating process, a forgetting factor is introduced to determine proper weights for different inputs. Simulated data in 3 situations and in vivo data collected from human carotid arteries are used to compare the proposed method with other popular clutter filters. Results show that the proposed method can achieve the most accurate velocity profiles in all simulation situations and introduces the fewest velocity artifacts in the tissue region in the in vivo experiment.
The effects of clutter-rejection filtering on estimating weather spectrum parameters
NASA Technical Reports Server (NTRS)
Davis, W. T.
1989-01-01
The effects of clutter-rejection filtering on estimating the weather parameters from pulse Doppler radar measurement data are investigated. The pulse pair method of estimating the spectrum mean and spectrum width of the weather is emphasized. The loss of sensitivity, a measure of the signal power lost due to filtering, is also considered. A flexible software tool developed to investigate these effects is described. It allows for simulated weather radar data, in which the user specifies an underlying truncated Gaussian spectrum, as well as for externally generated data which may be real or simulated. The filter may be implemented in either the time or the frequency domain. The software tool is validated by comparing unfiltered spectrum mean and width estimates to their true values, and by reproducing previously published results. The effects on the weather parameter estimates using simulated weather-only data are evaluated for five filters: an ideal filter, two infinite impulse response filters, and two finite impulse response filters. Results considering external data, consisting of weather and clutter data, are evaluated on a range cell by range cell basis. Finally, it is shown theoretically and by computer simulation that a linear phase response is not required for a clutter rejection filter preceeding pulse-pair parameter estimation.
NASA Astrophysics Data System (ADS)
Patnaik, Rohit; Casasent, David
2008-04-01
We consider automatic target recognition (ATR) in infrared (IR) imagery using the minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF). As in our prior work (SPIE 6566-03), we consider classification of true-class CAD targets and rejection of real clutter and unseen confuser CAD objects with range and full 360° aspect view variations. In this work, we address rejection of new UCIR bush clutter data. We also present performance scores for several different training and test cases with attention to filter capacity, i.e., the number of training images that can be included in one filter before performance on the test set deteriorates appreciably. We find that range rather than aspect view distortions seem to affect filter capacity more. Initial target contrast ratio tests are also presented. To more properly address clutter, in all tests we now form the magnitude of the output correlation plane before analysis. We also address when and why linear versus circular correlations are best. We also address DIF filter-synthesis and fast implementation for wide area "search" test regions. This introduces new issues concerning the region over which correlation plane energy is minimized in filter synthesis and the size of the FFT to use in tests. A key issue is that both training and tests should use the same procedures. This is vital for training and test metrics to be comparable. We distinguish between whether linear or circular correlation plane energy is minimized.
Song, Pengfei; Trzasko, Joshua D; Manduca, Armando; Qiang, Bo; Kadirvel, Ramanathan; Kallmes, David F; Chen, Shigao
2017-04-01
Singular value decomposition (SVD)-based ultrasound blood flow clutter filters have recently demonstrated substantial improvement in clutter rejection for ultrafast plane wave microvessel imaging, and have become the commonly used clutter filtering method for many novel ultrafast imaging applications such as functional ultrasound and super-resolution imaging. At present, however, the computational burden of SVD remains as a major hurdle for practical implementation and clinical translation of this method. To address this challenge, in the study we present two blood flow clutter filtering methods based on randomized SVD (rSVD) and randomized spatial downsampling to accelerate SVD clutter filtering with minimal compromise to the clutter filter performance. rSVD accelerates SVD computation by approximating the k largest singular values, while random downsampling accelerates both full SVD and rSVD by decomposing the original large data matrix into small matrices that can be processed in parallel. An in vitro blood flow phantom study with the presence of heavy tissue clutter showed significantly improved computational performance using the proposed methods with minimal deterioration to the clutter filter performance (less than 3-dB reduction in blood to clutter ratio, less than 0.2-cm(2)/s(2) increase in flow mean squared error, less than 0.1-cm/s increase in the standard deviation of the vessel blood flow signal, and less than 0.3-cm/s increase in tissue clutter velocity for both full SVD and rSVD when the downsampling factor was less than 20× ). The maximum acceleration was about threefold from randomized spatial downsampling, and approximately another threefold from rSVD. An in vivo rabbit kidney perfusion study showed that rSVD provided comparable performance to full SVD in clutter rejection in vivo (maximum difference of blood to clutter ratio was less than 0.6 dB), and random downsampling provided artifact-free perfusion imaging results when combined with both
Mixture-Tuned, Clutter Matched Filter for Remote Detection of Subpixel Spectral Signals
NASA Technical Reports Server (NTRS)
Thompson, David R.; Mandrake, Lukas; Green, Robert O.
2013-01-01
Mapping localized spectral features in large images demands sensitive and robust detection algorithms. Two aspects of large images that can harm matched-filter detection performance are addressed simultaneously. First, multimodal backgrounds may thwart the typical Gaussian model. Second, outlier features can trigger false detections from large projections onto the target vector. Two state-of-the-art approaches are combined that independently address outlier false positives and multimodal backgrounds. The background clustering models multimodal backgrounds, and the mixture tuned matched filter (MT-MF) addresses outliers. Combining the two methods captures significant additional performance benefits. The resulting mixture tuned clutter matched filter (MT-CMF) shows effective performance on simulated and airborne datasets. The classical MNF transform was applied, followed by k-means clustering. Then, each cluster s mean, covariance, and the corresponding eigenvalues were estimated. This yields a cluster-specific matched filter estimate as well as a cluster- specific feasibility score to flag outlier false positives. The technology described is a proof of concept that may be employed in future target detection and mapping applications for remote imaging spectrometers. It is of most direct relevance to JPL proposals for airborne and orbital hyperspectral instruments. Applications include subpixel target detection in hyperspectral scenes for military surveillance. Earth science applications include mineralogical mapping, species discrimination for ecosystem health monitoring, and land use classification.
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.
Spread-Spectrum Beamforming and Clutter Filtering for Plane-Wave Color Doppler Imaging.
Mansour, Omar; Poepping, Tamie L; Lacefield, James C
2016-07-21
Plane-wave imaging is desirable for its ability to achieve high frame rates, allowing the capture of fast dynamic events and continuous Doppler data. In most implementations of plane-wave imaging, multiple low-resolution images from different plane wave tilt angles are compounded to form a single high-resolution image, thereby reducing the frame rate. Compounding improves the lateral beam profile in the high-resolution image, but it also acts as a low-pass filter in slow time that causes attenuation and aliasing of signals with high Doppler shifts. This paper introduces a spread-spectrum color Doppler imaging method that produces high-resolution images without the use of compounding, thereby eliminating the tradeoff between beam quality, maximum unaliased Doppler frequency, and frame rate. The method uses a long, random sequence of transmit angles rather than a linear sweep of plane wave directions. The random angle sequence randomizes the phase of off-focus (clutter) signals, thereby spreading the clutter power in the Doppler spectrum, while keeping the spectrum of the in-focus signal intact. The ensemble of randomly tilted low-resolution frames also acts as the Doppler ensemble, so it can be much longer than a conventional linear sweep, thereby improving beam formation while also making the slow-time Doppler sampling frequency equal to the pulse repetition frequency. Experiments performed using a carotid artery phantom with constant flow demonstrate that the spread-spectrum method more accurately measures the parabolic flow profile of the vessel and outperforms conventional plane-wave Doppler in both contrast resolution and estimation of high flow velocities. The spread-spectrum method is expected to be valuable for Doppler applications that require measurement of high velocities at high frame rates.
Object tracking under nonuniform illumination with adaptive correlation filtering
NASA Astrophysics Data System (ADS)
Picos, Kenia; Díaz-Ramírez, Víctor H.; Kober, Vitaly
2013-09-01
A real-time system for illumination-invariant object tracking is proposed. The system is able to estimate at high-rate the position of a moving target in an input scene when is corrupted by the presence of a high cluttering background and nonuniform illumination. The position of the target is estimated with the help of a filter bank of space-variant correlation filters. The filters in the bank, adapt their parameters according to the local statistical parameters of the observed scene in a small region centered at coordinates of a predicted position for the target in each frame. The prediction is carried out by exploiting information of present and past frames, and by using a dynamic motion model of the target in a two-dimensional plane. Computer simulation results obtained with the proposed system are presented and discussed in terms of tracking accuracy, computational complexity, and tolerance to nonuniform illumination.
You, Wei; Wang, Yuanyuan
2010-08-01
A fast clutter rejection method for ultrasound color flow imaging is proposed based on the first-order perturbation as an efficient implementation of eigen-decomposition. The proposed method is verified by simulated data. Results show that the proposed method can be adaptive to non-stationary clutter movements and its computational complexity is lower than that of the conventional eigen-based clutter rejection methods.
Adaptive filters: stable but divergent
NASA Astrophysics Data System (ADS)
Rupp, Markus
2015-12-01
The pros and cons of a quadratic error measure in the context of various applications have often been discussed. In this tutorial, we argue that it is not only a suboptimal but definitely the wrong choice when describing the stability behavior of adaptive filters. We take a walk through the past and recent history of adaptive filters and present 14 canonical forms of adaptive algorithms and even more variants thereof contrasting their mean-square with their l 2-stability conditions. In particular, in safety critical applications, the convergence in the mean-square sense turns out to provide wrong results, often not leading to stability at all. Only the robustness concept with its l 2-stability conditions ensures the absence of divergence.
Speed adaptation as Kalman filtering.
Barraza, Jose F; Grzywacz, Norberto M
2008-10-01
If the purpose of adaptation is to fit sensory systems to different environments, it may implement an optimization of the system. What the optimum is depends on the statistics of these environments. Therefore, the system should update its parameters as the environment changes. A Kalman-filtering strategy performs such an update optimally by combining current estimations of the environment with those from the past. We investigate whether the visual system uses such a strategy for speed adaptation. We performed a matching-speed experiment to evaluate the time course of adaptation to an abrupt velocity change. Experimental results are in agreement with Kalman-modeling predictions for speed adaptation. When subjects adapt to a low speed and it suddenly increases, the time course of adaptation presents two phases, namely, a rapid decrease of perceived speed followed by a slower phase. In contrast, when speed changes from fast to slow, adaptation presents a single phase. In the Kalman-model simulations, this asymmetry is due to the prevalence of low speeds in natural images. However, this asymmetry disappears both experimentally and in simulations when the adapting stimulus is noisy. In both transitions, adaptation now occurs in a single phase. Finally, the model also predicts the change in sensitivity to speed discrimination produced by the adaptation.
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.
Adaptive filtering with correlated state noise
NASA Technical Reports Server (NTRS)
Argentiero, P.
1972-01-01
An adaptive filter which uses a minimum variance criteria to estimate state noise covariance is presented. It is not necessary to assume white state noise in order to implement the filter. Simulation results are given which demonstrate that the filter tracks a satellite in the presence of modeling errors better than a conventional minimum variance filter with state noise. It is also shown that the propagated convariance matrix of the filter is an accurate indicator of the filter's performance.
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.
Coordinated adaptive filters for motion simulators.
NASA Technical Reports Server (NTRS)
Parrish, R. V.; Dieudonne, J. E.; Bowles, R. L.; Martin, D. J.
1973-01-01
A new approach to providing motion drive signals to a flight simulator utilizing coordinated adaptive filters is presented. Some motivation for the use of coordinated washout is discussed, along with conditions that determine the burden of coordination. The coordinated adaptive filters are derived, based on continuous steepest descent, and the application of the filters to simulated flight data is demonstrated.
CMOS analog switches for adaptive filters
NASA Technical Reports Server (NTRS)
Dixon, C. E.
1980-01-01
Adaptive active low-pass filters incorporate CMOS (Complimentary Metal-Oxide Semiconductor) analog switches (such as 4066 switch) that reduce variation in switch resistance when filter is switched to any selected transfer function.
Nonstationary EO/IR Clutter Suppression and Dim Object Tracking
NASA Astrophysics Data System (ADS)
Tartakovsky, A.; Brown, A.; Brown, J.
2010-09-01
We develop and evaluate the performance of advanced algorithms which provide significantly improved capabilities for automated detection and tracking of ballistic and flying dim objects in the presence of highly structured intense clutter. Applications include ballistic missile early warning, midcourse tracking, trajectory prediction, and resident space object detection and tracking. The set of algorithms include, in particular, adaptive spatiotemporal clutter estimation-suppression and nonlinear filtering-based multiple-object track-before-detect. These algorithms are suitable for integration into geostationary, highly elliptical, or low earth orbit scanning or staring sensor suites, and are based on data-driven processing that adapts to real-world clutter backgrounds, including celestial, earth limb, or terrestrial clutter. In many scenarios of interest, e.g., for highly elliptic and, especially, low earth orbits, the resulting clutter is highly nonstationary, providing a significant challenge for clutter suppression to or below sensor noise levels, which is essential for dim object detection and tracking. We demonstrate the success of the developed algorithms using semi-synthetic and real data. In particular, our algorithms are shown to be capable of detecting and tracking point objects with signal-to-clutter levels down to 1/1000 and signal-to-noise levels down to 1/4.
Objects tracking with adaptive correlation filters and Kalman filtering
NASA Astrophysics Data System (ADS)
Ontiveros-Gallardo, Sergio E.; Kober, Vitaly
2015-09-01
Object tracking is commonly used for applications such as video surveillance, motion based recognition, and vehicle navigation. In this work, a tracking system using adaptive correlation filters and robust Kalman prediction of target locations is proposed. Tracking is performed by means of multiple object detections in reduced frame areas. A bank of filters is designed from multiple views of a target using synthetic discriminant functions. An adaptive approach is used to improve discrimination capability of the synthesized filters adapting them to multiple types of backgrounds. With the help of computer simulation, the performance of the proposed algorithm is evaluated in terms of detection efficiency and accuracy of object tracking.
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.
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.
Adaptive marginal median filter for colour images.
Morillas, Samuel; Gregori, Valentín; Sapena, Almanzor
2011-01-01
This paper describes a new filter for impulse noise reduction in colour images which is aimed at improving the noise reduction capability of the classical vector median filter. The filter is inspired by the application of a vector marginal median filtering process over a selected group of pixels in each filtering window. This selection, which is based on the vector median, along with the application of the marginal median operation constitutes an adaptive process that leads to a more robust filter design. Also, the proposed method is able to process colour images without introducing colour artifacts. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter.
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.
Multiple Model Adaptive Estimator Target Tracker for Maneuvering Targets in Clutter
2007-11-02
Clutter THESIS Presented to the Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force...Institute of Technology Air University Air Education and Training Command In Partial Fulfillment of the Requirements for the Degree of Master of Science...tolerated many late night homework sessions, my commandeering of the home computer, and all of the other aggravations that are part of being an AFIT spouse
Suppression of Biodynamic Interference by Adaptive Filtering
NASA Technical Reports Server (NTRS)
Velger, M.; Merhav, S. J.; Grunwald, A. J.
1984-01-01
Preliminary experimental results obtained in moving base simulator tests are presented. Both for pursuit and compensatory tracking tasks, a strong deterioration in tracking performance due to biodynamic interference is found. The use of adaptive filtering is shown to substantially alleviate these effects, resulting in a markedly improved tracking performance and reduction in task difficulty. The effect of simulator motion and of adaptive filtering on human operator describing functions is investigated. Adaptive filtering is found to substantially increase pilot gain and cross-over frequency, implying a more tight tracking behavior. The adaptive filter is found to be effective in particular for high-gain proportional dynamics, low display forcing function power and for pursuit tracking task configurations.
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…
Local image registration by adaptive filtering.
Caner, Gulcin; Tekalp, A Murat; Sharma, Gaurav; Heinzelman, Wendi
2006-10-01
We propose a new adaptive filtering framework for local image registration, which compensates for the effect of local distortions/displacements without explicitly estimating a distortion/displacement field. To this effect, we formulate local image registration as a two-dimensional (2-D) system identification problem with spatially varying system parameters. We utilize a 2-D adaptive filtering framework to identify the locally varying system parameters, where a new block adaptive filtering scheme is introduced. We discuss the conditions under which the adaptive filter coefficients conform to a local displacement vector at each pixel. Experimental results demonstrate that the proposed 2-D adaptive filtering framework is very successful in modeling and compensation of both local distortions, such as Stirmark attacks, and local motion, such as in the presence of a parallax field. In particular, we show that the proposed method can provide image registration to: a) enable reliable detection of watermarks following a Stirmark attack in nonblind detection scenarios, b) compensate for lens distortions, and c) align multiview images with nonparametric local motion.
Clutter Mitigation in Echocardiography Using Sparse Signal Separation
Turek, Javier S.; Elad, Michael; Yavneh, Irad
2015-01-01
In ultrasound imaging, clutter artifacts degrade images and may cause inaccurate diagnosis. In this paper, we apply a method called Morphological Component Analysis (MCA) for sparse signal separation with the objective of reducing such clutter artifacts. The MCA approach assumes that the two signals in the additive mix have each a sparse representation under some dictionary of atoms (a matrix), and separation is achieved by finding these sparse representations. In our work, an adaptive approach is used for learning the dictionary from the echo data. MCA is compared to Singular Value Filtering (SVF), a Principal Component Analysis- (PCA-) based filtering technique, and to a high-pass Finite Impulse Response (FIR) filter. Each filter is applied to a simulated hypoechoic lesion sequence, as well as experimental cardiac ultrasound data. MCA is demonstrated in both cases to outperform the FIR filter and obtain results comparable to the SVF method in terms of contrast-to-noise ratio (CNR). Furthermore, MCA shows a lower impact on tissue sections while removing the clutter artifacts. In experimental heart data, MCA obtains in our experiments clutter mitigation with an average CNR improvement of 1.33 dB. PMID:26199622
Adaptive filtering for the lattice Boltzmann method
NASA Astrophysics Data System (ADS)
Marié, Simon; Gloerfelt, Xavier
2017-03-01
In this study, a new selective filtering technique is proposed for the Lattice Boltzmann Method. This technique is based on an adaptive implementation of the selective filter coefficient σ. The proposed model makes the latter coefficient dependent on the shear stress in order to restrict the use of the spatial filtering technique in sheared stress region where numerical instabilities may occur. Different parameters are tested on 2D test-cases sensitive to numerical stability and on a 3D decaying Taylor-Green vortex. The results are compared to the classical static filtering technique and to the use of a standard subgrid-scale model and give significant improvements in particular for low-order filter consistent with the LBM stencil.
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.
Color image diffusion using adaptive bilateral filter.
Xie, Jun; Ann Heng, Pheng
2005-01-01
In this paper, we propose an approach to diffuse color images based on the bilateral filter. Real image data has a level of uncertainty that is manifested in the variability of measures assigned to pixels. This uncertainty is usually interpreted as noise and considered an undesirable component of the image data. Image diffusion can smooth away small-scale structures and noise while retaining important features, thus improving the performances for many image processing algorithms such as image compression, segmentation and recognition. The bilateral filter is noniterative, simple and fast. It has been shown to give similar and possibly better filtering results than iterative approaches. However, the performance of this filter is greatly affected by the choose of the parameters of filtering kernels. In order to remove noise and maintain the significant features on images, we extend the bilateral filter by introducing an adaptive domain spread into the nonlinear diffusion scheme. For color images, we employ the CIE-Lab color system to describe input images and the filtering process is operated using three channels together. Our analysis shows that the proposed method is more suitable for preserving strong edges on noisy images than the original bilateral filter. Empirical results on both nature images and color medical images confirm the novel method's advantages, and show it can diffuse various kinds of color images correctly and efficiently.
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.
Gearbox Fault Diagnosis Using Adaptive Wavelet Filter
NASA Astrophysics Data System (ADS)
LIN, J.; ZUO, M. J.
2003-11-01
Vibration signals from a gearbox are usually noisy. As a result, it is difficult to find early symptoms of a potential failure in a gearbox. Wavelet transform is a powerful tool to disclose transient information in signals. An adaptive wavelet filter based on Morlet wavelet is introduced in this paper. The parameters in the Morlet wavelet function are optimised based on the kurtosis maximisation principle. The wavelet used is adaptive because the parameters are not fixed. The adaptive wavelet filter is found to be very effective in detection of symptoms from vibration signals of a gearbox with early fatigue tooth crack. Two types of discrete wavelet transform (DWT), the decimated with DB4 wavelet and the undecimated with harmonic wavelet, are also used to analyse the same signals for comparison. No periodic impulses appear on any scale in either DWT decomposition.
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.
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.
Adaptive noise Wiener filter for scanning electron microscope imaging system.
Sim, K S; Teh, V; Nia, M E
2016-01-01
Noise on scanning electron microscope (SEM) images is studied. Gaussian noise is the most common type of noise in SEM image. We developed a new noise reduction filter based on the Wiener filter. We compared the performance of this new filter namely adaptive noise Wiener (ANW) filter, with four common existing filters as well as average filter, median filter, Gaussian smoothing filter and the Wiener filter. Based on the experiments results the proposed new filter has better performance on different noise variance comparing to the other existing noise removal filters in the experiments.
A practical sub-space adaptive filter.
Zaknich, A
2003-01-01
A Sub-Space Adaptive Filter (SSAF) model is developed using, as a basis, the Modified Probabilistic Neural Network (MPNN) and its extension the Tuneable Approximate Piecewise Linear Regression (TAPLR) model. The TAPLR model can be adjusted by a single smoothing parameter continuously from the best piecewise linear model in each sub-space to the best approximately piecewise linear model over the whole data space. A suitable value in between ensures that all neighbouring piecewise linear models merge together smoothly at their boundaries. This model was developed by altering the form of the MPNN, a network used for general nonlinear regression. The MPNNs special structure allows it to be easily used to model a process by appropriately weighting piecewise linear models associated with each of the network's radial basis functions. The model has now been further extended to allow each piecewise linear model section to be adapted separately as new data flows through it. By doing this, the proposed SSAF model represents a learning/filtering method for nonlinear processes that provides one solution to the stability/plasticity dilemma associated with standard adaptive filters.
Musical noise reduction using an adaptive filter
NASA Astrophysics Data System (ADS)
Hanada, Takeshi; Murakami, Takahiro; Ishida, Yoshihisa; Hoya, Tetsuya
2003-10-01
This paper presents a method for reducing a particular noise (musical noise). The musical noise is artificially produced by Spectral Subtraction (SS), which is one of the most conventional methods for speech enhancement. The musical noise is the tin-like sound and annoying in human auditory. We know that the duration of the musical noise is considerably short in comparison with that of speech, and that the frequency components of the musical noise are random and isolated. In the ordinary SS-based methods, the musical noise is removed by the post-processing. However, the output of the ordinary post-processing is delayed since the post-processing uses the succeeding frames. In order to improve this problem, we propose a novel method using an adaptive filter. In the proposed system, the observed noisy signal is used as the input signal to the adaptive filter and the output of SS is used as the reference signal. In this paper we exploit the normalized LMS (Least Mean Square) algorithm for the adaptive filter. Simulation results show that the proposed method has improved the intelligibility of the enhanced speech in comparison with the conventional method.
Nonlinear adaptive filtering of stimulus artifact.
Grieve, R; Parker, P A; Hudgins, B; Englehart, K
2000-03-01
Noninvasive measurements of somatosensory evoked potentials have both clinical and research applications. The electrical artifact which results from the stimulus is an interference which can distort the evoked signal, and introduce errors in response onset timing estimation. Given that this interference is synchronous with the evoked signal, it cannot be reduced by the conventional technique of ensemble averaging. The technique of adaptive noise cancelling has potential in this regard however, and has been used effectively in other similar problems. An adaptive noise cancelling filter which uses a neural network as the adaptive element is investigated in this application. The filter is implemented and performance determined in the cancelling of artifact for in vivo measurements on the median nerve. A technique of segmented neural network training is proposed in which the network is trained on that segment of the record time window which does not contain the evoked signal. The neural network is found to generalize well from this training to include the segment of the window containing the evoked signal. Both quantitative and qualitative measures show that significant stimulus artifact reduction is achieved.
Improved adaptive complex diffusion despeckling filter.
Bernardes, Rui; Maduro, Cristina; Serranho, Pedro; Araújo, Adérito; Barbeiro, Sílvia; Cunha-Vaz, José
2010-11-08
Despeckling optical coherence tomograms from the human retina is a fundamental step to a better diagnosis or as a preprocessing stage for retinal layer segmentation. Both of these applications are particularly important in monitoring the progression of retinal disorders. In this study we propose a new formulation for a well-known nonlinear complex diffusion filter. A regularization factor is now made to be dependent on data, and the process itself is now an adaptive one. Experimental results making use of synthetic data show the good performance of the proposed formulation by achieving better quantitative results and increasing computation speed.
Bayesian adaptive estimation of the auditory filter.
Shen, Yi; Richards, Virginia M
2013-08-01
A Bayesian adaptive procedure for estimating the auditory-filter shape was proposed and evaluated using young, normal-hearing listeners at moderate stimulus levels. The resulting quick-auditory-filter (qAF) procedure assumed the power spectrum model of masking with the auditory-filter shape being modeled using a spectrally symmetric, two-parameter rounded-exponential (roex) function. During data collection using the qAF procedure, listeners detected the presence of a pure-tone signal presented in the spectral notch of a noise masker. Dependent on the listener's response on each trial, the posterior probability distributions of the model parameters were updated, and the resulting parameter estimates were then used to optimize the choice of stimulus parameters for the subsequent trials. Results showed that the qAF procedure gave similar parameter estimates to the traditional threshold-based procedure in many cases and was able to reasonably predict the masked signal thresholds. Additional measurements suggested that occasional failures of the qAF procedure to reliably converge could be a consequence of incorrect responses early in a qAF track. The addition of a parameter describing lapses of attention reduced the likelihood of such failures.
Detection of fluctuating targets in correlated K-distributed clutter
NASA Astrophysics Data System (ADS)
Conte, E.; Longo, M.; Lops, M.; Ricci, G.
This paper deals with the detection of coherent pulse trains, with scan-to-scan amplitude and phase fluctuation, in correlated K-distributed clutter. A coherent adaptive structure, designed according to a generalized Neyman-Pearson strategy, is considered. A relevant feature of the proposed approach is that the detector is canonical, namely its structure does not depend on the fluctuation laws of the target amplitude and phase. In addition, the receiver in the presence of correlated clutter is the same as for uncorrelated observations, but for the presence of a whitening filter. The performance assessment shows that the proposed scheme outperforms the conventional detector under the most common models of scan-to-scan fluctuation. Moreover, the performance in correlated observations can be read off the curves corresponding to white noise, provided that the abscissas are interpreted as the signal-to-noise ratio at the output of the whitening filter.
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.
Design of suboptimal adaptive filter for stochastic systems
NASA Astrophysics Data System (ADS)
Ahn, Jun Il; Shin, Vladimir
2005-12-01
In this paper, the problem of estimating the system state in for linear discrete-time systems with uncertainties is considered. In [1], [2], we have proposed the fusion formula (FF) for an arbitrary number of correlated and uncorrelated estimates. The FF is applied to detection and filtering problem. The new suboptimal adaptive filter with parallel structure is herein proposed. In consequence of parallel structure of the proposed filter, parallel computers can be used for their design. A lower computational complexity and lower memory demand are achieved with the proposed filter than in the optimal adaptive Lainiotis-Kalman filter. Example demonstrates the accuracy of the new filter.
[Evaluation of an adaptive filter for CT under low-CNR condition: comparison with linear filter].
Mori, Issei; Uchida, Miho; Sato, Ami; Sato, Shingo; Tamura, Hajime; Takai, Yoshihiro; Ishibashi, Tadashi; Saito, Haruo; Hosokai, Yoshiyuki; Ogura, Takahide; Chida, Koichi; Machida, Yoshio
2009-01-20
The use of an adaptive filter for CT images is becoming a common procedure and is said to reduce image noise while preserving sharpness and helping to reduce the required X-ray dose. Although many reports support this view, the validity of such evaluations is arguable. When the linearity of a system is in question, physical performance indexes should be measured under conditions similar to those of clinical use. Evaluations of diagnosis using clinical images may be fallible because the non-filtered image used as the reference might not have been optimally reconstructed. We have chosen simple, but commonly used, adaptive filters for our evaluation. As a reference for comparing performance, we designed linear filters that best approximate the noise characteristics of the adaptive filters. MTF is measured through observation of the edge-spread function. Clinical abdominal images are used to compare the performance of adaptive filters and linear filters. We conclude that the performance of the type of adaptive filter we have chosen is virtually the same as that of the linear filter, as long as the image quality of soft tissues is our interest. Both the noise SD and MTF are virtually the same if the contrast of the object is not substantially higher than 150 HU. Images of soft tissues obtained with the use of adaptive filters are also virtually the same as those obtained by linear filters. The edge-preservation characteristic of this adaptive filter is not observable for soft tissues.
Adaptive Spatial-Temporal Filtering Methods for Clutter Removal and Target Tracking
2007-09-01
Stocker [16] and Schaum [18], [19]. However, experience shows that even with all these sacrifices and an excellent registration algorithm, there is...A.P. Schaum , “Principles of interpolator design and evaluation,” NRL Report 9356, Naval Research Laboratory, 1991. [19] A.P. Schaum , “Dual
Adaptive filter design using recurrent cerebellar model articulation controller.
Lin, Chih-Min; Chen, Li-Yang; Yeung, Daniel S
2010-07-01
A novel adaptive filter is proposed using a recurrent cerebellar-model-articulation-controller (CMAC). The proposed locally recurrent globally feedforward recurrent CMAC (RCMAC) has favorable properties of small size, good generalization, rapid learning, and dynamic response, thus it is more suitable for high-speed signal processing. To provide fast training, an efficient parameter learning algorithm based on the normalized gradient descent method is presented, in which the learning rates are on-line adapted. Then the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so the stability of the filtering error can be guaranteed. To demonstrate the performance of the proposed adaptive RCMAC filter, it is applied to a nonlinear channel equalization system and an adaptive noise cancelation system. The advantages of the proposed filter over other adaptive filters are verified through simulations.
An adaptive filter for smoothing noisy radar images
NASA Technical Reports Server (NTRS)
Frost, V. S.; Stiles, J. A.; Shanmugam, K. S.; Holtzman, J. C.; Smith, S. A.
1981-01-01
A spatial domain adaptive Wiener filter for smoothing radar images corrupted by multiplicative noise is presented. The filter is optimum in a minimum mean squared error sense, computationally efficient, and preserves edges in the image better than other filters. The proposed algorithm can also be used for processing optical images with illumination variations that have a multiplicative effect.
Adjustment of adaptive sum comb filter for PPG signals.
Pilt, Kristjan; Meigas, Kalju; Ferenets, Rain; Kaik, Juri
2009-01-01
AC component of photoplethysmography signal carries important information for diagnostics. Registered signal may be affected by noises, which are sharing the same bandwidth. Adaptive comb filter is used for the AC component extraction. Due to filter averaging behavior it decreases the signal shape difference between consecutive beats. Comb filter needs to be adjusted for PPG signal. Comb filter new weight values are determined through numerical computation. Experiments with generated photoplethysmographic signals were carried out to compare adjusted and non-adjusted adaptive sum comb filter.
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.
Adaptive Filtering in the Wavelet Transform Domain via Genetic Algorithms
2004-08-06
identification. Figure 1 shows a very basic example of this type of system . x(n) Figure 1. Basic system identification using adaptive filters block diagram...block diagram of adaptive wavelet filtering system . The main objective of the system shown in Figure 2 is to minimize the error signal, e(k), which is...in Table 1. Daub4 wavelets use filter banks (Vaidyanathan 1992) containing exactly four elements. 5 Figure 4. Time-Domain Representation of
You, Wei; Wang, Yuanyuan
2011-11-01
In ultrasound color flow imaging (CFI), the single-ensemble eigen-based filters can reject clutter components using each slow-time ensemble individually. They have shown excellent spatial adaptability. This article proposes a novel clutter rejection method called the single-ensemble geometry filter (SGF), which is derived from an analytic geometry perspective. If the transmitted pulse number M equals two, the clutter component distribution on a two-dimensional (2-D) plane will be similar to a tilted ellipse. Therefore, the direction of the major axis of the ellipse can be used as the first principal component of the autocorrelation matrix estimated from multiple ensembles. Then the algorithm is generalized from 2-D to a higher dimensional space by using linear algebra representations of the ellipse. Comparisons have been made with the high-pass filter (HPF), the Hankel-singular value decomposition (SVD) filter and the recursive eigen-decomposition (RED) method using both simulated and human carotid data. Results show that compared with HPF and Hankel-SVD, the proposed filter causes less bias on the velocity estimation when the clutter velocity is close to that of the blood flow. On the other hand, the proposed filter does not need to update the autocorrelation matrix and can achieve better spatial adaptability than the RED.
Progress in adaptive control of flexible spacecraft using lattice filters
NASA Technical Reports Server (NTRS)
Sundararajan, N.; Montgomery, R. C.
1985-01-01
This paper reviews the use of the least square lattice filter in adaptive control systems. Lattice filters have been used primarily in speech and signal processing, but they have utility in adaptive control because of their order-recursive nature. They are especially useful in dealing with structural dynamics systems wherein the order of a controller required to damp a vibration is variable depending on the number of modes significantly excited. Applications are presented for adaptive control of a flexible beam. Also, difficulties in the practical implementation of the lattice filter in adaptive control are discussed.
Investigation of Adaptive Robust Kalman Filtering Algorithms for GPS/DR Navigation System Filters
NASA Astrophysics Data System (ADS)
Elzoghby, MOSTAFA; Arif, USMAN; Li, FU; Zhi Yu, XI
2017-03-01
The conventional Kalman filter (KF) algorithm is suitable if the characteristic noise covariance for states as well as measurements is readily known but in most cases these are unknown. Similarly robustness is required instead of smoothing if states are changing abruptly. Such an adaptive as well as robust Kalman filter is vital for many real time applications, like target tracking and navigating aerial vehicles. A number of adaptive as well as robust Kalman filtering methods are available in the literature. In order to investigate the performance of some of these methods, we have selected three different Kalman filters, namely Sage Husa KF, Modified Adaptive Robust KF and Adaptively Robust KF, which are easily simulate able as well as implementable for real time applications. These methods are simulated for land based vehicle and the results are compared with conventional Kalman filter. Results show that the Modified Adaptive Robust KF is best amongst the selected methods and can be used for Navigation applications.
NASA Astrophysics Data System (ADS)
Stehwien, Wolfgang
1989-11-01
The problem of classifying radar clutter as found on air traffic control radar systems is studied. An algorithm based on Bayes decision theory and the parametric maximum a posteriori probability classifier is developed to perform this classification automatically. This classifier employs a quadratic discriminant function and is optimum for feature vectors that are distributed according to the multivariate normal density. Separable clutter classes are most likely to arise from the analysis of the Doppler spectrum. Specifically, a feature set based on the complex reflection coefficients of the lattice prediction error filter is proposed. The classifier is tested using data recorded from L-band air traffic control radars. The Doppler spectra of these data are examined; the properties of the feature set computed using these data are studied in terms of both the marginal and multivariate statistics. Several strategies involving different numbers of features, class assignments, and data set pretesting according to Doppler frequency and signal to noise ratio were evaluated before settling on a workable algorithm. Final results are presented in terms of experimental misclassification rates and simulated and classified plane position indicator displays.
NASA Astrophysics Data System (ADS)
Shi, Chenguang; Salous, Sana; Wang, Fei; Zhou, Jianjiang
2016-12-01
In this paper, we investigate the problem of low probability of intercept (LPI)-based adaptive radar waveform optimization in signal-dependent clutter for joint radar and cellular communication systems, where the radar system optimizes the transmitted waveform such that the interference caused to the cellular communication systems is strictly controlled. Assuming that the precise knowledge of the target spectra, the power spectral densities (PSDs) of signal-dependent clutters, the propagation losses of corresponding channels and the communication signals is known by the radar, three different LPI based criteria for radar waveform optimization are proposed to minimize the total transmitted power of the radar system by optimizing the multicarrier radar waveform with a predefined signal-to-interference-plus-noise ratio (SINR) constraint and a minimum required capacity for the cellular communication systems. These criteria differ in the way the communication signals scattered off the target are considered in the radar waveform design: (1) as useful energy, (2) as interference or (3) ignored altogether. The resulting problems are solved analytically and their solutions represent the optimum power allocation for each subcarrier in the multicarrier radar waveform. We show with numerical results that the LPI performance of the radar system can be significantly improved by exploiting the scattered echoes off the target due to cellular communication signals received at the radar receiver.
Shi, Chenguang; Salous, Sana; Wang, Fei; Zhou, Jianjiang
2016-01-01
In this paper, we investigate the problem of low probability of intercept (LPI)-based adaptive radar waveform optimization in signal-dependent clutter for joint radar and cellular communication systems, where the radar system optimizes the transmitted waveform such that the interference caused to the cellular communication systems is strictly controlled. Assuming that the precise knowledge of the target spectra, the power spectral densities (PSDs) of signal-dependent clutters, the propagation losses of corresponding channels and the communication signals is known by the radar, three different LPI based criteria for radar waveform optimization are proposed to minimize the total transmitted power of the radar system by optimizing the multicarrier radar waveform with a predefined signal-to-interference-plus-noise ratio (SINR) constraint and a minimum required capacity for the cellular communication systems. These criteria differ in the way the communication signals scattered off the target are considered in the radar waveform design: (1) as useful energy, (2) as interference or (3) ignored altogether. The resulting problems are solved analytically and their solutions represent the optimum power allocation for each subcarrier in the multicarrier radar waveform. We show with numerical results that the LPI performance of the radar system can be significantly improved by exploiting the scattered echoes off the target due to cellular communication signals received at the radar receiver.
Real time microcontroller implementation of an adaptive myoelectric filter.
Bagwell, P J; Chappell, P H
1995-03-01
This paper describes a real time digital adaptive filter for processing myoelectric signals. The filter time constant is automatically selected by the adaptation algorithm, giving a significant improvement over linear filters for estimating the muscle force and controlling a prosthetic device. Interference from mains sources often produces problems for myoelectric processing, and so 50 Hz and all harmonic frequencies are reduced by an averaging filter and differential process. This makes practical electrode placement and contact less critical and time consuming. An economic real time implementation is essential for a prosthetic controller, and this is achieved using an Intel 80C196KC microcontroller.
Clutter discrimination algorithm simulation in pulse laser radar imaging
NASA Astrophysics Data System (ADS)
Zhang, Yan-mei; Li, Huan; Guo, Hai-chao; Su, Xuan; Zhu, Fule
2015-10-01
Pulse laser radar imaging performance is greatly influenced by different kinds of clutter. Various algorithms are developed to mitigate clutter. However, estimating performance of a new algorithm is difficult. Here, a simulation model for estimating clutter discrimination algorithms is presented. This model consists of laser pulse emission, clutter jamming, laser pulse reception and target image producing. Additionally, a hardware platform is set up gathering clutter data reflected by ground and trees. The data logging is as clutter jamming input in the simulation model. The hardware platform includes a laser diode, a laser detector and a high sample rate data logging circuit. The laser diode transmits short laser pulses (40ns FWHM) at 12.5 kilohertz pulse rate and at 905nm wavelength. An analog-to-digital converter chip integrated in the sample circuit works at 250 mega samples per second. The simulation model and the hardware platform contribute to a clutter discrimination algorithm simulation system. Using this system, after analyzing clutter data logging, a new compound pulse detection algorithm is developed. This new algorithm combines matched filter algorithm and constant fraction discrimination (CFD) algorithm. Firstly, laser echo pulse signal is processed by matched filter algorithm. After the first step, CFD algorithm comes next. Finally, clutter jamming from ground and trees is discriminated and target image is produced. Laser radar images are simulated using CFD algorithm, matched filter algorithm and the new algorithm respectively. Simulation result demonstrates that the new algorithm achieves the best target imaging effect of mitigating clutter reflected by ground and trees.
Advanced Broadband Acoustic Clutter
2010-10-31
7, 18]). Key result: despite some claims to the contrary in the ocean acoustics community, sub-bottom clutter can and does occur in shallow water ...propagation, and clutter in both theory and measurements. This is important because in some shallow water areas (especially those with significant riverine...Abraham, data from Clutter Experiment) to examine dependence of clutter statistics on multipath (Ref [8]). Key result: in the shallow water
Decision-directed entropy-based adaptive filtering
NASA Astrophysics Data System (ADS)
Myler, Harley R.; Weeks, Arthur R.; Van Dyke-Lewis, Michelle
1991-12-01
A recurring problem in adaptive filtering is selection of control measures for parameter modification. A number of methods reported thus far have used localized order statistics to adaptively adjust filter parameters. The most effective techniques are based on edge detection as a decision mechanism to allow the preservation of edge information while noise is filtered. In general, decision-directed adaptive filters operate on a localized area within an image by using statistics of the area as a discrimination parameter. Typically, adaptive filters are based on pixel to pixel variations within a localized area that are due to either edges or additive noise. In homogeneous areas within the image where variances are due to additive noise, the filter should operate to reduce the noise. Using an edge detection technique, a decision directed adaptive filter can vary the filtering proportional to the amount of edge information detected. We show an approach using an entropy measure on edges to differentiate between variations in the image due to edge information as compared against noise. The method uses entropy calculated against the spatial contour variations of edges in the window.
An adaptive Kalman filter for ECG signal enhancement.
Vullings, Rik; de Vries, Bert; Bergmans, Jan W M
2011-04-01
The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement of the SNR and loss of clinically relevant physiological signal dynamics. Using a bayesian framework, in this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive estimation of the process and measurement noise covariances is performed by maximizing the bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to fluctuate.
The cerebellum as an adaptive filter: a general model?
Dean, Paul; Porrill, John
2010-01-01
Many functional models of the cerebellar microcircuit are based on the adaptive-filter model first proposed by Fujita. The adaptive filter has powerful signal processing capacities that are suitable for both sensory and motor tasks, and uses a simple and intuitively plausible decorrelation learning rule that offers and account of the evolution of the inferior olive. Moreover, in those cases where the input-output transformations of cerebellar microzones have been sufficiently characterised, they appear to conform to those predicted by the adaptive-filter model. However, these cases are few in number, and comparing the model with the internal operations of the microcircuit itself has not proved straightforward. Whereas some microcircuit features appear compatible with adaptive-filter function, others such as simple granular-layer processing or Purkinje cell bistability, do not. How far these seeming incompatibilities indicate additional computational roles for the cerebellar microcircuit remains to be determined.
Rezaee, Kh.; Haddadnia, J.
2013-01-01
Background: Breast cancer is currently one of the leading causes of death among women worldwide. The diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians. Objective: This paper proposes a new algorithm which draws on discrete wavelet transform and adaptive K-means techniques to transmute the medical images implement the tumor estimation and detect breast cancer tumors in mammograms in early stages. It also allows the rapid processing of the input data. Method: In the first step, after designing a filter, the discrete wavelet transform is applied to the input images and the approximate coefficients of scaling components are constructed. Then, the different parts of image are classified in continuous spectrum. In the next step, by using adaptive K-means algorithm for initializing and smart choice of clusters’ number, the appropriate threshold is selected. Finally, the suspicious cancerous mass is separated by implementing the image processing techniques. Results: We Received 120 mammographic images in LJPEG format, which had been scanned in Gray-Scale with 50 microns size, 3% noise and 20% INU from clinical data taken from two medical databases (mini-MIAS and DDSM). The proposed algorithm detected tumors at an acceptable level with an average accuracy of 92.32% and sensitivity of 90.24%. Also, the Kappa coefficient was approximately 0.85, which proved the suitable reliability of the system performance. Conclusion: The exact positioning of the cancerous tumors allows the radiologist to determine the stage of disease progression and suggest an appropriate treatment in accordance with the tumor growth. The low PPV and high NPV of the system is a warranty of the system and both clinical specialists and patients can trust its output. PMID:25505753
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.
Enhanced adaptive loop filter for motion compensated frame.
Yoo, Young-Joe; Seo, Chan-Won; Han, Jong-Ki; Nguyen, Truong Q
2011-08-01
We propose an adaptive loop filter to remove the redundancy between current and motion compensated frames so that the residual signal is minimized, thus coding efficiency increases. The loop filter coefficients and offset are optimized for each frame or a set of blocks to minimize the total energy of the residual signal resulting from motion estimation and compensation. The optimized loop filter with offset is applied for the set of blocks where the filtering process gives coding gain based upon rate-distortion cost. The proposed loop filter is used for the motion compensated frame whereas the conventional adaptive interpolation filter (AIF) is applied to the reference frames to interpolate the subpixel values. Another conventional scheme adaptive loop filter (ALF), is used after deblocking filtering to enhance quality of reconstructed frames, not to minimize energy of residual signal. The proposed loop filter can be used in combination with the AIF and ALF. Experimental results show that proposed algorithm provides the averaged bit reduction of 8% compared to conventional H.264/AVC scheme. When the proposed scheme is combined with AIF and ALF, the coding gain increases even further.
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…
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.
Weighted adaptive spatial filtering in digital holographic microscopy
NASA Astrophysics Data System (ADS)
Hong, Yuan; Shi, Tielin; Wang, Xiao; Zhang, Yichun; Chen, Kepeng; Liao, Guanglan
2017-01-01
Spatial filtering, a key point to realize real-time measurement, is used commonly in digital off-axis holography to extract desired terms. In this paper, we propose a weighted adaptive spatial filtering method by weighting the adaptive filtering window (obtained from image segmentation) based on signal to noise ratio. The advantages of this method are evaluated by simulations and further verified by recorded digital image plane holograms. The results demonstrate that our method is effective in suppressing noise and retaining the sharp edges in the reconstructed 3D profiles.
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.
Fast HDR image upscaling using locally adapted linear filters
NASA Astrophysics Data System (ADS)
Talebi, Hossein; Su, Guan-Ming; Yin, Peng
2015-02-01
A new method for upscaling high dynamic range (HDR) images is introduced in this paper. Overshooting artifact is the common problem when using linear filters such as bicubic interpolation. This problem is visually more noticeable while working on HDR images where there exist more transitions from dark to bright. Our proposed method is capable of handling these artifacts by computing a simple gradient map which enables the filter to be locally adapted to the image content. This adaptation consists of first, clustering pixels into regions with similar edge structures and second, learning the shape and length of our symmetric linear filter for each of these pixel groups. This new filter can be implemented in a separable fashion which perfectly fits hardware implementations. Our experimental results show that training our filter with HDR images can effectively reduce the overshooting artifacts and improve upon the visual quality of the existing linear upscaling approaches.
Improving nonlinear modeling capabilities of functional link adaptive filters.
Comminiello, Danilo; Scarpiniti, Michele; Scardapane, Simone; Parisi, Raffaele; Uncini, Aurelio
2015-09-01
The functional link adaptive filter (FLAF) represents an effective solution for online nonlinear modeling problems. In this paper, we take into account a FLAF-based architecture, which separates the adaptation of linear and nonlinear elements, and we focus on the nonlinear branch to improve the modeling performance. In particular, we propose a new model that involves an adaptive combination of filters downstream of the nonlinear expansion. Such combination leads to a cooperative behavior of the whole architecture, thus yielding a performance improvement, particularly in the presence of strong nonlinearities. An advanced architecture is also proposed involving the adaptive combination of multiple filters on the nonlinear branch. The proposed models are assessed in different nonlinear modeling problems, in which their effectiveness and capabilities are shown.
Lee, Boreom; Kee, Youngwook; Han, Jonghee; Yi, Won Jin
2011-01-01
Photoplethysmographic (PPG) signal can provide important information about cardiovascular and respiratory conditions of individuals in a hospital or daily life. However, PPG can be distorted by motion artifacts significantly. Therefore, the reduction of the effects of motion artifacts is very important procedure for monitoring cardio-respiratory system by PPG. There have been many adaptive techniques to reduce motion artifacts from PPG signal including normalized least mean squares (NLMS) method, recursive least squares (RLS) filter, and Kalman filter. In the present study, we propose the adaptive comb filter (ACF) for reducing the effects of motion artifacts from PPG signal. ACF with adaptive lattice infinite impulse response (IIR) notch filter (ALNF) successfully reduced the motion artifacts from the quasi-periodic PPG signal.
Convergence Analysis of LMS based Adaptive filter
NASA Astrophysics Data System (ADS)
Rai, Amrita; Kohli, Amit Kumar
2010-11-01
A standard algorithm for LMS-filter simulation, tested with several convergence criteria is presented in this paper. We analyze the steady-state mean square error (MSE) convergence of the LMS algorithm when random functions are used as reference inputs. In this paper, we make a more precise analysis using the deterministic nature of the reference inputs and their time-variant correlation matrix. Simulations performed under MATLAB show remarkable differences between convergence criteria with various value of the step size.
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.
Building block for an orthonormal-lattice-filter adaptive network
NASA Astrophysics Data System (ADS)
Gabriel, W. F.
1980-07-01
The recent algorithm for a multistage multichannel orthonormal lattice filter proposed by M. Aftab Alam is a welcome addition to the library of adaptive-processing algorithms and provides a flexible alternative to the conventional approach of an optimum Weiner filter. This algorithm is based on a Gram-Schmidt orthonormalization procedure which is similar to cascade adaptive processing techniques described in earlier works. One of the most desirable features of this type of processing network is that it can be implemented with simple one-stage orthogonal-filter building blocks which directly filter the input data samples. These building blocks are the major subject of this report, and a particular configuration is developed based on a modified version of the familiar Howells-Applebaum algorithm. It can be implemented in either analog or digital form, data storage is not required, it is unconditionally stable, speed of convergence is no longer a problem, and the design is simple. The performance characteristics of a complete orthogonal-lattice-filter network operating in the spacial domain were simulated for example cases of one, two, and three strong incoherent signal sources spaced within a beamwidth for a eight-element linear-array antenna. The adaptive spacial filter patterns and the transient responses demonstrate that the building block has sufficient transient-response speed and control to permit full use of the processing capabilities inherent in a Gram-Schmidt cascade network.
3-D adaptive nonlinear complex-diffusion despeckling filter.
Rodrigues, Pedro; Bernardes, Rui
2012-12-01
This work aims to improve the process of speckle noise reduction while preserving edges and other relevant features through filter expansion from 2-D to 3-D. Despeckling is very important for data visual inspection and as a preprocessing step for other algorithms, as they are usually notably influenced by speckle noise. To that intent, a 3-D approach is proposed for the adaptive complex-diffusion filter. This 3-D iterative filter was applied to spectral-domain optical coherence tomography medical imaging volumes of the human retina and a quantitative evaluation of the results was performed to allow a demonstration of the better performance of the 3-D over the 2-D filtering and to choose the best total diffusion time. In addition, we propose a fast graphical processing unit parallel implementation so that the filter can be used in a clinical setting.
A reduced bias delay lock loop for adaptive filters
NASA Astrophysics Data System (ADS)
Fan, Guangteng; Huang, Yangbo; Su, Yingxue; Li, Jingyuan; Sun, Guangfu
2017-01-01
Narrowband interferences (NBIs) severely degrade the quality of a received signal and can hinder the operation of GPS receivers, and therefore, they are commonly excised using an adaptive transversal filter. This filter does not cause code tracking bias in the case of an ideal analog receiver channel when its magnitude and phase response are constant; however, distortion is induced by RF cables, amplifiers, and mixers that results in an asymmetric correlation function. This correlation function is further deformed by the adaptive transversal filter, resulting in a nonzero bias. Given the adaptive nature of this transversal filter, the bias varies based on the jamming pattern. For precision navigation applications, this bias must be mitigated. With this problem in mind, a new technique called amplitude estimating delay lock loop (AEDLL) is presented. By using data related to a known structure of the adaptive transversal filter, the proposed method only needs to estimate the amplitude of the correlation function and revise the correlation function for code tracking. Simulations show that the AEDLL method is capable of reducing the RMSE of code tracking bias to less than 0.12 ns, which is significantly smaller than that achieved using existing methods.
An Adaptive Kalman Filter Excisor for Suppressing Narrowband Interference
1993-11-01
interferences in- connues. Le filtre de Kalman doit alors "apprendre" ý ajuster un de ses param~tres pour effectuer le meilleur traitement. L’erreur est...4"L l B"• -- -- - - -.- ,_, . An~. A)7cQ 0 -QGOP II liii 111111 IIa( Naional 06fenso I ’ I Deence nitonals I "It AN ADAPTIVE KALMAN FILTER EXCISOR...Ottawa 0 A o~ oO Best Available COpy 4INational Defense Defence nationals AN ADAPTIVE KALMAN FILTER EXCISOR FOR SUPPRESSING NARROWBAND INTERFERENCE by
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.
An adaptive neural fuzzy filter and its applications.
Lin, C T; Juang, C F
1997-01-01
A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning). In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters. In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds. However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes. Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically.
Adaptive conductance filtering for spatially varying noise in PET images
NASA Astrophysics Data System (ADS)
Padfield, Dirk R.; Manjeshwar, Ravindra
2006-03-01
PET images that have been reconstructed with unregularized algorithms are commonly smoothed with linear Gaussian filters to control noise. Since these filters are spatially invariant, they degrade feature contrast in the image, compromising lesion detectability. Edge-preserving smoothing filters can differentially preserve edges and features while smoothing noise. These filters assume spatially uniform noise models. However, the noise in PET images is spatially variant, approximately following a Poisson behavior. Therefore, different regions of a PET image need smoothing by different amounts. In this work, we introduce an adaptive filter, based on anisotropic diffusion, designed specifically to overcome this problem. In this algorithm, the diffusion is varied according to a local estimate of the noise using either the local median or the grayscale image opening to weight the conductance parameter. The algorithm is thus tailored to the task of smoothing PET images, or any image with Poisson-like noise characteristics, by adapting itself to varying noise while preserving significant features in the image. This filter was compared with Gaussian smoothing and a representative anisotropic diffusion method using three quantitative task-relevant metrics calculated on simulated PET images with lesions in the lung and liver. The contrast gain and noise ratio metrics were used to measure the ability to do accurate quantitation; the Channelized Hotelling Observer lesion detectability index was used to quantify lesion detectability. The adaptive filter improved the signal-to-noise ratio by more than 45% and lesion detectability by more than 55% over the Gaussian filter while producing "natural" looking images and consistent image quality across different anatomical regions.
Reversible wavelet filter banks with side informationless spatially adaptive low-pass filters
NASA Astrophysics Data System (ADS)
Abhayaratne, Charith
2011-07-01
Wavelet transforms that have an adaptive low-pass filter are useful in applications that require the signal singularities, sharp transitions, and image edges to be left intact in the low-pass signal. In scalable image coding, the spatial resolution scalability is achieved by reconstructing the low-pass signal subband, which corresponds to the desired resolution level, and discarding other high-frequency wavelet subbands. In such applications, it is vital to have low-pass subbands that are not affected by smoothing artifacts associated with low-pass filtering. We present the mathematical framework for achieving 1-D wavelet transforms that have a spatially adaptive low-pass filter (SALP) using the prediction-first lifting scheme. The adaptivity decisions are computed using the wavelet coefficients, and no bookkeeping is required for the perfect reconstruction. Then, 2-D wavelet transforms that have a spatially adaptive low-pass filter are designed by extending the 1-D SALP framework. Because the 2-D polyphase decompositions are used in this case, the 2-D adaptivity decisions are made nonseparable as opposed to the separable 2-D realization using 1-D transforms. We present examples using the 2-D 5/3 wavelet transform and their lossless image coding and scalable decoding performances in terms of quality and resolution scalability. The proposed 2-D-SALP scheme results in better performance compared to the existing adaptive update lifting schemes.
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
Performance of an Adaptive Matched Filter Using the Griffiths Algorithm
1988-12-01
Simon. Introduction to Adaptive Filters. New York: Macmillan Publishing Company, 1984. 11. Sklar , Bernard . Digital Communications Fundamentals and...York: Harper and Row, 1986. 8. Widrow, Bernard and Samuel D. Stearns. Adaptive Signal Processing. Englewood Cliffs, N.J.: Prentice-Hall, 1985. 9...Fourier Transforms. and Optics. New York: John Wiley and Sons, 1978. 15. Widrow, Bernard and others. "The Complex LMS Algorithm," Proceedings of the IEEE
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.
Enhancing Adaptive Filtering Approaches for Land Data Assimilation Systems
Technology Transfer Automated Retrieval System (TEKTRAN)
Recent work has presented the initial application of adaptive filtering techniques to land surface data assimilation systems. Such techniques are motivated by our current lack of knowledge concerning the structure of large-scale error in either land surface modeling output or remotely-sensed estima...
Robust visual tracking via adaptive kernelized correlation filter
NASA Astrophysics Data System (ADS)
Wang, Bo; Wang, Desheng; Liao, Qingmin
2016-10-01
Correlation filter based trackers have proved to be very efficient and robust in object tracking with a notable performance competitive with state-of-art trackers. In this paper, we propose a novel object tracking method named Adaptive Kernelized Correlation Filter (AKCF) via incorporating Kernelized Correlation Filter (KCF) with Structured Output Support Vector Machines (SOSVM) learning method in a collaborative and adaptive way, which can effectively handle severe object appearance changes with low computational cost. AKCF works by dynamically adjusting the learning rate of KCF and reversely verifies the intermediate tracking result by adopting online SOSVM classifier. Meanwhile, we bring Color Names in this formulation to effectively boost the performance owing to its rich feature information encoded. Experimental results on several challenging benchmark datasets reveal that our approach outperforms numerous state-of-art trackers.
A new clutter rejection algorithm for Doppler ultrasound.
Cloutier, Guy; Chen, Danmin; Durand, Louis-Gilles
2003-04-01
Several strategies, known as clutter or wall Doppler filtering, were proposed to remove the strong echoes produced by stationary or slow moving tissue structures from the Doppler blood flow signal. In this study, the matching pursuit (MP) method is proposed to remove clutter components. The MP method decomposes the Doppler signal into wavelet atoms that are selected in a decreasing energy order. Thus, the high-energy clutter components are extracted first. In the present study, the pulsatile Doppler signal s(n) was simulated by a sum of random-phase sinusoids. Two types of high-amplitude clutter signals were then superimposed on s(n): time-varying low-frequency components, covering systole and early diastole, and short transient clutter signals, distributed within the whole cardiac cycle. The Doppler signals were modeled with the MP method and the most dominant atoms were subtracted from the time-domain signal s(n) until the signal-to-clutter (S/C) ratio reached a maximum. For the low-frequency clutter signal, the improvement in S/C ratio was 19.0 +/- 0.6 dB, and 72.0 +/- 4.5 atoms were required to reach this performance. For the transient clutter signal, ten atoms were required and the maximum improvement in S/C ratio was 5.5 +/- 0.5 dB. The performance of the MP method was also tested on real data recorded over the common carotid artery of a normal subject. Removing 15 atoms significantly improved the appearance of the Doppler sonogram contaminated with low-frequency clutter. Many more atoms (over 200) were required to remove transient clutter components. These results suggest the possibility of using this signal processing approach to implement clutter rejection filters on ultrasound commercial instruments.
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.
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.
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
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.
Selected annotated bibliographies for adaptive filtering of digital image data
Mayers, Margaret; Wood, Lynnette
1988-01-01
Digital spatial filtering is an important tool both for enhancing the information content of satellite image data and for implementing cosmetic effects which make the imagery more interpretable and appealing to the eye. Spatial filtering is a context-dependent operation that alters the gray level of a pixel by computing a weighted average formed from the gray level values of other pixels in the immediate vicinity.Traditional spatial filtering involves passing a particular filter or set of filters over an entire image. This assumes that the filter parameter values are appropriate for the entire image, which in turn is based on the assumption that the statistics of the image are constant over the image. However, the statistics of an image may vary widely over the image, requiring an adaptive or "smart" filter whose parameters change as a function of the local statistical properties of the image. Then a pixel would be averaged only with more typical members of the same population. This annotated bibliography cites some of the work done in the area of adaptive filtering. The methods usually fall into two categories, (a) those that segment the image into subregions, each assumed to have stationary statistics, and use a different filter on each subregion, and (b) those that use a two-dimensional "sliding window" to continuously estimate the filter either the spatial or frequency domain, or may utilize both domains. They may be used to deal with images degraded by space variant noise, to suppress undesirable local radiometric statistics while enforcing desirable (user-defined) statistics, to treat problems where space-variant point spread functions are involved, to segment images into regions of constant value for classification, or to "tune" images in order to remove (nonstationary) variations in illumination, noise, contrast, shadows, or haze.Since adpative filtering, like nonadaptive filtering, is used in image processing to accomplish various goals, this bibliography
Kalman filtering to suppress spurious signals in adaptive optics control.
Poyneer, Lisa A; Véran, Jean-Pierre
2010-11-01
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.
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 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.
Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images.
Xu, Xiaoyin; Miller, Eric L; Chen, Dongbin; Sarhadi, Mansoor
2004-02-01
In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the second filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.
Parameter testing for lattice filter based adaptive modal control systems
NASA Technical Reports Server (NTRS)
Sundararajan, N.; Williams, J. P.; Montgomery, R. C.
1983-01-01
For Large Space Structures (LSS), an adaptive control system is highly desirable. The present investigation is concerned with an 'indirect' adaptive control scheme wherein the system order, mode shapes, and modal amplitudes are estimated on-line using an identification scheme based on recursive, least-squares, lattice filters. Using the identified model parameters, a modal control law based on a pole-placement scheme with the objective of vibration suppression is employed. A method is presented for closed loop adaptive control of a flexible free-free beam. The adaptive control scheme consists of a two stage identification scheme working in series and a modal pole placement control scheme. The main conclusion from the current study is that the identified parameters cannot be directly used for controller design purposes.
Adaptive-filter models of the cerebellum: computational analysis.
Dean, Paul; Porrill, John
2008-01-01
Many current models of the cerebellar cortical microcircuit are equivalent to an adaptive filter using the covariance learning rule. The adaptive filter is a development of the original Marr-Albus framework that deals naturally with continuous time-varying signals, thus addressing the issue of 'timing' in cerebellar function, and it can be connected in a variety of ways to other parts of the system, consistent with the microzonal organization of cerebellar cortex. However, its computational capacities are not well understood. Here we summarise the results of recent work that has focused on two of its intrinsic properties. First, an adaptive filter seeks to decorrelate its (mossy fibre) inputs from a (climbing fibre) teaching signal. This procedure can be used both for sensory processing, e.g. removal of interference from sensory signals, and for learning accurate motor commands, by decorrelating an efference copy of those commands from a sensory signal of inaccuracy. As a model of the cerebellum the adaptive filter thus forms a natural link between events at the cellular level, such as forms of synaptic plasticity and the learning rules they embody, and intelligent behaviour at the system level. Secondly, it has been shown that the covariance learning rule enables the filter to handle input and intrinsic noise optimally. Such optimality may underlie the recently described role of the cerebellum in producing accurate smooth pursuit eye movements in the face of sensory noise. Moreover, it has the consequence of driving most input weights to very small values, consistent with experimental data that many parallel-fibre synapses are normally silent. The effectiveness of silent synapses can only be altered by LTP, so learning tasks depending on a reduction of Purkinje cell firing require the synapses to be embedded in a second, inhibitory pathway from parallel fibre to Purkinje cell. This pathway and the appropriate climbing-fibre related plasticity have been described
Warnecke, Michaela; Chiu, Chen; Engelberg, Jonathan; Moss, Cynthia F
2015-09-01
In their natural environment, big brown bats forage for small insects in open spaces, as well as in vegetation and in the presence of acoustic clutter. While searching and hunting for prey, bats experience sonar interference, not only from densely cluttered environments, but also from calls of conspecifics foraging in close proximity. Previous work has shown that when two bats compete for a single prey item in a relatively open environment, one of the bats may go silent for extended periods of time, which can serve to minimize sonar interference between conspecifics. Additionally, pairs of big brown bats have been shown to adjust frequency characteristics of their vocalizations to avoid acoustic interference in echo processing. In this study, we extended previous work by examining how the presence of conspecifics and environmental clutter influence the bat's echolocation behavior. By recording multichannel audio and video data of bats engaged in insect capture in open and cluttered spaces, we quantified the bats' vocal and flight behaviors. Big brown bats flew individually and in pairs in an open and cluttered room, and the results of this study shed light on the different strategies that this species employs to negotiate a complex and dynamic environment.
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.
A novel adaptive noise filtering method for SAR images
NASA Astrophysics Data System (ADS)
Li, Weibin; He, Mingyi
2009-08-01
In the most application situation, signal or image always is corrupted by additive noise. As a result there are mass methods to remove the additive noise while few approaches can work well for the multiplicative noise. The paper presents an improved MAP-based filter for multiplicative noise by adaptive window denoising technique. A Gamma noise models is discussed and a preprocessing technique to differential the matured and un-matured pixel is applied to get accurate estimate for Equivalent Number of Looks. Also the adaptive local window growth and 3 different denoise strategies are applied to smooth noise while keep its subtle information according to its local statistics feature. The simulation results show that the performance is better than existing filter. Several image experiments demonstrate its theoretical 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.
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.
Microseismic event denoising via adaptive directional vector median filters
NASA Astrophysics Data System (ADS)
Zheng, Jing; Lu, Ji-Ren; Jiang, Tian-Qi; Liang, Zhe
2017-01-01
We present a novel denoising scheme via Radon transform-based adaptive vector directional median filters named adaptive directional vector median filter (AD-VMF) to suppress noise for microseismic downhole dataset. AD-VMF contains three major steps for microseismic downhole data processing: (i) applying Radon transform on the microseismic data to obtain the parameters of the waves, (ii) performing S-transform to determine the parameters for filters, and (iii) applying the parameters for vector median filter (VMF) to denoise the data. The steps (i) and (ii) can realize the automatic direction detection. The proposed algorithm is tested with synthetic and field datasets that were recorded with a vertical array of receivers. The P-wave and S-wave direct arrivals are properly denoised for poor signal-to-noise ratio (SNR) records. In the simulation case, we also evaluate the performance with mean square error (MSE) in terms of signal-to-noise ratio (SNR). The result shows that the distortion of the proposed method is very low; the SNR is even less than 0 dB.
A Kalman filter approach to adaptive estimation of multispectral signatures
NASA Technical Reports Server (NTRS)
Crane, R. B.
1973-01-01
The signatures of remote sensing data from agricultural crops exhibit significant non-stationarity, so that the performance of fixed parameter classifiers degenerates with time and distance from the initial training data. A class of adaptive decision-directed classifiers are being developed, based on Kalman filter theory. Limited results to date on two data sets indicate approximately a 25 to 40% reduction in rates of misclassification.
Doppler characteristics of sea clutter.
Raynal, Ann Marie; Doerry, Armin Walter
2010-06-01
Doppler radars can distinguish targets from clutter if the target's velocity along the radar line of sight is beyond that of the clutter. Some targets of interest may have a Doppler shift similar to that of clutter. The nature of sea clutter is different in the clutter and exo-clutter regions. This behavior requires special consideration regarding where a radar can expect to find sea-clutter returns in Doppler space and what detection algorithms are most appropriate to help mitigate false alarms and increase probability of detection of a target. This paper studies the existing state-of-the-art in the understanding of Doppler characteristics of sea clutter and scattering from the ocean to better understand the design and performance choices of a radar in differentiating targets from clutter under prevailing sea conditions.
Image super-resolution via adaptive filtering and regularization
NASA Astrophysics Data System (ADS)
Ren, Jingbo; Wu, Hao; Dong, Weisheng; Shi, Guangming
2014-11-01
Image super-resolution (SR) is widely used in the fields of civil and military, especially for the low-resolution remote sensing images limited by the sensor. Single-image SR refers to the task of restoring a high-resolution (HR) image from the low-resolution image coupled with some prior knowledge as a regularization term. One classic method regularizes image by total variation (TV) and/or wavelet or some other transform which introduce some artifacts. To compress these shortages, a new framework for single image SR is proposed by utilizing an adaptive filter before regularization. The key of our model is that the adaptive filter is used to remove the spatial relevance among pixels first and then only the high frequency (HF) part, which is sparser in TV and transform domain, is considered as the regularization term. Concretely, through transforming the original model, the SR question can be solved by two alternate iteration sub-problems. Before each iteration, the adaptive filter should be updated to estimate the initial HF. A high quality HF part and HR image can be obtained by solving the first and second sub-problem, respectively. In experimental part, a set of remote sensing images captured by Landsat satellites are tested to demonstrate the effectiveness of the proposed framework. Experimental results show the outstanding performance of the proposed method in quantitative evaluation and visual fidelity compared with the state-of-the-art methods.
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.
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 Kalman filtering for histogram-based appearance learning in infrared imagery.
Venkataraman, Vijay; Fan, Guoliang; Havlicek, Joseph P; Fan, Xin; Zhai, Yan; Yeary, Mark B
2012-11-01
Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors, including complex target maneuvers, ego-motion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection process and track lock over extended time periods. Two key issues in overcoming this problem are how to represent the target and how to learn its appearance online. In this paper, we adopt a recent appearance model that estimates the pixel intensity histograms as well as the distribution of local standard deviations in both the foreground and background regions for robust target representation. Appearance learning is then cast as an adaptive Kalman filtering problem where the process and measurement noise variances are both unknown. We formulate this problem using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Although convergence of the ALS algorithm is guaranteed only for the case of globally wide sense stationary process and measurement noises, we demonstrate for the first time that the technique can often be applied with great effectiveness under the much weaker assumption of piecewise stationarity. The performance advantages of the ALS method relative to the classical covariance matching are illustrated by means of simulated stationary and nonstationary systems. Against real data, our results show that the ALS-based algorithm outperforms the covariance matching as well as the traditional histogram similarity-based methods, achieving sub-pixel tracking accuracy against the well-known AMCOM closure sequences and the recent SENSIAC automatic target recognition dataset.
Comparison between digital Doppler filtering processes applied to radar signals
NASA Astrophysics Data System (ADS)
Desodt, G.
1983-10-01
Two families of Doppler processes based on FFT and FIR filters, respectively, are compared in terms of hardware complexity and performance. It is shown that FIR filter banks are characterized by better performance than FFT filter banks. For the same number of pulses, the FIR processor permits a better clutter rejection and greater bandwidth than the FFT one. Also, an FIR-based bank has a much simpler and more adaptable architecture than an FFT-based bank.
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.
A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.
Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao
2016-12-19
The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.
Adaptive probabilistic collocation based Kalman filter for unsaturated flow problem
NASA Astrophysics Data System (ADS)
Man, J.; Li, W.; Zeng, L.; Wu, L.
2015-12-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively 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 to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so called "cure of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF is even more computationally expensive than EnKF. Motivated by recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problem. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to alleviate the inconsistency between model parameters and states. The performance of RAPCKF is tested by unsaturated flow numerical cases. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.
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.
Image denoising using a directional adaptive diffusion filter
NASA Astrophysics Data System (ADS)
Zhao, Cuifang; Shi, Caicheng; He, Peikun
2006-11-01
Partial differential equations (PDEs) are well-known due to their good processing results which it can not only smooth the noise but also preserve the edges. But the shortcomings of these processes came to being noticed by people. In some sense, PDE filter is called "cartoon model" as it produces an approximation of the input image, use the same diffusion model and parameters to process noise and signal because it can not differentiate them, therefore, the image is naturally modified toward piecewise constant functions. A new method called a directional adaptive diffusion filter is proposed in the paper, which combines PDE mode with wavelet transform. The undecimated discrete wavelet transform (UDWT) is carried out to get different frequency bands which have obviously directional selectivity and more redundancy details. Experimental results show that the proposed method provides a performance better to preserve textures, small details and global information.
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.
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.
2009-09-30
height above the seabed of 18 m ( water depth was ~165m) in an area where there were few/no seabed features. Due to the proximity of the source and...understanding of the mechanisms associated with sonar clutter in shallow water . The statistical characterization of these features will lead to clutter...3285, 2007. 5 6 [5] Holland C.W., J.R. Preston, and D.A. Abraham, Long-range acoustic scattering from a shallow- water mud volcano cluster J
Attitude determination using an adaptive multiple model filtering Scheme
NASA Technical Reports Server (NTRS)
Lam, Quang; Ray, Surendra N.
1995-01-01
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
Noninvasive fetal ECG estimation using adaptive comb filter.
Wei, Zheng; Xueyun, Wei; Jian jian, Zhong; Hongxing, Liu
2013-10-01
This paper describes a robust and simple algorithm for fetal electrocardiogram (FECG) estimation from abdominal signal using adaptive comb filter (ACF). The ACF can adjust itself to the temporal variations in fundamental frequency, which makes it qualified for the estimation of quasi-periodic component from physiologic signal, such as ECG. The validity and performance of the described method are confirmed through experiments on real fetal ECG data. A comparison with the well-known independent component analysis (ICA) method has also been presented.
Jeong, Jinsoo
2011-01-01
This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC) structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure.
Adaptive de-blocking filter for low bit rate applications
NASA Astrophysics Data System (ADS)
Jin, Xin; Zhu, Guangxi
2006-01-01
In block-based video compression technology, blocking artifacts are obvious because of the luminance and chrominance discontinuities which are caused by block-based discrete cosine transform (DCT) and motion compensation. As a kind of solution, an in-loop filter has been successfully used in H.264 adapting to quantization parameter and video content. In this paper, blocking artifacts distribution properties are analyzed carefully to reflect the blocking effect more accurately in the low bit rate applications. Two important parameters, named blocking severity and pixel variation, are defined to describe the boundary strength and the gradient of the samples across the edge respectively. Through series of statistical data retrieval and analysis for these parameters using multiple representative video sequences, a novel blocking artifacts distribution model is concluded. Based on this distribution model, an improved filter is proposed to H.264 with novel strength determination rule and different alpha model. Comparing with H.264 anchor results, the proposed de-blocking filter shows better performance especially in subjective aspect, which could be widely used in low bit rate applications.
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.
Residual mode filters and adaptive control in large space structures
NASA Technical Reports Server (NTRS)
Davidson, Roger A.; Balas, Mark J.
1989-01-01
One of the most difficult problems in controlling large systems and structures is compensating for the destructive interaction which can occur between the reduced-order model (ROM) of the plant, which is used by the controller, and the unmodeled dynamics of the plant, often called the residual modes. The problem is more significant in the case of large space structures because their naturally light damping and high performance requirements lead to more frequent, destructive residual mode interaction (RMI). Using the design/compensation technique of residual mode filters (RMF's), effective compensation of RMI can be accomplished in a straightforward manner when using linear controllers. The use of RMF's has been shown to be effective for a variety of large structures, including a space-based laser and infinite dimensional systems. However, the dynamics of space structures is often uncertain and may even change over time due to on-orbit erosion from space debris and corrosive chemicals in the upper atmosphere. In this case, adaptive control can be extremely beneficial in meeting the performance requirements of the structure. Adaptive control for large structures is also based on ROM's and so destructive RMI may occur. Unfortunately, adaptive control is inherently nonlinear, and therefore the known results of RMF's cannot be applied. The purpose is to present the results of new research showing the effects of RMI when using adaptive control and the work which will hopefully lead to RMF compensation of this problem.
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
Evaluating the adaptive-filter model of the cerebellum.
Dean, Paul; Porrill, John
2011-07-15
The adaptive-filter model of the cerebellar microcircuit is in widespread use, combining as it does an explanation of key microcircuit features with well-specified computational power. Here we consider two methods for its evaluation. One is to test its predictions concerning relations between cerebellar inputs and outputs. Where the relevant experimental data are available, e.g. for the floccular role in image stabilization, the predictions appear to be upheld. However, for the majority of cerebellar microzones these data have yet to be obtained. The second method is to test model predictions about details of the microcircuit. We focus on features apparently incompatible with the model, in particular non-linear patterns in Purkinje cell simple-spike firing. Analysis of these patterns suggests the following three conclusions. (i) It is important to establish whether they can be observed during task-related behaviour. (ii) Highly non-linear models based on these patterns are unlikely to be universal, because they would be incompatible with the (approximately) linear nature of floccular function. (iii) The control tasks for which these models are computationally suited need to be identified. At present, therefore, the adaptive filter remains a candidate model of at least some cerebellar microzones, and its evaluation suggests promising lines for future enquiry.
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.
Suppression of impulse noise in medical images with the use of Fuzzy Adaptive Median Filter.
Toprak, Abdullah; Güler, Inan
2006-12-01
A new rule based fuzzy filter for removal of highly impulse noise, called Rule Based Fuzzy Adaptive Median (RBFAM) Filter, is aimed to be discussed in this paper. The RBFAM filter is an improved version of Adaptive Median Filter (AMF) and is presented in the aim of noise reduction of images corrupted with additive impulse noise. The filter has three stages. Two of those stages are fuzzy rule based and last stage is based on standard median and adaptive median filter. The proposed filter can preserve image details better then AMF while suppressing additive salt & pepper or impulse type noise. In this paper, we placed our preference on bell-shaped membership function instead of triangular membership function in order to observe better results. Experimental results indicates that the proposed filter is improvable with increased fuzzy rules to reduce more noise corrupted images and to remove salt and pepper noise in a more effective way than what AMF filter does.
NASA Astrophysics Data System (ADS)
Zhu, Binqi; Gao, Yesheng; Wang, Kaizhi; Liu, Xingzhao
2016-04-01
A computational method for suppressing clutter and generating clear microwave images of targets is proposed in this paper, which combines synthetic aperture radar (SAR) principles with recursive method and waveform design theory, and it is suitable for SAR for special applications. The nonlinear recursive model is introduced into the SAR operation principle, and the cubature Kalman filter algorithm is used to estimate target and clutter responses in each azimuth position based on their previous states, which are both assumed to be Gaussian distributions. NP criteria-based optimal waveforms are designed repeatedly as the sensor flies along its azimuth path and are used as the transmitting signals. A clutter suppression filter is then designed and added to suppress the clutter response while maintaining most of the target response. Thus, with fewer disturbances from the clutter response, we can generate the SAR image with traditional azimuth matched filters. Our simulations show that the clutter suppression filter significantly reduces the clutter response, and our algorithm greatly improves the SINR of the SAR image based on different clutter suppression filter parameters. As such, this algorithm may be preferable for special target imaging when prior information on the target is available.
2007-09-30
the processes that control the lens formation may be related to gas and that this may either directly (e.g., bubbles in the water ) or indirectly (via...hyperbolic arrival shape of arrivals that have traversed a path with an interval velocity equal to the reducing velocity. The water -sediment interface...main results from other analyses during FY07 • Shallow water mud volcanoes have a high potential for leading to sonar clutter, having large target
2008-09-30
shallow water 1 Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to...claims to the contrary in the ocean acoustics community, sub-bottom clutter can and does occur in shallow water environments. A step-by-step detailed...that of the overlying water column and hence there is no critical angle. Thus, the reflection, scattering, propagation and reverberation are
Adaptive Current Control Method for Hybrid Active Power Filter
NASA Astrophysics Data System (ADS)
Chau, Minh Thuyen
2016-09-01
This paper proposes an adaptive current control method for Hybrid Active Power Filter (HAPF). It consists of a fuzzy-neural controller, identification and prediction model and cost function. The fuzzy-neural controller parameters are adjusted according to the cost function minimum criteria. For this reason, the proposed control method has a capability on-line control clings to variation of the load harmonic currents. Compared to the single fuzzy logic control method, the proposed control method shows the advantages of better dynamic response, compensation error in steady-state is smaller, able to online control is better and harmonics cancelling is more effective. Simulation and experimental results have demonstrated the effectiveness of the proposed control method.
Adaptive filtering for white-light LED visible light communication
NASA Astrophysics Data System (ADS)
Hsu, Chin-Wei; Chen, Guan-Hong; Wei, Liang-Yu; Chow, Chi-Wai; Lu, I.-Cheng; Liu, Yen-Liang; Chen, Hsing-Yu; Yeh, Chien-Hung; Liu, Yang
2017-01-01
White-light phosphor-based light-emitting diode (LED) can be used to provide lighting and visible light communication (VLC) simultaneously. However, the long relaxation time of phosphor can reduce the modulation bandwidth and limit the VLC data rate. Recent VLC works focus on improving the LED modulation bandwidths. Here, we propose and demonstrate the use of adaptive Volterra filtering (AVF) to increase the data rate of a white-light LED VLC system. The detailed algorithm and implementation of the AVF for the VLC system have been discussed. Using our proposed electrical frontend circuit and the proposed AVF, a significant data rate enhancement to 700.68 Mbit/s is achieved after 1-m free-space transmission using a single white-light phosphor-based LED.
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.
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.
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
2015-01-01
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. PMID:25648711
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.
Adaptive Wiener filter super-resolution of color filter array images.
Karch, Barry K; Hardie, Russell C
2013-08-12
Digital color cameras using a single detector array with a Bayer color filter array (CFA) require interpolation or demosaicing to estimate missing color information and provide full-color images. However, demosaicing does not specifically address fundamental undersampling and aliasing inherent in typical camera designs. Fast non-uniform interpolation based super-resolution (SR) is an attractive approach to reduce or eliminate aliasing and its relatively low computational load is amenable to real-time applications. The adaptive Wiener filter (AWF) SR algorithm was initially developed for grayscale imaging and has not previously been applied to color SR demosaicing. Here, we develop a novel fast SR method for CFA cameras that is based on the AWF SR algorithm and uses global channel-to-channel statistical models. We apply this new method as a stand-alone algorithm and also as an initialization image for a variational SR algorithm. This paper presents the theoretical development of the color AWF SR approach and applies it in performance comparisons to other SR techniques for both simulated and real data.
MR images restoration with the use of fuzzy filter having adaptive membership parameters.
Güler, I; Toprak, A; Demirhan, A; Karakiş, R
2008-06-01
A new fuzzy adaptive median filter is presented for the noise reduction of magnetic resonance images corrupted with heavy impulse (salt and pepper) noise. In this paper, we have proposed a Fuzzy Adaptive Median Filter with Adaptive Membership Parameters (FAMFAMP) for removing highly corrupted salt and pepper noise, with preserving image edges and details. The FAMFAMP filter is an improved version of Adaptive Median Filter (AMF) and is presented in the aim of noise reduction of images corrupted with additive impulse noise. The proposed filter can preserve image details better than AMF while suppressing additive salt and pepper or impulse type noise. In this paper, we placed our preference on bell-shaped membership function with adaptive parameters instead of triangular membership function without variable coefficients in order to observe better results.
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.
Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V
2015-01-01
Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.
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.
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
A gradient-adaptive lattice-based complex adaptive notch filter
NASA Astrophysics Data System (ADS)
Zhu, Rui; Yang, Feiran; Yang, Jun
2016-12-01
This paper presents a new complex adaptive notch filter to estimate and track the frequency of a complex sinusoidal signal. The gradient-adaptive lattice structure instead of the traditional gradient one is adopted to accelerate the convergence rate. It is proved that the proposed algorithm results in unbiased estimations by using the ordinary differential equation approach. The closed-form expressions for the steady-state mean square error and the upper bound of step size are also derived. Simulations are conducted to validate the theoretical analysis and demonstrate that the proposed method generates considerably better convergence rates and tracking properties than existing methods, particularly in low signal-to-noise ratio environments.
NASA Astrophysics Data System (ADS)
Shen, Ting-ao; Li, Hua-nan; Zhang, Qi-xin; Li, Ming
2017-02-01
The convergence rate and the continuous tracking precision are two main problems of the existing adaptive notch filter (ANF) for frequency tracking. To solve the problems, the frequency is detected by interpolation FFT at first, which aims to overcome the convergence rate of the ANF. Then, referring to the idea of negative feedback, an evaluation factor is designed to monitor the ANF parameters and realize continuously high frequency tracking accuracy. According to the principle, a novel adaptive frequency estimation algorithm based on interpolation FFT and improved ANF is put forward. Its basic idea, specific measures and implementation steps are described in detail. The proposed algorithm obtains a fast estimation of the signal frequency, higher accuracy and better universality qualities. Simulation results verified the superiority and validity of the proposed algorithm when compared with original algorithms.
A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation
Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao
2016-01-01
The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361
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.
Adaptive mean filtering for noise reduction in CT polymer gel dosimetry
Hilts, Michelle; Jirasek, Andrew
2008-01-15
X-ray computed tomography (CT) as a method of extracting 3D dose information from irradiated polymer gel dosimeters is showing potential as a practical means to implement gel dosimetry in a radiation therapy clinic. However, the response of CT contrast to dose is weak and noise reduction is critical in order to achieve adequate dose resolutions with this method. Phantom design and CT imaging technique have both been shown to decrease image noise. In addition, image postprocessing using noise reduction filtering techniques have been proposed. This work evaluates in detail the use of the adaptive mean filter for reducing noise in CT gel dosimetry. Filter performance is systematically tested using both synthetic patterns mimicking a range of clinical dose distribution features as well as actual clinical dose distributions. Both low and high signal-to-noise ratio (SNR) situations are examined. For all cases, the effects of filter kernel size and the number of iterations are investigated. Results indicate that adaptive mean filtering is a highly effective tool for noise reduction CT gel dosimetry. The optimum filtering strategy depends on characteristics of the dose distributions and image noise level. For low noise images (SNR {approx}20), the filtered results are excellent and use of adaptive mean filtering is recommended as a standard processing tool. For high noise images (SNR {approx}5) adaptive mean filtering can also produce excellent results, but filtering must be approached with more caution as spatial and dose distortions of the original dose distribution can occur.
Image Restoration on Copper Inscription Using Nonlinear Filtering and Adaptive Threshold
NASA Astrophysics Data System (ADS)
Chairy, A.; Suprapto, Y. K.; Yuniarno, E. M.
2017-01-01
Inscription is an important document inherited by history of kingdom. Inscription made on hard stuff such as stone and copper. Therefore it is necessary digitizing documents, to keep the authenticity of the document. But the document of the historical heritage have disruption on inscription plate which be called noise. So that, it is necessary to reduce the noise in the image of the inscription, to ease the documentation of historical digital. Then, separation between the background and the writing object carved on inscription is conducted so easy to read. This research is using nonlinear filtering method to reduce the noise and adaptive threshold to separate between the background and letter inscription. Nonlinear filtering method used is median filter, harmonic mean filter and contra harmonic mean filter, whereas in the adaptive threshold using adaptive mean and adaptive median threshold. The results of this research is using measurement methods MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and SNR (Signal to Noise Ratio).
NASA Astrophysics Data System (ADS)
Kobayashi, Taizo; Kato, Daiki; Koga, Hiroyuki; Morimoto, Kenichi; Fukuda, Makoto; Kinoshita, Yoshiharu; Yoshida, Hiroshi; Konishi, Satoshi
This paper proposes a cooperative operation of serially connected membrane filters toward adaptive blood cell separation system in order to overcome a restriction of a single membrane filter. Serially connected membrane filters allow that downstream filters extract blood plasma from residual blood at upstream filters. Consequently, it becomes possible to adapt filtering characteristics to changing properties of blood. We focus on trans-membrane pressure difference in order to prevent hemolysis. Our strategy can be realized as a miniaturized PDMS fluidic chip. Our laboratory experiment using a prototype shows that plasma extraction efficiency is improved from 34% to 75%. Toward an integrated system, this paper also demonstrates multiple filters are successfully integrated into a PDMS fluidic chip.
An algorithmic approach to adaptive state filtering using recurrent neural networks.
Parlos, A G; Menon, S K; Atiya, A
2001-01-01
Practical algorithms are presented for adaptive state filtering in nonlinear dynamic systems when the state equations are unknown. The state equations are constructively approximated using neural networks. The algorithms presented are based on the two-step prediction-update approach of the Kalman filter. The proposed algorithms make minimal assumptions regarding the underlying nonlinear dynamics and their noise statistics. Non-adaptive and adaptive state filtering algorithms are presented with both off-line and online learning stages. The algorithms are implemented using feedforward and recurrent neural network and comparisons are presented. Furthermore, extended Kalman filters (EKFs) are developed and compared to the filter algorithms proposed. For one of the case studies, the EKF converges but results in higher state estimation errors that the equivalent neural filters. For another, more complex case study with unknown system dynamics and noise statistics, the developed EKFs do not converge. The off-line trained neural state filters converge quite rapidly and exhibit acceptable performance. Online training further enhances the estimation accuracy of the developed adaptive filters, effectively decoupling the eventual filter accuracy from the accuracy of the process model.
Performance evaluation of correlation filters for target tracking
NASA Astrophysics Data System (ADS)
Gaxiola, Leopoldo N.; Diaz-Ramirez, Victor H.; Tapia, Juan J.; García-Martínez, Pascuala; Cuevas, Andres
2015-09-01
A performance evaluation of several state-of-the-art correlation filters within the context of target tracking is presented. The filters are tested using an introduced algorithm that is adapted online using information of current and past scene frames of the scene. The algorithm achieves a high-rate operation by focusing signal processing on a small fragment of the scene in each frame. The correlation filters are tested using several video test sequences that contain geometric modifications of the target, partial occlusions and clutter. The performance of the tested filters is characterized in terms of detection efficiency, tracking accuracy, and computational complexity using objective metrics.
Heterogeneous Multiple Sensors Joint Tracking of Maneuvering Target in Clutter
Wu, Panlong; Li, Xingxiu; Kong, Jianshou; Liu, Jiale
2015-01-01
To solve the problem of tracking maneuvering airborne targets in the presence of clutter, an improved interacting multiple model probability data association algorithm (IMMPDA-MDCM) using radar/IR sensors fusion is proposed. Under the architecture of the proposed algorithm, the radar/IR centralized fusion tracking scheme of IMMPDA-MDCM is designed to guarantee the observability of the target state. The interacting multiple model (IMM) deals with the model switching. The modified debiased converted measurement (MDCM) filter accounts for non-linearity in the dynamic system models, and reduces the effect of measurement noise on the covariance effectively. The probability data association (PDA) handles data association and measurement uncertainties in clutter. The simulation results show that the proposed algorithm can improve the tracking precision for maneuvering target in clutters, and has higher tracking precision than the traditional IMMPDA based on EKF and IMMPDA based on DCM algorithm. PMID:26193279
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.
Hanna, Andrew I; Mandic, Danilo P
2003-03-01
A complex-valued nonlinear gradient descent (CNGD) learning algorithm for a simple finite impulse response (FIR) nonlinear neural adaptive filter with an adaptive amplitude of the complex activation function is proposed. This way the amplitude of the complex-valued analytic nonlinear activation function of a neuron in the learning algorithm is made gradient adaptive to give the complex-valued adaptive amplitude nonlinear gradient descent (CAANGD). Such an algorithm is beneficial when dealing with signals that have rich dynamical behavior. Simulations on the prediction of complex-valued coloured and nonlinear input signals show the gradient adaptive amplitude, CAANGD, outperforming the standard CNGD algorithm.
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.
Waveform design for cognitive radar: target detection in heavy clutter
NASA Astrophysics Data System (ADS)
Kirk, Benjamin H.; Narayanan, Ram M.; Martone, Anthony F.; Sherbondy, Kelly D.
2016-05-01
In many applications of radar systems, detection of targets in environments with heavy clutter and interference can be difficult. It is desired that a radar system should detect targets at a further range as well as be able to detect these targets with very few false positive or negative readings. In a cognitive radar system, there are ways that these negative effects can be mitigated and target detection can be significantly improved. An important metric to focus on for increasing target detectability is the signal-to-clutter ratio (SCR). Cognitive radar offers solutions to issues such as this with the use of a priori knowledge of targets and environments as well as real time adaptations. A feature of cognitive radar that is of interest is the ability to adapt and optimize transmitted waveforms to a given situation. A database is used to hold a priori and dynamic knowledge of the operational environment and targets to be detected, such as clutter characteristics and target radar cross-section (RCS) estimations. Assuming this knowledge is available or can be estimated in real-time, the transmitted waveform can be tailored using methods such as transmission of a spectrum corresponding to the target-to-clutter ratio (TCR). These methods provide significant improvement in distinguishing targets from clutter or interference.
Extraction of a Weak Co-Channel Interfering Communication Signal Using Adaptive Filtering
2015-03-01
unlimited 12b. DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) Conventional separation techniques such as filters cannot be used in a scenario where a...to achieve a reasonable error rate. 14. SUBJECT TERMS Adaptive filter, signal separation 15. NUMBER OF PAGES 71 16. PRICE CODE 17. SECURITY...INTENTIONALLY LEFT BLANK iv ABSTRACT Conventional separation techniques such as filters cannot be used in a scenario where a weak signal is embedded
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
Adaptive Spatial Filtering with Principal Component Analysis for Biomedical Photoacoustic Imaging
NASA Astrophysics Data System (ADS)
Nagaoka, Ryo; Yamazaki, Rena; Saijo, Yoshifumi
Photoacoustic (PA) signal is very sensitive to noise generated by peripheral equipment such as power supply, stepping motor or semiconductor laser. Band-pass filter is not effective because the frequency bandwidth of the PA signal also covers the noise frequency. The objective of the present study is to reduce the noise by using an adaptive spatial filter with principal component analysis (PCA).
New approaches for dim target detection and clutter rejection
NASA Astrophysics Data System (ADS)
Liou, Ren-Jean
1993-01-01
This dissertation presents a new method for clutter rejection and dim target track detection from infrared (IR) satellite data using neural networks. A method referred to as 'high order correlation method' is developed which recursively computes the spatio-temporal cross-correlations between data of consecutive scans. The implementation of this scheme using a connectionist network is also proposed. Several important properties of the high order correlation method are established which indicate that the resultant filtered images capture all the target information. Simulation results using this approach show at least 93% clutter rejection under moderate clutter density. Further improvement in the clutter rejection is achieved by modifying the high order correlation method to incorporate the target motion dynamics. The implementation of this 'modified high order correlation' using a high order neural network architecture is also developed. Simulation results indicate at least 97% clutter rejection rate for this method. To test the performance, experimental studies of this modified high order correlations are conducted under various scenarios which include: multiple target detection, continuous mode operation, various background clutter densities, and detection using variable number of scans and order of correlation. This algorithm performs very well even under many difficult operating environments. A new scoring process is developed to improve the discrimination ability of the modified high order correlation scheme by employing velocity and curvature information. This scoring process is then used to identify each individual track in the scene by using the properties of the modified high order correlation method. This modification not only significantly improves the clutter rejection under very dense clutter environment, but also increases the feasibility of using the modified high order correlation method for other areas such as data association target classification
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
Feasability of adaptive vibration control of a space truss using modal filters and a neural network
NASA Astrophysics Data System (ADS)
Bosse, Albert; Fisher, Shalom; Shelley, Stuart J.; Lim, Tae W.
1996-05-01
An adaptive algorithm is proposed for the control of a large space truss structure which uses modal filters for independent modal space control and a simple neural network that provides an on-line system identification capability. The modal filters are computed off-line using measured frequency response functions and estimated pole values for the modes of interest, and provide a coordinate transformation that yields modal coordinates from physical response measurements. The time histories for the modal coordinates are then processed in real time by the neural network, which models a single degree of freedom system transfer function and provides estimates of modal parameters, namely, frequency, damping ratio and modal gain. The modal filters are used to implement independent modal space control on a 3.74 meter space truss using a single reaction-mass actuator and 32 accelerometers. The performance of the modal filter based controller is compared to that of a local rate feedback controller using the same actuator. The applicability of the adaptive filter to adaptive control is demonstrated by real time estimation of the modal parameters of the truss with and without control. Because the modal filter control gain can be adjusted to maintain a desired closed loop damping ratio, which is tracked by the adaptive filter, adaptive control of individual modes in a time-varying system is possible. The goal of this work is to field a control system which can maintain desired closed loop damping ratios for mode frequency variations as high as 10%.
Ergün, Ayla; Barbieri, Riccardo; Eden, Uri T; Wilson, Matthew A; Brown, Emery N
2007-03-01
The stochastic state point process filter (SSPPF) and steepest descent point process filter (SDPPF) are adaptive filter algorithms for state estimation from point process observations that have been used to track neural receptive field plasticity and to decode the representations of biological signals in ensemble neural spiking activity. The SSPPF and SDPPF are constructed using, respectively, Gaussian and steepest descent approximations to the standard Bayes and Chapman-Kolmogorov (BCK) system of filter equations. To extend these approaches for constructing point process adaptive filters, we develop sequential Monte Carlo (SMC) approximations to the BCK equations in which the SSPPF and SDPPF serve as the proposal densities. We term the two new SMC point process filters SMC-PPFs and SMC-PPFD, respectively. We illustrate the new filter algorithms by decoding the wind stimulus magnitude from simulated neural spiking activity in the cricket cercal system. The SMC-PPFs and SMC-PPFD provide more accurate state estimates at low number of particles than a conventional bootstrap SMC filter algorithm in which the state transition probability density is the proposal density. We also use the SMC-PPFs algorithm to track the temporal evolution of a spatial receptive field of a rat hippocampal neuron recorded while the animal foraged in an open environment. Our results suggest an approach for constructing point process adaptive filters using SMC methods.
Optimal velocity estimation in ultrasound color flow imaging in presence of clutter.
Løvstakken, Lasse; Bjaerum, Steinar; Torp, Hans
2007-03-01
In color flow imaging (CFI), the rejection of tissue clutter signal is treated separately from blood velocity estimation by high-pass filtering the received Doppler signal. The complete suppression of clutter is then difficult to achieve without affecting the subsequent velocity estimates. In this work, a different approach to velocity estimation is investigated, based on a statistical model of the signal from both clutter and blood. An analytic expression for the Cramer-Rao lower bound (CRLB) is developed, and used to determine the existence of an efficient maximum likelihood estimator (MLE) of blood velocity in CFI when assuming full knowledge of the clutter statistics. We further simulate and compare the performance of the MLE to that of the autocorrelation method (ACM) using finite-impulse response (FIR) and polynomial regression clutter filters. Two signal scenarios are simulated, representing a central and peripheral vessel. Simulations showed that, by including 3-9 (independent) spatial points, the MLE variance approached the CRLB in both scenarios. The ACM was approximately unbiased only for the central scenario in the clutter filter pass band, then with a variance of up to four times the CRLB. The ACM suffered from a severe bias in the filter transition region, and a significant performance gain was achieved here using the MLE. For practical use, the clutter properties must be estimated. We finally replaced the known clutter statistics with an estimate obtained from low-rank approximations of the received sample correlation matrix. Used in the model-based framework, this method came close to the performance of the MLE, and it may be an important step toward a practical model-based estimator, including tissue clutter with optimal performance.
Adaptive high temperature superconducting filters for interference rejection
Raihn, K.F.; Fenzi, N.O.; Hey-Shipton, G.L.; Saito, E.R.; Loung, P.V.; Aidnik, D.L.
1996-07-01
An optically switched high temperature superconducting (HTS) band-reject filter bank is presented. Fast low loss switching of high quality (Q) factor HTS filter elements enables digital selection of arbitrary pass-bands and stop-bands. Patterned pieces of GaAs and silicon are used in the manufacture of the photosensitive switches. Fiber optic cabling is used to transfer the optical energy from an LED to the switch. The fiber optic cable minimizes the thermal loading of the filter package and de-couples the switch`s power source from the RF circuit. This paper will discuss the development of a computer-controlled HTS bank of optically switchable, narrow band, high Q bandstop filters which incorporates a cryocooler to maintain the 77 K operating temperature of the HTS microwave circuit.
Adaptive enhancement of magnetoencephalographic signals via multichannel filtering
Lewis, P.S.
1989-01-01
A time-varying spatial/temporal filter for enhancing multichannel magnetoencephalographic (MEG) recordings of evoked responses is described. This filter is based in projections derived from a combination of measured data and a priori models of the expected response. It produces estimates of the evoked fields in single trial measurements. These estimates can reduce the need for signal averaging in some situations. The filter uses the a priori model information to enhance responses where they exist, but avoids creating responses that do not exist. Examples are included of the filter's application to both MEG single trial data containing an auditory evoked field and control data with no evoked field. 5 refs., 7 figs.
Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller.
Lin, Chih-Min; Yang, Ming-Shu; Chao, Fei; Hu, Xiao-Min; Zhang, Jun
2016-10-01
This paper aims to propose an efficient network and applies it as an adaptive filter for the signal processing problems. An adaptive filter is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC. Due to the better ability of handling uncertainties, type-2 fuzzy sets can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so that the convergence of the filtering error can be guaranteed. In order to demonstrate the performance of the proposed adaptive T2FCMAC filter, it is tested in signal processing applications, including a nonlinear channel equalization system, a time-varying channel equalization system, and an adaptive noise cancellation system. The advantages of the proposed filter over the other adaptive filters are verified through simulations.
Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels.
Samsonov, Alexei A; Johnson, Chris R
2004-10-01
Anisotropic diffusion filtering is widely used for MR image enhancement. However, the anisotropic filter is nonoptimal for MR images with spatially varying noise levels, such as images reconstructed from sensitivity-encoded data and intensity inhomogeneity-corrected images. In this work, a new method for filtering MR images with spatially varying noise levels is presented. In the new method, a priori information regarding the image noise level spatial distribution is utilized for the local adjustment of the anisotropic diffusion filter. Our new method was validated and compared with the standard filter on simulated and real MRI data. The noise-adaptive method was demonstrated to outperform the standard anisotropic diffusion filter in both image error reduction and image signal-to-noise ratio (SNR) improvement. The method was also applied to inhomogeneity-corrected and sensitivity encoding (SENSE) images. The new filter was shown to improve segmentation of MR brain images with spatially varying noise levels.
Reduction of background clutter in structured lighting systems
Carlson, Jeffrey J.; Giles, Michael K.; Padilla, Denise D.; Davidson, Jr., Patrick A.; Novick, David K.; Wilson, Christopher W.
2010-06-22
Methods for segmenting the reflected light of an illumination source having a characteristic wavelength from background illumination (i.e. clutter) in structured lighting systems can comprise pulsing the light source used to illuminate a scene, pulsing the light source synchronously with the opening of a shutter in an imaging device, estimating the contribution of background clutter by interpolation of images of the scene collected at multiple spectral bands not including the characteristic wavelength and subtracting the estimated background contribution from an image of the scene comprising the wavelength of the light source and, placing a polarizing filter between the imaging device and the scene, where the illumination source can be polarized in the same orientation as the polarizing filter. Apparatus for segmenting the light of an illumination source from background illumination can comprise an illuminator, an image receiver for receiving images of multiple spectral bands, a processor for calculations and interpolations, and a polarizing filter.
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.
Adaptive two-pass median filter based on support vector machines for image restoration.
Lin, Tzu-Chao; Yu, Pao-Ta
2004-02-01
In this letter, a novel adaptive filter, the adaptive two-pass median (ATM) filter based on support vector machines (SVMs), is proposed to preserve more image details while effectively suppressing impulse noise for image restoration. The proposed filter is composed of a noise decision maker and two-pass median filters. Our new approach basically uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a corrupted pixel, the noise-free reduction median filter will be triggered to replace it. Otherwise, it remains unchanged. Then, to improve the quality of the restored image, a decision impulse filter is put to work in the second-pass filtering procedure. As for the noise suppressing both fixed-valued and random-valued impulses without degrading the quality of the fine details, the results of our extensive experiments demonstrate that the proposed filter outperforms earlier median-based filters in the literature. Our new filter also provides excellent robustness at various percentages of impulse noise.
Time-sequenced adaptive filtering using a modified P-vector algorithm
NASA Astrophysics Data System (ADS)
Williams, Robert L.
1996-10-01
An adaptive algorithm and two stage filter structure were developed for adaptive filtering of certain classes of signals that exhibit cyclostationary characteristics. The new modified P-vector algorithm (mPa) eliminates the need for a separate desired signal which is typically required by conventional adaptive algorithms. It is then implemented in a time-sequenced manner to counteract the nonstationary characteristics typically found in certain radar and bioelectromagnetic signals. Initial algorithm testing is performed on evoked responses generated by the visual cortex of the human brain with the objective, ultimately, to transition the results to radar signals. Each sample of the evoked response is modeled as the sum of three uncorrelated signal components, a time-varying mean (M), a noise component (N), and a random jitter component (Q). A two stage single channel time-sequenced adaptive filter structure was developed which improves convergence characteristics by de coupling the time-varying mean component from the `Q' and noise components in the first stage. The EEG statistics must be known a priori and are adaptively estimated from the pre stimulus data. The performance of the two stage mPa time-sequenced adaptive filter approaches the performance for the ideal case of an adaptive filter having a noiseless desired response.
Adaptive Low Dissipative High Order Filter Methods for Multiscale MHD Flows
NASA Technical Reports Server (NTRS)
Yee, H. C.; Sjoegreen, Bjoern
2004-01-01
Adaptive low-dissipative high order filter finite difference methods for long time wave propagation of shock/turbulence/combustion compressible viscous MHD flows has been constructed. Several variants of the filter approach that cater to different flow types are proposed. These filters provide a natural and efficient way for the minimization of the divergence of the magnetic field [divergence of B] numerical error in the sense that no standard divergence cleaning is required. For certain 2-D MHD test problems, divergence free preservation of the magnetic fields of these filter schemes has been achieved.
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.
Block-adaptive filtering and its application to seismic-event detection
Clark, G.A.
1981-04-01
Block digital filtering involves the calculation of a block or finite set of filter output samples from a block of input samples. The motivation for block processing arises from computational advantages of the technique. Block filters take good advantage of parallel processing architectures, which are becoming more and more attractive with the advent of very large scale integrated (VLSI) circuits. This thesis extends the block technique to Wiener and adaptive filters, both of which are statistical filters. The key ingredient to this extension turns out to be the definition of a new performance index, block mean square error (BMSE), which combines the well known sum square error (SSE) and mean square error (MSE). A block adaptive filtering procedure is derived in which the filter coefficients are adjusted once per each output block in accordance with a generalized block least mean-square (BLMS) algorithm. Convergence properties of the BLMS algorithm are studied, including conditions for guaranteed convergence, convergence speed, and convergence accuracy. Simulation examples are given for clarity. Convergence properties of the BLMS and LMS algorithms are analyzed and compared. They are shown to be analogous, and under the proper circumstances, equivalent. The block adaptive filter was applied to the problem of detecting small seismic events in microseismic background noise. The predictor outperformed the world-wide standardized seismograph network (WWSSN) seismometers in improving signal-to-noise ratio (SNR).
A model for radar images and its application to adaptive digital filtering of multiplicative noise
NASA Technical Reports Server (NTRS)
Frost, V. S.; Stiles, J. A.; Shanmugan, K. S.; Holtzman, J. C.
1982-01-01
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.
Waveform-Agile Tracking In Heavy Sea Clutter
2007-01-01
Waveform-Agile Tracking In Heavy Sea Clutter Sandeep P. Sira , Antonia Papandreou-Suppappola, Darryl Morrell†, and Douglas Cochran SenSIP Center...to a non-adaptive system. The paper is organized as follows. In Section II, we describe the models for the target dynamics, clutter, and observations ...dependence on zs[n]. C. Observations Model At the end of Sub-dwell 2 of the kth dwell, the measurement provided to the tracker is Yk = [yTn̂0−nv
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.
NASA Astrophysics Data System (ADS)
Malek, Obaidul; Venetsanopoulos, Anastasios; Anpalagan, Alagan
2010-08-01
Space Time Adaptive Processing (STAP) is a multi-dimensional adaptive signal processing technique, which processes the signal in spatial and Doppler domains for which a target detection hypothesis is to be formed. It is a sample based technique and based on the assumption of adequate number of Independent and Identically Distributed (i.i.d.) training data set in the surrounding environment. The principal challenge of the radar processing lies when it violates these underlying assumptions due to severe dynamic heterogeneous clutter (hot clutter) and jammer effects. This in turn degrades the Signal to Interference-plus-Noise Ratio (SINR), hence signal detection performance. Classical Wiener filtering theory is inadequate to deal with nonlinear and nonstationary interferences, however Wiener filtering approach is optimal for stationary and linear systems. But, these challenges can be overcome by Adaptive Sequential State Estimation (ASSE) filtering technique.
Jokinen, Emma; Yrttiaho, Santeri; Pulakka, Hannu; Vainio, Martti; Alku, Paavo
2012-12-01
Post-filtering can be utilized to improve the quality and intelligibility of telephone speech. Previous studies have shown that energy reallocation with a high-pass type filter works effectively in improving the intelligibility of speech in difficult noise conditions. The present study introduces a signal-to-noise ratio adaptive post-filtering method that utilizes energy reallocation to transfer energy from the first formant to higher frequencies. The proposed method adapts to the level of the background noise so that, in favorable noise conditions, the post-filter has a flat frequency response and the effect of the post-filtering is increased as the level of the ambient noise increases. The performance of the proposed method is compared with a similar post-filtering algorithm and unprocessed speech in subjective listening tests which evaluate both intelligibility and listener preference. The results indicate that both of the post-filtering methods maintain the quality of speech in negligible noise conditions and are able to provide intelligibility improvement over unprocessed speech in adverse noise conditions. Furthermore, the proposed post-filtering algorithm performs better than the other post-filtering method under evaluation in moderate to difficult noise conditions, where intelligibility improvement is mostly required.
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.
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.
Optimum Clutter Rejection for Infrared Surveillance Sensors.
1989-12-19
Adaptive spatial filtering is a robust approach to spatial discrimination that is based on the general theory of statistical hypothesis testing. In...motivated by the general theory of statistical hypothesis testing, which has been applied to signal detection problems by Kelly [3] and Reed et. al. [4-6...costs with those of an existing baseline filter bank. A robust adaptive filter design was first selected based on the theory of statistical hypothesis
The role of adaptive immunity as an ecological filter on the gut microbiota in zebrafish.
Stagaman, Keaton; Burns, Adam R; Guillemin, Karen; Bohannan, Brendan Jm
2017-03-17
All animals live in intimate association with communities of microbes, collectively referred to as their microbiota. Certain host traits can influence which microbial taxa comprise the microbiota. One potentially important trait in vertebrate animals is the adaptive immune system, which has been hypothesized to act as an ecological filter, promoting the presence of some microbial taxa over others. Here we surveyed the intestinal microbiota of 68 wild-type zebrafish, with functional adaptive immunity, and 61 rag1(-) zebrafish, lacking functional B- and T-cell receptors, to test the role of adaptive immunity as an ecological filter on the intestinal microbiota. In addition, we tested the robustness of adaptive immunity's filtering effects to host-host interaction by comparing the microbiota of fish populations segregated by genotype to those containing both genotypes. The presence of adaptive immunity individualized the gut microbiota and decreased the contributions of neutral processes to gut microbiota assembly. Although mixing genotypes led to increased phylogenetic diversity in each, there was no significant effect of adaptive immunity on gut microbiota composition in either housing condition. Interestingly, the most robust effect on microbiota composition was co-housing within a tank. In all, these results suggest that adaptive immunity has a role as an ecological filter of the zebrafish gut microbiota, but it can be overwhelmed by other factors, including transmission of microbes among hosts.The ISME Journal advance online publication, 17 March 2017; doi:10.1038/ismej.2017.28.
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.
Adaptive Kalman filtering methods for tracking GPS signals in high noise/high dynamic environments
NASA Astrophysics Data System (ADS)
Zuo, Qiyao; Yuan, Hong; Lin, Baojun
2007-11-01
GPS C/A signal tracking algorithms have been developed based on adaptive Kalman filtering theory. In the research, an adaptive Kalman filter is used to substitute for standard tracking loop filters. The goal is to improve estimation accuracy and tracking stabilization in high noise and high dynamic environments. The linear dynamics model and the measurements model are designed to estimate code phase, carrier phase, Doppler shift, and rate of change of Doppler shift. Two adaptive algorithms are applied to improve robustness and adaptive faculty of the tracking, one is Sage adaptive filtering approach and the other is strong tracking method. Both the new algorithms and the conventional tracking loop have been tested by using simulation data. In the simulation experiment, the highest jerk of the receiver is set to 10G m/s 3 with the lowest C/No 30dBHz. The results indicate that the Kalman filtering algorithms are more robust than the standard tracking loop, and performance of tracking loop using the algorithms is satisfactory in such extremely adverse circumstances.
Chi-squared smoothed adaptive particle-filtering based prognosis
NASA Astrophysics Data System (ADS)
Ley, Christopher P.; Orchard, Marcos E.
2017-01-01
This paper presents a novel form of selecting the likelihood function of the standard sequential importance sampling/re-sampling particle filter (SIR-PF) with a combination of sliding window smoothing and chi-square statistic weighting, so as to: (a) increase the rate of convergence of a flexible state model with artificial evolution for online parameter learning (b) improve the performance of a particle-filter based prognosis algorithm. This is applied and tested with real data from oil total base number (TBN) measurements from three haul trucks. The oil data has high measurement uncertainty and an unknown phenomenological state model. Performance of the proposed algorithm is benchmarked against the standard form of SIR-PF estimation which utilises the Normal (Gaussian) likelihood function. Both implementations utilise the same particle filter based prognosis algorithm so as to provide a common comparison. A sensitivity analysis is also performed to further explore the effects of the combination of sliding window smoothing and chi-square statistic weighting to the SIR-PF.
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
Riaz, Nadeem; Shanker, Piyush; Wiersma, Rodney; Gudmundsson, Olafur; Mao, Weihua; Widrow, Bernard; Xing, Lei
2009-10-07
Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.
Clutter Rejection for Mid Infrared Surveillance.
1983-01-01
34clutter- edge effect ," with account taken of non-random sampling, correlation, and non-stationarity of the noise. A principle is established for determining...parameter’s range is smaller unless the clutter- edge effect described in section IIB is extreme. If n is fixed by specifying the optical sensor and...15 . , B. Threshold Parameter, Critical Sample Size, and Clutter- edge Effect The sampling s.d.s of the uniform sky an,! clutter populations are
NASA Technical Reports Server (NTRS)
Kelly, D. A.; Fermelia, A.; Lee, G. K. F.
1990-01-01
An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter.
Gear Fault Signal Detection based on an Adaptive Fractional Fourier Transform Filter
NASA Astrophysics Data System (ADS)
Zhou, Xiaojun; Shao, Yimin; Zhen, Dong; Gu, Fengshou; Ball, Andrew
2011-07-01
Vibration-based fault diagnosis is widely used for gearbox monitoring. However, it often needs considerable effort to extract effective diagnostic feature signal from noisy vibration signals because of rich signal components contained in a complex gear transmission system. In this paper, an adaptive fractional Fourier transform filter is proposed to suppress noise in gear vibration signals and hence to highlight signal components originated from gear fault dynamic characteristics. The approach relies on the use of adaptive filters in the fractional Fourier transform domain with the optimised fractional transform order and the filter parameters, while the transform orders are selected when the signal have the highest energy gathering and the filter parameters are determined by evolutionary rules. The results from the simulation and experiments have verified the performance of the proposed algorithm in extracting the gear failure signal components from the noisy signals based on a multistage gearbox system.
Stuttering, Cluttering, and Phonological Complexity: Case Studies
ERIC Educational Resources Information Center
LaSalle, Lisa R.; Wolk, Lesley
2011-01-01
The phonological complexity of dysfluencies in those who clutter and/or stutter may help us better understand phonetic factors in these two types of fluency disorders. In this preliminary investigation, cases were three 14-year-old males, diagnosed as a Stutterer, a Clutterer, and a Stutterer-Clutterer. Spontaneous speech samples were transcribed,…
A study of infrared spectroscopy de-noising based on LMS adaptive filter
NASA Astrophysics Data System (ADS)
Mo, Jia-qing; Lv, Xiao-yi; Yu, Xiao
2015-12-01
Infrared spectroscopy has been widely used, but which often contains a lot of noise, so the spectral characteristic of the sample is seriously affected. Therefore the de-noising is very important in the spectrum analysis and processing. In the study of infrared spectroscopy, the least mean square (LMS) adaptive filter was applied in the field firstly. LMS adaptive filter algorithm can reserve the detail and envelope of the effective signal when the method was applied to infrared spectroscopy of breast cancer which signal-to-noise ratio (SNR) is lower than 10 dB, contrast and analysis the result with result of wavelet transform and ensemble empirical mode decomposition (EEMD). The three evaluation standards (SNR, root mean square error (RMSE) and the correlation coefficient (ρ)) fully proved de-noising advantages of LMS adaptive filter in infrared spectroscopy of breast cancer.
Seasonal signal capturing in time series of up coordinates by means of adaptive filters
NASA Astrophysics Data System (ADS)
Yalvac, S.; Ustun, A.
2013-12-01
Digital filters, is a system that performs mathematical operations on a sampled or discrete time signals. Adaptive filters designed for noise canceling are capable tools of decomposing correlated parts of data sets. This kind of filters which optimize itself using Least Mean Square (LMS) algorithm is a powerful tool for understand the truth hidden into the complex data sets like time series in Geosciences. The complex data sets such as CGPS (Continuously operating reference station) station's time series can be understood better with adaptive noise canceling by means of decompose coherent (seasonal effect, tectonic plate motion) and incoherent (noise; site-specific effects) parts of data. In this study, it is aimed to model the subsidence caused by groundwater withdrawal based on the seasonal correlation between consecutive years of CGPS time series. For this purpose, two stations where located into subsidence area of 3 year time series have analyzed with adaptive noise canceling filter. According to the results, the annual movement of these two stations have strong relationship. Also, subsidence behavior are correlated with annual rainfall data. BELD station one year filtered movement KAMN station one year filtered movements
An Adaptive Filter for the Removal of Drifting Sinusoidal Noise Without a Reference.
Kelly, John W; Siewiorek, Daniel P; Smailagic, Asim; Wang, Wei
2016-01-01
This paper presents a method for filtering sinusoidal noise with a variable bandwidth filter that is capable of tracking a sinusoid's drifting frequency. The method, which is based on the adaptive noise canceling (ANC) technique, will be referred to here as the adaptive sinusoid canceler (ASC). The ASC eliminates sinusoidal contamination by tracking its frequency and achieving a narrower bandwidth than typical notch filters. The detected frequency is used to digitally generate an internal reference instead of relying on an external one as ANC filters typically do. The filter's bandwidth adjusts to achieve faster and more accurate convergence. In this paper, the focus of the discussion and the data is physiological signals, specifically electrocorticographic (ECoG) neural data contaminated with power line noise, but the presented technique could be applicable to other recordings as well. On simulated data, the ASC was able to reliably track the noise's frequency, properly adjust its bandwidth, and outperform comparative methods including standard notch filters and an adaptive line enhancer. These results were reinforced by visual results obtained from real ECoG data. The ASC showed that it could be an effective method for increasing signal to noise ratio in the presence of drifting sinusoidal noise, which is of significant interest for biomedical applications.
Human tracking in thermal images using adaptive particle filters with online random forest learning
NASA Astrophysics Data System (ADS)
Ko, Byoung Chul; Kwak, Joon-Young; Nam, Jae-Yeal
2013-11-01
This paper presents a fast and robust human tracking method to use in a moving long-wave infrared thermal camera under poor illumination with the existence of shadows and cluttered backgrounds. To improve the human tracking performance while minimizing the computation time, this study proposes an online learning of classifiers based on particle filters and combination of a local intensity distribution (LID) with oriented center-symmetric local binary patterns (OCS-LBP). Specifically, we design a real-time random forest (RF), which is the ensemble of decision trees for confidence estimation, and confidences of the RF are converted into a likelihood function of the target state. First, the target model is selected by the user and particles are sampled. Then, RFs are generated using the positive and negative examples with LID and OCS-LBP features by online learning. The learned RF classifiers are used to detect the most likely target position in the subsequent frame in the next stage. Then, the RFs are learned again by means of fast retraining with the tracked object and background appearance in the new frame. The proposed algorithm is successfully applied to various thermal videos as tests and its tracking performance is better than those of other methods.
Automatic target classification of slow moving ground targets using space-time adaptive processing
NASA Astrophysics Data System (ADS)
Malas, John Alexander
2002-04-01
Air-to-ground surveillance radar technologies are increasingly being used by theater commanders to detect, track, and identify ground moving targets. New radar automatic target recognition (ATR) technologies are being developed to aid the pilot in assessing the ground combat picture. Most air-to-ground surveillance radars use Doppler filtering techniques to separate target returns from ground clutter. Unfortunately, Doppler filter techniques fall short on performance when target geometry and ground vehicle speed result in low line of sight velocities. New clutter filter techniques compatible with emerging advancements in wideband radar operation are needed to support surveillance modes of radar operation when targets enter this low velocity regime. In this context, space-time adaptive processing (STAP) in conjunction with other algorithms offers a class of signal processing that provide improved target detection, tracking, and classification in the presence of interference through the adaptive nulling of both ground clutter and/or jamming. Of particular interest is the ability of the radar to filter and process the complex target signature data needed to generate high range resolution (HRR) signature profiles on ground targets. A new approach is proposed which will allow air-to-ground target classification of slow moving vehicles in clutter. A wideband STAP approach for clutter suppression is developed which preserves the amplitude integrity of returns from multiple range bins consistent with the HRR ATR approach. The wideband STAP processor utilizes narrowband STAP principles to generate a series of adaptive sub-band filters. Each sub-band filter output is used to construct the complete filtered response of the ground target. The performance of this new approach is demonstrated and quantified through the implementation of a one dimensional template-based minimum mean squared error classifier. Successful minimum velocity identification is defined in terms of
The prediction of EEG signals using a feedback-structured adaptive rational function filter.
Kim, H S; Kim, T S; Choi, Y H; Park, S H
2000-08-01
In this article, we present a feedback-structured adaptive rational function filter based on a recursive modified Gram-Schmidt algorithm and apply it to the prediction of an EEG signal that has nonlinear and nonstationary characteristics. For the evaluation of the prediction performance, the proposed filter is compared with other methods, where a single-step prediction and a multi-step prediction are considered for a short-term prediction, and the prediction performance is assessed in normalized mean square error. The experimental results show that the proposed filter shows better performance than other methods considered for the short-term prediction of EEG signals.
NASA Astrophysics Data System (ADS)
Rahimi, Afshin; Kumar, Krishna Dev; Alighanbari, Hekmat
2017-05-01
Reaction wheels, as one of the most commonly used actuators in satellite attitude control systems, are prone to malfunction which could lead to catastrophic failures. Such malfunctions can be detected and addressed in time if proper analytical redundancy algorithms such as parameter estimation and control reconfiguration are employed. Major challenges in parameter estimation include speed and accuracy of the employed algorithm. This paper presents a new approach for improving parameter estimation with adaptive unscented Kalman filter. The enhancement in tracking speed of unscented Kalman filter is achieved by systematically adapting the covariance matrix to the faulty estimates using innovation and residual sequences combined with an adaptive fault annunciation scheme. The proposed approach provides the filter with the advantage of tracking sudden changes in the system non-measurable parameters accurately. Results showed successful detection of reaction wheel malfunctions without requiring a priori knowledge about system performance in the presence of abrupt, transient, intermittent, and incipient faults. Furthermore, the proposed approach resulted in superior filter performance with less mean squared errors for residuals compared to generic and adaptive unscented Kalman filters, and thus, it can be a promising method for the development of fail-safe satellites.
New cardiac MRI gating method using event-synchronous adaptive digital filter.
Park, Hodong; Park, Youngcheol; Cho, Sungpil; Jang, Bongryoel; Lee, Kyoungjoung
2009-11-01
When imaging the heart using MRI, an artefact-free electrocardiograph (ECG) signal is not only important for monitoring the patient's heart activity but also essential for cardiac gating to reduce noise in MR images induced by moving organs. The fundamental problem in conventional ECG is the distortion induced by electromagnetic interference. Here, we propose an adaptive algorithm for the suppression of MR gradient artefacts (MRGAs) in ECG leads of a cardiac MRI gating system. We have modeled MRGAs by assuming a source of strong pulses used for dephasing the MR signal. The modeled MRGAs are rectangular pulse-like signals. We used an event-synchronous adaptive digital filter whose reference signal is synchronous to the gradient peaks of MRI. The event detection processor for the event-synchronous adaptive digital filter was implemented using the phase space method-a sort of topology mapping method-and least-squares acceleration filter. For evaluating the efficiency of the proposed method, the filter was tested using simulation and actual data. The proposed method requires a simple experimental setup that does not require extra hardware connections to obtain the reference signals of adaptive digital filter. The proposed algorithm was more effective than the multichannel approach.
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.
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.
Steganalysis of content-adaptive JPEG steganography based on Gauss partial derivative filter bank
NASA Astrophysics Data System (ADS)
Zhang, Yi; Liu, Fenlin; Yang, Chunfang; Luo, Xiangyang; Song, Xiaofeng; Lu, Jicang
2017-01-01
A steganalysis feature extraction method based on Gauss partial derivative filter bank is proposed in this paper to improve the detection performance for content-adaptive JPEG steganography. Considering that the embedding changes of content-adaptive steganographic schemes are performed in the texture and edge regions, the proposed method generates filtered images comprising rich texture and edge information using Gauss partial derivative filter bank, and histograms of absolute values of filtered subimages are extracted as steganalysis features. Gauss partial derivative filter bank can represent texture and edge information in multiple orientations with less computation load than conventional methods and prevent redundancy in different filtered images. These two properties are beneficial in the extraction of low-complexity sensitive features. The results of experiments conducted on three selected modern JPEG steganographic schemes-uniform embedding distortion, JPEG universal wavelet relative distortion, and side-informed UNIWARD-indicate that the proposed feature set is superior to the prior art feature sets-discrete cosine transform residual, phase aware rich model, and Gabor filter residual.
Robust adaptive extended Kalman filtering for real time MR-thermometry guided HIFU interventions.
Roujol, Sébastien; de Senneville, Baudouin Denis; Hey, Silke; Moonen, Chrit; Ries, Mario
2012-03-01
Real time magnetic resonance (MR) thermometry is gaining clinical importance for monitoring and guiding high intensity focused ultrasound (HIFU) ablations of tumorous tissue. The temperature information can be employed to adjust the position and the power of the HIFU system in real time and to determine the therapy endpoint. The requirement to resolve both physiological motion of mobile organs and the rapid temperature variations induced by state-of-the-art high-power HIFU systems require fast MRI-acquisition schemes, which are generally hampered by low signal-to-noise ratios (SNRs). This directly limits the precision of real time MR-thermometry and thus in many cases the feasibility of sophisticated control algorithms. To overcome these limitations, temporal filtering of the temperature has been suggested in the past, which has generally an adverse impact on the accuracy and latency of the filtered data. Here, we propose a novel filter that aims to improve the precision of MR-thermometry while monitoring and adapting its impact on the accuracy. For this, an adaptive extended Kalman filter using a model describing the heat transfer for acoustic heating in biological tissues was employed together with an additional outlier rejection to address the problem of sparse artifacted temperature points. The filter was compared to an efficient matched FIR filter and outperformed the latter in all tested cases. The filter was first evaluated on simulated data and provided in the worst case (with an approximate configuration of the model) a substantial improvement of the accuracy by a factor 3 and 15 during heat up and cool down periods, respectively. The robustness of the filter was then evaluated during HIFU experiments on a phantom and in vivo in porcine kidney. The presence of strong temperature artifacts did not affect the thermal dose measurement using our filter whereas a high measurement variation of 70% was observed with the FIR 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.
Yang, Feng; Wang, Yongqi; Chen, Hao; Zhang, Pengyan; Liang, Yan
2016-10-11
In this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptively partitioned into two parts, persistent and birth measurement sets, for updating the persistent and birth target Probability Hypothesis Density, respectively. Furthermore, the collaboration mechanism of multiple probability hypothesis density (PHDs) is established, where tracks can be automatically extracted. Simulation results reveal that the proposed filter yields considerable computational savings in processing requirements and significant improvement in tracking accuracy.
Performance Analysis of Adaptive Volterra Filters in the Finite-Alphabet Input Case
NASA Astrophysics Data System (ADS)
Besbes, Hichem; Jaïdane, Mériem; Ezzine, Jelel
2004-12-01
This paper deals with the analysis of adaptive Volterra filters, driven by the LMS algorithm, in the finite-alphabet inputs case. A tailored approach for the input context is presented and used to analyze the behavior of this nonlinear adaptive filter. Complete and rigorous mean square analysis is provided without any constraining independence assumption. Exact transient and steady-state performances expressed in terms of critical step size, rate of transient decrease, optimal step size, excess mean square error in stationary mode, and tracking nonstationarities are deduced.
Gharieb, R R; Cichocki, A
2001-03-01
An adaptive filtering approach for the segmentation and tracking of electro-encephalogram (EEG) signal waves is described. In this approach, an adaptive recursive bandpass filter is employed for estimating and tracking the centre frequency associated with each EEG wave. The main advantage inherent in the approach is that the employed adaptive filter has only one unknown coefficient to be updated. This coefficient, having an absolute value less than 1, represents an efficient distinct feature for each EEG specific wave, and its time function reflects the non-stationarity behaviour of the EEG signal. Therefore the proposed approach is simple and accurate in comparison with existing multivariate adaptive approaches. The approach is examined using extensive computer simulations. It is applied to computer-generated EEG signals composed of different waves. The adaptive filter coefficient (i.e. the segmentation parameter) is -0.492 for the delta wave, -0.360 for the theta wave, -0.191 for the alpha wave, -0.027 for the sigma wave, 0.138 for the beta wave and 0.605 for the gamma wave. This implies that the segmentation parameter increases with the increase in the centre frequency of the EEG waves, which provides fast on-line information about the behaviour of the EEG signal. The approach is also applied to real-world EEG data for the detection of sleep spindles.
Cannistraci, Carlo Vittorio; Abbas, Ahmed; Gao, Xin
2015-01-26
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.
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
1996-12-01
algorithms for obtaining rapid convergence of the tap weights of a transversal filter to their optimum settings ( Godard , 1974). This algorithm is...1366, Dec. 1989. 10. Godard , D. N. (1974) "Channel equalization using a Kalman filter for fast data transmission," IBM K. Res. Dev., vol. 18, pp. 267
A New Synchronized Miniature Rubidium Oscillator with an Auto-Adaptive Disciplining Filter
2001-11-01
33rd Annual Precise Time and Time Interval (PTTI) Meeting A NEW SYNCHRONIZED MINIATURE RUBIDIUM DISCIPLINING FILTER OSCILLATOR WITH AN AUTO...ADAPTIVE Pascal Rochat and Bernard Leuenberger Temex Neuchfitel Time SA, Switzerland Abstract A new rubidium line (SRO) integrating timing functions and... time interval measurements was developed using an auto-adaptive disciplining algorithm. This led to an ultra-stable time & frequency machine usable
Pritamdas, K; Singh, Kh Manglem; Singh, L Lolitkumar
2016-01-01
A new adaptive switching algorithm is presented where two adaptive filters are switched correspondingly for lower and higher noise ratio of the image. An adaptive center weighted vector median filter is used for the lower noise ratio whereas for higher noise ratio the noisy pixels are detected based on the comparison of the difference between the mean of the vector pixels in the window and the approximated variance of the vector pixels in the window. Then the window comprising the detected noisy pixel is further considered where the pixels are given exponential weights according to their similarity to the other neighboring pixels, spatially and radio metrically. The noisy pixels are then replaced by the weighted average of the pixels within the window. The filter is able to preserve higher signal content in the higher noise ratio as compared to other robust filters in comparison. With a little high in computational complexity, this technique performs well both in lower and higher noise ratios. Simulation results on various RGB images show that the proposed algorithm outperforms many other existing nonlinear filters in terms of preservation of edges and fine details.
Zurbenko, I.; Chen, J.; Rao, S.T.
1997-11-01
The issue of global climate change due to increased anthropogenic emissions of greenhouse gases in the atmosphere has gained considerable attention and importance. Climate change studies require the interpretation of weather data collected in numerous locations and/or over the span of several decades. Unfortunately, these data contain biases caused by changes in instruments and data acquisition procedures. It is essential that biases are identified and/or removed before these data can be used confidently in the context of climate change research. The purpose of this paper is to illustrate the use of an adaptive moving average filter and compare it with traditional parametric methods. The advantage of the adaptive filter over traditional parametric methods is that it is less effected by seasonal patterns and trends. The filter has been applied to upper air relative humidity and temperature data. Applied to generated data, the filter has a root mean squared error accuracy of about 600 days when locating changes of 0.1 standard deviations and about 20 days for changes of 0.5 standard deviations. In some circumstances, the accuracy of location estimation can be improved through parametric techniques used in conjunction with the adaptive filter.
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.
Thakur, A; Anand, R S
2007-01-01
This article discusses an adaptive filtering technique for reducing speckle using second order statistics of the speckle pattern in ultrasound medical images. Several region-based adaptive filter techniques have been developed for speckle noise suppression, but there are no specific criteria for selecting the region growing size in the post processing of the filter. The size appropriate for one local region may not be appropriate for other regions. Selection of the correct region size involves a trade-off between speckle reduction and edge preservation. Generally, a large region size is used to smooth speckle and a small size to preserve the edges into an image. In this paper, a smoothing procedure combines the first order statistics of speckle for the homogeneity test and second order statistics for selection of filters and desired region growth. Grey level co-occurrence matrix (GLCM) is calculated for every region during the region contraction and region growing for second order statistics. Further, these GLCM features determine the appropriate filter for the region smoothing. The performance of this approach is compared with the aggressive region-growing filter (ARGF) using edge preservation and speckle reduction tests. The processed image results show that the proposed method effectively reduces speckle noise and preserves edge details.
Development of an adaptive bilateral filter for evaluating color image difference
NASA Astrophysics Data System (ADS)
Wang, Zhaohui; Hardeberg, Jon Yngve
2012-04-01
Spatial filtering, which aims to mimic the contrast sensitivity function (CSF) of the human visual system (HVS), has previously been combined with color difference formulae for measuring color image reproduction errors. These spatial filters attenuate imperceptible information in images, unfortunately including high frequency edges, which are believed to be crucial in the process of scene analysis by the HVS. The adaptive bilateral filter represents a novel approach, which avoids the undesirable loss of edge information introduced by CSF-based filtering. The bilateral filter employs two Gaussian smoothing filters in different domains, i.e., spatial domain and intensity domain. We propose a method to decide the parameters, which are designed to be adaptive to the corresponding viewing conditions, and the quantity and homogeneity of information contained in an image. Experiments and discussions are given to support the proposal. A series of perceptual experiments were conducted to evaluate the performance of our approach. The experimental sample images were reproduced with variations in six image attributes: lightness, chroma, hue, compression, noise, and sharpness/blurriness. The Pearson's correlation values between the model-predicted image difference and the observed difference were employed to evaluate the performance, and compare it with that of spatial CIELAB and image appearance model.
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.
An optimized locally adaptive non-local means denoising filter for cryo-electron microscopy data.
Wei, Dai-Yu; Yin, Chang-Cheng
2010-12-01
Cryo-electron microscopy (cryo-EM) now plays an important role in structural analysis of macromolecular complexes, organelles and cells. However, the cryo-EM images obtained close to focus and under low dose conditions have a very high level of noise and a very low contrast, which hinders high-resolution structural analysis. Here, an optimized locally adaptive non-local (LANL) means filter, which can preserve signal details and simultaneously significantly suppress noise for cryo-EM data, is presented. This filter takes advantage of a wide range of pixels to estimate the denoised pixel values instead of the traditional filter that only uses pixels in the local neighborhood. The filter performed well on simulated data and showed promising results on raw cryo-EM images and tomograms. The predominant advantage of this optimized LANL-means filter is the structural signal and the background are clearly distinguishable. This locally adaptive non-local means filter may become a useful tool in the analysis of cryo-EM data, such as automatic particle picking, extracting structural features and segmentation of tomograms.
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
Maier, Andreas; Wigström, Lars; Hofmann, Hannes G.; Hornegger, Joachim; Zhu, Lei; Strobel, Norbert; Fahrig, Rebecca
2011-01-01
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
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.
Adaptive Filter Techniques for Optical Beam Jitter Control and Target Tracking
2008-12-01
Analysis ......................................................51 5. Standard Deviation of Beam Position Error ...................................51 6...Organization of Analysis ...................................................................51 B. FEEDFORWARD ADAPTIVE FILTERS USING MULTIPLE...actuator (loud speaker or CFSM) before its effect reaches the error sensor. In ANC lingo , y(t) must first pass through the secondary plant dynamics of the
Design of adaptive filter amplifier in UV communication based on DSP
NASA Astrophysics Data System (ADS)
Lv, Zhaoshun; Wu, Hanping; Li, Junyu
2016-10-01
According to the problem of the weak signal at receiving end in UV communication, we design a high gain, continuously adjustable adaptive filter amplifier. Based on proposing overall technical indicators and analyzing its working principle of the signal amplifier, we use chip LMH6629MF and two chips of AD797BN to achieve three-level cascade amplification. And apply hardware of DSP TMS320VC5509A to implement digital filtering. Design and verification by Multisim, Protel 99SE and CCS, the results show that: the amplifier can realize continuously adjustable amplification from 1000 to 10000 times without distortion. Magnification error is <=%4@1000 10000. And equivalent input noise voltage of amplification circuit is <=6 nV/ √Hz @30KHz 45KHz, and realizing function of adaptive filtering. The design provides theoretical reference and technical support for the UV weak signal processing.
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.
Impact of Rician adapted Non-Local Means filtering on HARDI.
Descoteaux, Maxime; Wiest-Daesslé, Nicolas; Prima, Sylvain; Barillot, Christian; Deriche, Rachid
2008-01-01
In this paper we study the impact of denoising the raw high angular resolution diffusion imaging (HARDI) data with the Non-Local Means filter adapted to Rician noise (NLMr). We first show that NLMr filtering improves robustness of apparent diffusion coefficient (ADC) and orientation distribution function (ODF) reconstructions from synthetic HARDI datasets. Our results suggest that the NLMr filtering improve the quality of anisotropy maps computed from ADC and ODF and improve the coherence of q-ball ODFs with the underlying anatomy while not degrading angular resolution. These results are shown on a biological phantom with known ground truth and on a real human brain dataset. Most importantly, we show that multiple measurements of diffusion-weighted (DW) images and averaging these images along each direction can be avoided because NLMr filtering of the individual DW images produces better quality generalized fractional anisotropy maps and more accurate ODF fields than when computed from the averaged DW datasets.
Sannelli, Claudia; Vidaurre, Carmen; Muller, Klaus-Robert; Blankertz, Benjamin
2010-01-01
Laplacian filters are commonly used in Brain Computer Interfacing (BCI). When only data from few channels are available, or when, like at the beginning of an experiment, no previous data from the same user is available complex features cannot be used. In this case band power features calculated from Laplacian filtered channels represents an easy, robust and general feature to control a BCI, since its calculation does not involve any class information. For the same reason, the performance obtained with Laplacian features is poor in comparison to subject-specific optimized spatial filters, such as Common Spatial Patterns (CSP) analysis, which, on the other hand, can be used just in a later phase of the experiment, since they require a considerable amount of training data in order to enroll a stable and good performance. This drawback is particularly evident in case of poor performing BCI users, whose data is highly non-stationary and contains little class relevant information. Therefore, Laplacian filtering is preferred to CSP, e.g., in the initial period of co-adaptive calibration, a novel BCI paradigm designed to alleviate the problem of BCI illiteracy. In fact, in the co-adaptive calibration design the experiment starts with a subject-independent classifier and simple features are needed in order to obtain a fast adaptation of the classifier to the newly acquired user's data. Here, the use of an ensemble of local CSP patches (CSPP) is proposed, which can be considered as a compromise between Laplacians and CSP: CSPP needs less data and channels than CSP, while being superior to Laplacian filtering. This property is shown to be particularly useful for the co-adaptive calibration design and is demonstrated on off-line data from a previous co-adaptive BCI study.
Advanced methods and means to improve atmospheric lidar stability against sky background clutter
NASA Astrophysics Data System (ADS)
Agishev, Ravil R.
2011-11-01
An impact of intensive background clutter on lidar photodetectors leads to changes of their sensitivity and can even overload them. As a result, information on atmospheric optical parameters is distorted and sometimes can be completely lost. Since a problem of lidar system structure and parameters adaptation to background radiation remains actual one, some advanced methods and means to improve atmospheric lidar stability against sky background clutter are discussed.
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.
FASART: An iterative reconstruction algorithm with inter-iteration adaptive NAD filter.
Zhou, Ziying; Li, Yugang; Zhang, Fa; Wan, Xiaohua
2015-01-01
Electron tomography (ET) is an essential imaging technique for studying structures of large biological specimens. These structures are reconstructed from a set of projections obtained at different sample orientations by tilting the specimen. However, most of existing reconstruction methods are not appropriate when the data are extremely noisy and incomplete. A new iterative method has been proposed: adaptive simultaneous algebraic reconstruction with inter-iteration adaptive non-linear anisotropic diffusion (NAD) filter (FASART). We also adopted an adaptive parameter and discussed the step for the filter in this reconstruction method. Experimental results show that FASART can restrain the noise generated in the process of iterative reconstruction and still preserve the more details of the structure edges.
Neural Network Aided Adaptive Extended Kalman Filtering Approach for DGPS Positioning
NASA Astrophysics Data System (ADS)
Jwo, Dah-Jing; Huang, Hung-Chih
2004-09-01
The extended Kalman filter, when employed in the GPS receiver as the navigation state estimator, provides optimal solutions if the noise statistics for the measurement and system are completely known. In practice, the noise varies with time, which results in performance degradation. The covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. The technique attempts to make the actual filter residuals consistent with their theoretical covariance. However, this innovation-based adaptive estimation shows very noisy results if the window size is small. To resolve the problem, a multilayered neural network is trained to identify the measurement noise covariance matrix, in which the back-propagation algorithm is employed to iteratively adjust the link weights using the steepest descent technique. Numerical simulations show that based on the proposed approach the adaptation performance is substantially enhanced and the positioning accuracy is substantially improved.
Spors, Sascha; Buchner, Herbert; Rabenstein, Rudolf; Herbordt, Wolfgang
2007-07-01
The acoustic theory for multichannel sound reproduction systems usually assumes free-field conditions for the listening environment. However, their performance in real-world listening environments may be impaired by reflections at the walls. This impairment can be reduced by suitable compensation measures. For systems with many channels, active compensation is an option, since the compensating waves can be created by the reproduction loudspeakers. Due to the time-varying nature of room acoustics, the compensation signals have to be determined by an adaptive system. The problems associated with the successful operation of multichannel adaptive systems are addressed in this contribution. First, a method for decoupling the adaptation problem is introduced. It is based on a generalized singular value decomposition and is called eigenspace adaptive filtering. Unfortunately, it cannot be implemented in its pure form, since the continuous adaptation of the generalized singular value decomposition matrices to the variable room acoustics is numerically very demanding. However, a combination of this mathematical technique with the physical description of wave propagation yields a realizable multichannel adaptation method with good decoupling properties. It is called wave domain adaptive filtering and is discussed here in the context of wave field synthesis.
Bayesian Contrast Measures and Clutter Distribution Determinants of Human Target Detection.
Novak, Ana; Armstrong, Nicholas; Caelli, Terry; Blair, Iain
2017-03-01
Human target detection is known to be dependent on a number of components: one, basic electro-optics including image contrast, the target size, pixel resolution, and contrast sensitivity; two, target shape, image type and features, types of clutter; and three, context and task requirements. Here, we consider a Bayesian approach to investigating how these components contribute to target detection. To this end, we develop and compare three different formulations for contrast: mean contrast, perceptual contrast, and a Bayesian-based histogram contrast statistic. Results on past detection data show how the latter contrast measure correlates well with human performance factoring out all other dimensions. As for clutter, our findings show that with large targets, there are effectively no clutter effects. Furthermore, clutter does not have a major effect on detection when it is not contiguous with the target even when it is smaller. However, except for large targets, when the target is contiguous with the clutter, detection clearly decreases as a function of the similarity of target and clutter features-creating type of "clutter camouflage". This Bayesian formulation uses priors based on the contrast histogram statistics derived from all the images, the image context, and implies that human observers have adapted their criteria to fit with the image set, context, and task.
2014-01-01
Background The calculation of arterial oxygen saturation (SpO2) relies heavily on the amplitude information of the high-quality photoplethysmographic (PPG) signals, which could be contaminated by motion artifacts (MA) during monitoring. Methods A new method combining temporally constrained independent component analysis (cICA) and adaptive filters is presented here to extract the clean PPG signals from the MA corrupted PPG signals with the amplitude information reserved. The underlying PPG signal could be extracted from the MA contaminated PPG signals automatically by using cICA algorithm. Then the amplitude information of the PPG signals could be recovered by using adaptive filters. Results Compared with conventional ICA algorithms, the proposed approach is permutation and scale ambiguity-free. Numerical examples with both synthetic datasets and real-world MA corrupted PPG signals demonstrate that the proposed method could remove the MA from MA contaminated PPG signals more effectively than the two existing FFT-LMS and moving average filter (MAF) methods. Conclusions This paper presents a new method which combines the cICA algorithm and adaptive filter to extract the underlying PPG signals from the MA contaminated PPG signals with the amplitude information reserved. The new method could be used in the situations where one wants to extract the interested source automatically from the mixed observed signals with the amplitude information reserved. The results of study demonstrated the efficacy of this proposed method. PMID:24761769
Tsui, Po-Hsiang; Wan, Yung-Liang; Huang, Chih-Chung; Wang, Ming-Chen
2010-10-01
The Nakagami parameter is associated with the Nakagami distribution estimated from ultrasonic backscattered signals and closely reflects the scatterer concentrations in tissues. There is an interest in exploring the possibility of enhancing the ability of the Nakagami parameter to characterize tissues. In this paper, we explore the effect of adaptive thresholdfiltering based on the noise-assisted empirical mode decomposition of the ultrasonic backscattered signals on the Nakagami parameter as a function of scatterer concentration for improving the Nakagami parameter performance. We carried out phantom experiments using 5 MHz focused and nonfocused transducers. Before filtering, the dynamic ranges of the Nakagami parameter, estimated using focused and nonfocused transducers between the scatterer concentrations of 2 and 32 scatterers/mm3, were 0.44 and 0.1, respectively. After filtering, the dynamic ranges of the Nakagami parameter, using the focused and nonfocused transducers, were 0.71 and 0.79, respectively. The experimental results showed that the adaptive threshold filter makes the Nakagami parameter measured by a focused transducer more sensitive to the variation in the scatterer concentration. The proposed method also endows the Nakagami parameter measured by a nonfocused transducer with the ability to differentiate various scatterer concentrations. However, the Nakagami parameters estimated by focused and nonfocused transducers after adaptive threshold filtering have different physical meanings: the former represents the statistics of signals backscattered from unresolvable scatterers while the latter is associated with stronger resolvable scatterers or local inhomogeneity due to scatterer aggregation.
Reducing the effect of respiration in baroreflex sensitivity estimation with adaptive filtering.
Tiinanen, Suvi; Tulppo, Mikko; Seppänen, Tapio
2008-01-01
Cardiac baroreflex is described by baroreflex sensitivity (BRS) from blood pressure and heart rate interval (RRi) fluctuations. However, respiration affects both blood pressure and RRi via mechanisms that are not necessarily of baroreflex origin. To separate the effects of baroreflex and respiration, metronome-guided breathing in a high frequency band (HF, 0.25-0.4 Hz) and a low frequency spectral band (LF, 0.04-0.15 Hz) have therefore been commonly used for BRS estimation. The controlled breathing may, however, change the natural functioning of the autonomic system and interfere BRS estimates. To enable usage of spontaneous breathing, we propose an adaptive LMS-based filter for removing the respiration effect from the BRS estimates. ECG, continuous blood pressure and respiration were measured during 5 min spontaneous and 5 min controlled breathing at 0.25 Hz in healthy males (n = 24, 33+/-7 years). BRS was calculated with spectral methods from the LF band with and without filtering. In those subjects whose spontaneous breathing rate was <0.15 Hz, the BRS(LF) values were overestimated, whereas the adaptive filtering reduced the bias significantly. As a conclusion, the adaptive filter reduces the distorting effect of respiration on BRS values, which enables more accurate estimation of BRS and the usage of spontaneous breathing as a measurement protocol.
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 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.
Optimization of an adaptive nonlinear filter for the analysis of nystagmus.
Engelken, E J; Stevens, K W; Enderle, J D
1991-01-01
An adaptive nonlinear digital filter has been designed for the analysis of an eye-movement signal called nystagmus. Nystagmus is a bi-phasic signal consisting of a sequence of tracking eye movements called "slow-phase" interspersed with brief, high-velocity refixation movements called "fast-phase." The objective of the analysis is to separate the nystagmus signal into its fast- and slow-phase components. Specifically, the goal is to produce an evenly sampled estimate of slow-phase velocity (SPV) and an estimate of the peak fast-phase velocity. Classically this has been done using pattern recognition methods that exploit the fact that the fast-phase is a relatively short duration, high-velocity movement compared to the slow-phase. Unfortunately, these velocity and duration differences do not reliably separate the slow- and fast-phases under all conditions, especially when the signal is noisy. We have designed and built an adaptive nonlinear digital filter that easily outperforms the more complex pattern recognition algorithms. This new filter, called an Adaptive Asymmetrically Trimmed-Mean (AATM) filter, works under the assumption that, on the average, the eyes spend more time in slow-phase than in fast-phase. Thus, in any given data segment, most of the data samples are slow-phase samples. By analyzing the amplitude distribution of the data samples in the segment we can determine which of these samples are slow-phase. We used computer generated nystagmus signals contaminated with 3 levels of noise to evaluate the filter. The filter parameters were then optimized using Monte Carlo procedures producing an extremely robust analysis method.
Envelope analysis with a genetic algorithm-based adaptive filter bank for bearing fault detection.
Kang, Myeongsu; Kim, Jaeyoung; Choi, Byeong-Keun; Kim, Jong-Myon
2015-07-01
This paper proposes a fault detection methodology for bearings using envelope analysis with a genetic algorithm (GA)-based adaptive filter bank. Although a bandpass filter cooperates with envelope analysis for early identification of bearing defects, no general consensus has been reached as to which passband is optimal. This study explores the impact of various passbands specified by the GA in terms of a residual frequency components-to-defect frequency components ratio, which evaluates the degree of defectiveness in bearings and finally outputs an optimal passband for reliable bearing fault detection.
The application of dummy noise adaptive Kalman filter in underwater navigation
NASA Astrophysics Data System (ADS)
Li, Song; Zhang, Chun-Hua; Luan, Jingde
2011-10-01
The track of underwater target is easy to be affected by the various by the various factors, which will cause poor performance in Kalman filter with the error in the state and measure model. In order to solve the situation, a method is provided with dummy noise compensative technology. Dummy noise is added to state and measure model artificially, and then the question can be solved by the adaptive Kalman filter with unknown time-changed statistical character. The simulation result of underwater navigation proves the algorithm is effective.
Modified Log-LMS adaptive filter with low signal distortion for biomedical applications.
Jiao, Yuzhong; Cheung, Rex Y P; Mok, Mark P C
2012-01-01
Life signals from human body, e.g. heartbeat or electrocardiography (ECG), are usually weak and susceptible to external noise and interference. Adaptive filter is a good tool to reduce the influence of ambient noise/interference on the life signals. Least mean squares (LMS) algorithm, as one of most popular adaptive algorithms for active noise cancellation (ANC) by adaptive filtering, has the advantage of easy implementation. In order to further decrease the complexity of LMS algorithm based adaptive filter, a Log-LMS algorithm was proposed, which quantized signals by the function of log2. The algorithm can replace multipliers by simple shifting. However, both LMS algorithm and Log-LMS algorithm have the disadvantage of serious signal distortion in biomedical applications. In this paper, a modified Log-LMS algorithm is presented, which divides the convergence process into two different stages, and utilizes different quantization method in each stage. Two scenarios of biomedical applications are used for analysis, 1) using stethoscope in emergence medical helicopter and 2) measuring ECG under power line interference. The simulated results show that the modified algorithm can achieve fast convergence and low signal distortion in processing periodic life signals.
Adaptive filtering and feed-forward control for suppression of vibration and jitter
NASA Astrophysics Data System (ADS)
Anderson, Eric H.; Blankinship, Ross L.; Fowler, Leslie P.; Glaese, Roger M.; Janzen, Paul C.
2007-04-01
This paper describes the use of adaptive filtering to control vibration and optical jitter. Adaptive filtering is a class of signal processing techniques developed over the last several decades and applied since to applications ranging from communications to image processing. Basic concepts in adaptive filtering and feedforward control are reviewed. A series of examples in vibration, motion and jitter control, including cryocoolers, ground-based active optics systems, flight motion simulators, wind turbines and airborne optical beam control systems, illustrates the effectiveness of the adaptive methods. These applications make use of information and signals that originate from system disturbances and minimize the correlations between disturbance information and error and performance measures. The examples incorporate a variety of disturbance types including periodic, multi-tonal, broadband stationary and non-stationary. Control effectiveness with slowly-varying narrowband disturbances originating from cryocoolers can be extraordinary, reaching 60 dB of reduction or rejection. In other cases, performance improvements are only 30-50%, but such reductions effectively complement feedback servo performance in many applications.
Stasiunas, Antanas; Verikas, Antanas; Bacauskiene, Marija; Miliauskas, Rimvydas
2012-03-01
Outer hair cells in the cochlea of the ear, together with the local structures of the basilar membrane, reticular lamina and tectorial membrane constitute the adaptive primary filters (PF) of the second order. We used them for designing a serial-parallel signal filtering system. We determined a rational number of the PF included in Gaussian channels of the system, summation weights of the output signals, and distribution of the PF along the basilar membrane. A Gaussian panoramic filter bank each channel of which consists of five PF is presented as an example. The properties of the PF, the channel and the filter bank operating in the linear and nonlinear modes are determined during adaptation and under efferent control. The results suggest that application of biological filtering principles can be useful for designing cochlear implants with new speech encoding strategies.
Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI
NASA Astrophysics Data System (ADS)
Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R.
2017-04-01
Objective. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. Approach. To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. Main results. The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (<35 Hz) frequency ranges. MBRLAF is computationally more demanding than RLAF, but highly effective in all EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. Significance. In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We
Yu, Alfred C H; Cobbold, Richard S C
2008-03-01
Because of their adaptability to the slow-time signal contents, eigen-based filters have shown potential in improving the flow detection performance of color flow images. This paper proposes a new eigen-based filter called the Hankel-SVD filter that is intended to process each slowtime ensemble individually. The new filter is derived using the notion of principal Hankel component analysis, and it achieves clutter suppression by retaining only the principal components whose order is greater than the clutter eigen-space dimension estimated from a frequency based analysis algorithm. To assess its efficacy, the Hankel-SVD filter was first applied to synthetic slow-time data (ensemble size: 10) simulated from two different sets of flow parameters that model: 1) arterial imaging (blood velocity: 0 to 38.5 cm/s, tissue motion: up to 2 mm/s, transmit frequency: 5 MHz, pulse repetition period: 0.4 ms) and 2) deep vessel imaging (blood velocity: 0 to 19.2 cm/s, tissue motion: up to 2 cm/s, transmit frequency: 2 MHz, pulse repetition period: 2.0 ms). In the simulation analysis, the post-filter clutter-to- blood signal ratio (CBR) was computed as a function of blood velocity. Results show that for the same effective stopband size (50 Hz), the Hankel-SVD filter has a narrower transition region in the post-filter CBR curve than that of another type of adaptive filter called the clutter-downmixing filter. The practical efficacy of the proposed filter was tested by application to in vivo color flow data obtained from the human carotid arteries (transmit frequency: 4 MHz, pulse repetition period: 0.333 ms, ensemble size: 10). The resulting power images show that the Hankel-SVD filter can better distinguish between blood and moving-tissue regions (about 9 dB separation in power) than the clutter-downmixing filter and a fixed-rank multi ensemble-based eigen-filter (which showed a 2 to 3 dB separation).
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.
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.
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
Adapting a truly nonlinear filter to the ocean acoustic inverse problem
NASA Astrophysics Data System (ADS)
Ganse, Andrew A.; Odom, Robert I.
2005-04-01
Nonlinear inverse problems including the ocean acoustic problem have been solved by Monte Carlo, locally-linear, and filter based techniques such as the Extended Kalman Filter (EKF). While these techniques do provide statistical information about the solution (e.g., mean and variance), each suffers from inherent limitations in their approach to nonlinear problems. Monte Carlo techniques are expensive to compute and do not contribute to intuitive interpretation of a problem, and locally-linear techniques (including the EKF) are limited by the multimodal objective landscape of nonlinear problems. A truly nonlinear filter, based on recent work in nonlinear tracking, estimates state information for a nonlinear problem in continual measurement updates and is adapted to solving nonlinear inverse problems. Additional terms derived from the system's state PDF are added to the mean and covariance of the solution to address the nonlinearities of the problem, and overall the technique offers improved performance in nonlinear inversion. [Work supported by ONR.
NASA Technical Reports Server (NTRS)
Smith, J. W.; Edwards, J. W.
1980-01-01
Analysis of a longitudinal pilot-induced oscillation (PIO) experienced just prior to touchdown on the final flight of the space shuttle's approach landing tests indicated that the source of the problem was a combination of poor basic handling qualities aggravated by time delays through the digital flight control computer and rate limiting of the elevator actuators due to high pilot gain. A nonlinear PIO suppression (PIOS) filter was designed and developed to alleviate the vehicle's PIO tendencies by reducing the gain in the command path. From analytical and simulator studies it was shown that the PIOS filter, in an adaptive fashion, can attenuate the command path gain without adding phase lag to the system. With the pitch attitude loop of a simulated shuttle model closed, the PIOS filter increased the gain margin by a factor of about two.
Adaptive filtering for reduction of speckle in ultrasonic pulse-echo images.
Bamber, J C; Daft, C
1986-01-01
Current medical ultrasonic scanning instrumentation permits the display of fine image detail (speckle) which does not transfer useful information but degrades the apparent low contrast resolution in the image. An adaptive two-dimensional filter has been developed which uses local features of image texture to recognize and maximally low-pass filter those parts of the image which correspond to fully developed speckle, while substantially preserving information associated with resolved-object structure. A first implementation of the filter is described which uses the ratio of the local variance and the local mean as the speckle recognition feature. Preliminary results of applying this form of display processing to medical ultrasound images are very encouraging; it appears that the visual perception of features such as small discrete structures, subtle fluctuations in mean echo level and changes in image texture may be enhanced relative to that for unprocessed images.
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
Lepine, Nicholas N; Tajima, Takuro; Ogasawara, Takayuki; Kasahara, Ryoichi; Koizumi, Hiroshi; Lepine, Nicholas N; Tajima, Takuro; Ogasawara, Takayuki; Kasahara, Ryoichi; Koizumi, Hiroshi; Koizumi, Hiroshi; Ogasawara, Takayuki; Tajima, Takuro; Kasahara, Ryoichi; Lepine, Nicholas N
2016-08-01
An adaptive Kalman filter-based fusion algorithm capable of estimating respiration rate for unobtrusive respiratory monitoring is proposed. Using both signal characteristics and a priori information, the Kalman filter is adaptively optimized to improve accuracy. Furthermore, the system is able to combine the respiration-related signals extracted from a textile ECG sensor and an accelerometer to create a single robust measurement. We measured derived respiratory rates and, when compared to a reference, found root-mean-square error of 2.11 breaths-per-minute (BrPM) while lying down, 2.30 BrPM while sitting, 5.97 BrPM while walking, and 5.98 BrPM while running. These results demonstrate that the proposed system is applicable to unobtrusive monitoring for various applications.
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.
Adaptive control of a flexible beam using least square lattice filters
NASA Technical Reports Server (NTRS)
Sundararajan, N.; Montgomery, R. C.
1983-01-01
This paper presents an indirect adaptive control scheme for the control of flexible structures using recursive least square lattice filters. The identification scheme uses lattice filters which provide an on-line estimate of the number of modes, mode shapes and modal amplitudes. These modes are coupled and a transformation to decouple them in order to obtain the natural modes is presented. The decoupled modal amplitude time series are then used in an equation error identification scheme to identify the model parameters in an autoregressive moving average (ARMA) form. The control is based on modal pole placement scheme with the objective of vibration suppression. The control gains are calculated based on the identified ARMA parameters. Before using the identified parameters for control, detailed testing and validation procedures are carried out on the identified parameters. The full adaptive control scheme is demonstrated using the simulation for the 12 foot free-free beam apparatus at NASA Langley Research Center.
Performance characteristics of an adaptive controller based on least-mean-square filters
NASA Technical Reports Server (NTRS)
Mehta, R. S.; Merhav, S. J.
1986-01-01
A closed-loop, adaptive-control scheme that uses a least-mean-square filter as the controller model is presented, along with simulation results that demonstrate the excellent robustness of this scheme. It is shown that the scheme adapts very well to unknown plants, even those that are marginally stable, responds appropriately to changes in plant parameters, and is not unduly affected by additive noise. A heuristic argument for the conditions necessary for convergence is presented. Potential applications and extensions of the scheme are also discussed.
Performance characteristics of an adaptive controller based on least-mean-square filters
NASA Technical Reports Server (NTRS)
Mehta, Rajiv S.; Merhav, Shmuel J.
1986-01-01
A closed loop, adaptive control scheme that uses a least mean square filter as the controller model is presented, along with simulation results that demonstrate the excellent robustness of this scheme. It is shown that the scheme adapts very well to unknown plants, even those that are marginally stable, responds appropriately to changes in plant parameters, and is not unduly affected by additive noise. A heuristic argument for the conditions necessary for convergence is presented. Potential applications and extensions of the scheme are also discussed.
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 filtering of biodynamic stick feedthrough in manipulation tasks on board moving platforms
NASA Technical Reports Server (NTRS)
Velger, M.; Grunwald, A.; Merhav, S.
1986-01-01
A novel approach to suppress the effects of biodynamic interference is presented. An adaptive noise canceling technique is employed for substracting the platform motion correlated components from the control stick output. The effects of biodynamic interference and its suppression by adaptive noise cancellation has been evaluated in a series of tracking tasks performed in a moving base simulator. Simulator motions were in pitch, roll and combined pitch and roll. Human operator performance was assessed from the mean square values of the tracking error and the control activity. The tracking error and the total stick output signal were found to increase significantly with motion and to diminish substantially with adaptive noise cancellation, thus providing a considerable improvement in tracking performance under conditions in which platform motion were present. The adaptive filter was found to cause a significant increase in the cross-over frequency and decrease in the phase margin. Moreover, the adaptive filter was found to significantly improve the human operator visual motor response. This improvement is manifested as an increased human operator gain, a smaller time delay and lower pilot workload.
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.
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.
Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion.
Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing
2011-01-01
In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.
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.
Clutter interference and the integration time of echoes in the echolocating bat, Eptesicus fuscus.
Simmons, J A; Freedman, E G; Stevenson, S B; Chen, L; Wohlgenant, T J
1989-10-01
The ability of the echolocating bat, Eptesicus fuscus, to detect a sonar target is affected by the presence of other targets along the same axis at slightly different ranges. If echoes from one target arrive at about the same delay as echoes from another target, clutter interference occurs and one set of echoes masks the other. Although the bat's sonar emissions and the echoes themselves are 2 to 5 ms long, echoes (of approximately equal sensation levels--around 15 dB SL) only interfere with each other if they arrive within 200 to 400 microseconds of the same arrival time. This figure is an estimate of the integration time of the bat's sonar receiver for echoes. The fine structure of the clutter-interference data reflects the reinforcement and cancellation of echoes according to their time separation. When clutter interference first occurs, the waveforms of test and cluttering echoes already overlap for much of their duration. The masking effect underlying clutter interference appears specifically due to overlap, not between raw echo waveforms, but between the patterns of mechanical excitation created when echoes pass through bandpass filters equivalent to auditory-nerve tuning curves. While the time scale of clutter interference is substantially shorter than the duration of echo waveforms, it still is much longer than the eventual width of a target's range-axis image expressed in terms of echo delay.
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%.
Ockham's Razor Applied: It's Mission Clutter.
ERIC Educational Resources Information Center
DeCicco, Emily K.; Allison, Jeanette
1999-01-01
Maintains that assigning schools responsibility for solving societal problems results in mission clutter, the root cause of failure in public schools. Discusses how mission clutter occurred and its dangers, focusing on the loss of critical educational focus, impediment to teacher recruitment and retention, occupational stress, and unfair…
Dynamic Singularity Spectrum Distribution of Sea Clutter
NASA Astrophysics Data System (ADS)
Xiong, Gang; Yu, Wenxian; Zhang, Shuning
2015-12-01
The fractal and multifractal theory have provided new approaches for radar signal processing and target-detecting under the background of ocean. However, the related research mainly focuses on fractal dimension or multifractal spectrum (MFS) of sea clutter. In this paper, a new dynamic singularity analysis method of sea clutter using MFS distribution is developed, based on moving detrending analysis (DMA-MFSD). Theoretically, we introduce the time information by using cyclic auto-correlation of sea clutter. For transient correlation series, the instantaneous singularity spectrum based on multifractal detrending moving analysis (MF-DMA) algorithm is calculated, and the dynamic singularity spectrum distribution of sea clutter is acquired. In addition, we analyze the time-varying singularity exponent ranges and maximum position function in DMA-MFSD of sea clutter. For the real sea clutter data, we analyze the dynamic singularity spectrum distribution of real sea clutter in level III sea state, and conclude that the radar sea clutter has the non-stationary and time-varying scale characteristic and represents the time-varying singularity spectrum distribution based on the proposed DMA-MFSD method. The DMA-MFSD will also provide reference for nonlinear dynamics and multifractal signal processing.
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; ...
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
Clutter Management for Individuals with Multiple Sclerosis
2014-01-01
Background: Although there is substantial anecdotal evidence that clutter is common among people with multiple sclerosis (MS), the literature contains no reports of studies on the actual prevalence of the problem or its impact on functional performance in this population. Clutter promotes confusion and places individuals in potentially dangerous situations by increasing their risks of falling, losing medications, and misplacing important documents. In addition, it may negatively affect activities of daily living (ADLs). Many common MS symptoms such as decreased mobility, visual or cognitive changes, fatigue, and depression can exacerbate clutter accumulation, which in turn can have detrimental effects on physical, financial, emotional, cognitive, and social functioning. It is critical for MS clinicians to address clutter management in order to improve patients' overall functional independence and participation. Methods: A clutter reduction protocol was developed and implemented at our institution for individuals with MS. Our group program addresses psychosocial issues preventing organization and offers practical strategies for clutter removal and management to improve performance in ADLs. A clutter questionnaire is administered to individuals before and after their participation in the group program. Results: Anecdotal reports indicate that the intervention helped to reduce clutter, promote a more realistic attitude toward “possessions,” and establish a sense of accomplishment in controlling one's environment. Participants also reported fewer falls, feeling less isolated, increased ease in finding their medications, and a general sense of cognitive clarity in accomplishing ADLs. Outcome assessments are now being developed to objectively measure these effects as well as the prevalence of clutter within the MS population. Conclusions: Clutter management is an important area for MS clinicians to address because it can significantly affect patients' functioning
... disorder characterized by a rapid and/or irregular speaking rate, excessive disfluencies, and often other symptoms such ... Has little or no apparent physical struggle in speaking. Has few if any accessory (secondary) behaviors. A ...
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.
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-05-30
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.
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
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.
Automatic nevi segmentation using adaptive mean shift filters and feature analysis
NASA Astrophysics Data System (ADS)
King, Michael A.; Lee, Tim K.; Atkins, M. Stella; McLean, David I.
2004-05-01
A novel automatic method of segmenting nevi is explained and analyzed in this paper. The first step in nevi segmentation is to iteratively apply an adaptive mean shift filter to form clusters in the image and to remove noise. The goal of this step is to remove differences in skin intensity and hairs from the image, while still preserving the shape of nevi present on the skin. Each iteration of the mean shift filter changes pixel values to be a weighted average of pixels in its neighborhood. Some new extensions to the mean shift filter are proposed to allow for better segmentation of nevi from the skin. The kernel, that describes how the pixels in its neighborhood will be averaged, is adaptive; the shape of the kernel is a function of the local histogram. After initial clustering, a simple merging of clusters is done. Finally, clusters that are local minima are found and analyzed to determine which clusters are nevi. When this algorithm was compared to an assessment by an expert dermatologist, it showed a sensitivity rate and diagnostic accuracy of over 95% on the test set, for nevi larger than 1.5mm.
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
2014-12-09
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.
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 relatively 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 to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so-called "curse of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF could be even more computationally expensive than EnKF. Motivated by most recent developments in uncertainty quantification, 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. The "restart" technology is used to eliminate the inconsistency between model parameters and states. The performance of RAPCKF is tested with numerical cases of unsaturated flow models. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.
Application of an automatic adaptive filter for Heart Rate Variability analysis.
Dos Santos, Laurita; Barroso, Joaquim J; Macau, Elbert E N; de Godoy, Moacir F
2013-12-01
The presence of artifacts and noise effects in temporal series can seriously hinder the analysis of Heart Rate Variability (HRV). The tachograms should be carefully edited to avoid erroneous interpretations. The physician should carefully analyze the tachogram in order to detect points that might be associated with unlikely biophysical behavior and manually eliminate them from the data series. However, this is a time-consuming procedure. To facilitate the pre-analysis of the tachogram, this study uses a method of data filtering based on an adaptive filter which is quickly able to analyze a large amount of data. The method was applied to 229 time series from a database of patients with different clinical conditions: premature newborns, full-term newborns, healthy young adults, adults submitted to a very-low-calorie diet, and adults under preoperative evaluation for coronary artery bypass grafting. This proposed method is compared to the demanding conventional method, wherein the corrections of occasional ectopic beats and artifacts are usually manually executed by a specialist. To confirm the reliability of the results obtained, correlation coefficients were calculated, using both automatic and manual methods of ltering for each HRV index selected. A high correlation between the results was found, with highly significant p values, for all cases, except for some parameters analyzed in the premature newborns group, an issue that is thoroughly discussed. The authors concluded that the proposed adaptive filtering method helps to efficiently handle the task of editing temporal series for HRV analysis.
Superconducting Magnetometry for Cardiovascular Studies and AN Application of Adaptive Filtering.
NASA Astrophysics Data System (ADS)
Leifer, Mark Curtis
Sensitive magnetic detectors utilizing Superconducting Quantum Interference Devices (SQUID's) have been developed and used for studying the cardiovascular system. The theory of magnetic detection of cardiac currents is discussed, and new experimental data supporting the validity of the theory is presented. Measurements on both humans and dogs, in both healthy and diseased states, are presented using the new technique, which is termed vector magnetocardiography. In the next section, a new type of superconducting magnetometer with a room temperature pickup is analyzed, and techniques for optimizing its sensitivity to low-frequency sub-microamp currents are presented. Performance of the actual device displays significantly improved sensitivity in this frequency range, and the ability to measure currents in intact, in vivo biological fibers. The final section reviews the theoretical operation of a digital self-optimizing filter, and presents a four-channel software implementation of the system. The application of the adaptive filter to enhancement of geomagnetic signals for earthquake forecasting is discussed, and the adaptive filter is shown to outperform existing techniques in suppressing noise from geomagnetic records.
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.
Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems
NASA Astrophysics Data System (ADS)
Imani, Mahdi; Braga-Neto, Ulisses M.
2017-01-01
Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear models with application in estimation and control of Boolean processes based on noisy and incomplete measurements. The optimal minimum mean square error (MMSE) algorithms for POBDS state estimation, namely, the Boolean Kalman filter (BKF) and Boolean Kalman smoother (BKS), are intractable in the case of large systems, due to computational and memory requirements. To address this, we propose approximate MMSE filtering and smoothing algorithms based on the auxiliary particle filter (APF) method from sequential Monte-Carlo theory. These algorithms are used jointly with maximum-likelihood (ML) methods for simultaneous state and parameter estimation in POBDS models. In the presence of continuous parameters, ML estimation is performed using the expectation-maximization (EM) algorithm; we develop for this purpose a special smoother which reduces the computational complexity of the EM algorithm. The resulting particle-based adaptive filter is applied to a POBDS model of Boolean gene regulatory networks observed through noisy RNA-Seq time series data, and performance is assessed through a series of numerical experiments using the well-known cell cycle gene regulatory model.
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.
Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter
Chu, Hairong; Sun, Tingting; Zhang, Baiqiang; Zhang, Hongwei; Chen, Yang
2017-01-01
In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the “Velocity and Attitude” matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment. PMID:28098829
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.
Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter.
Chu, Hairong; Sun, Tingting; Zhang, Baiqiang; Zhang, Hongwei; Chen, Yang
2017-01-14
In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the "Velocity and Attitude" matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.
Zhang, Yin; Chase, Steve M
2013-01-01
Neural prosthetics are a promising technology for alleviating paralysis by actuating devices directly from the intention to move. Typical implementations of these devices require a calibration session to define decoding parameters that map recorded neural activity into movement of the device. However, a major factor limiting the clinical deployment of this technology is stability: with fixed decoding parameters, control of the prosthetic device has been shown to degrade over time. Here we apply a dual estimation procedure to adaptively capture changes in decoding parameters. In simulation, we find that our stabilized dual Kalman filter can run autonomously for hundreds of thousands of trials with little change in performance. Further, when we apply our algorithm off-line to estimate arm trajectories from neural data recorded over five consecutive days, we find that it outperforms a static Kalman filter, even when it is re-calibrated at the beginning of each day.
Recursive Bayesian electromagnetic refractivity estimation from radar sea clutter
NASA Astrophysics Data System (ADS)
Vasudevan, Sathyanarayanan; Anderson, Richard H.; Kraut, Shawn; Gerstoft, Peter; Rogers, L. Ted; Krolik, Jeffrey L.
2007-04-01
Estimation of the range- and height-dependent index of refraction over the sea surface facilitates prediction of ducted microwave propagation loss. In this paper, refractivity estimation from radar clutter returns is performed using a Markov state space model for microwave propagation. Specifically, the parabolic approximation for numerical solution of the wave equation is used to formulate the refractivity from clutter (RFC) problem within a nonlinear recursive Bayesian state estimation framework. RFC under this nonlinear state space formulation is more efficient than global fitting of refractivity parameters when the total number of range-varying parameters exceeds the number of basis functions required to represent the height-dependent field at a given range. Moreover, the range-recursive nature of the estimator can be easily adapted to situations where the refractivity modeling changes at discrete ranges, such as at a shoreline. A fast range-recursive solution for obtaining range-varying refractivity is achieved by using sequential importance sampling extensions to state estimation techniques, namely, the forward and Viterbi algorithms. Simulation and real data results from radar clutter collected off Wallops Island, Virginia, are presented which demonstrate the ability of this method to produce propagation loss estimates that compare favorably with ground truth refractivity measurements.
Phenomenology and Signal Processing for UXO/Clutter Discrimination
2009-08-01
1 2. Multiaxis EMI Measurements of Clutter Items ...................................................................... 4...items that have signature parameters that are significantly different than those of UXO items. 4 2. Multiaxis EMI Measurements of Clutter Items...The first task of this project was to assemble a clutter signature library based on multiaxis EMI measurements of clutter items. An inventory of
NASA Astrophysics Data System (ADS)
Sakata, Ren; Tomioka, Tazuko; Kobayashi, Takahiro
When a cognitive radio system dynamically utilizes a frequency band, channel control information must be communicated over the network in order for the currently available carrier frequencies to be shared. In order to keep efficient spectrum utilization, this control information should also be dynamically transmitted through channels such as cognitive pilot channels based on the channel conditions. If transmitters dynamically select carrier frequencies, receivers must receive the control signal without knowledge of its carrier frequencies. A novel scheme called differential code parallel transmission (DCPT) enables receivers to receive low-rate information without any knowledge of the carrier frequency. The transmitter simultaneously transmits two signals whose carrier frequencies are separated by a predefined value. The absolute values of the carrier frequencies can be varied. When the receiver receives the DCPT signal, it multiplies the signal by a frequency-shifted version of itself; this yields a DC component that represents the data signal, which is then demodulated. However, the multiplication process results in the noise power being squared, necessitating high received signal power. In this paper, to realize a bandpass filter that passes only DCPT signals of unknown frequency and that suppresses noise and interference at other frequencies, a DCPT-adaptive bandpass filter (ABF) that employs an adaptive equalizer is proposed. In the training phase, the received signal is the filter input and the frequency-shifted signal is the training input. Then, the filter is trained to pass the higher-frequency signal of the two DCPT signals. The performance of DCPT-ABF is evaluated through computer simulations. We find that DCPT-ABF operates successfully even under strong interference.
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.
CT image artifacts from brachytherapy seed implants: A postprocessing 3D adaptive median filter
Basran, Parminder S.; Robertson, Andrew; Wells, Derek
2011-02-15
Purpose: To design a postprocessing 3D adaptive median filter that minimizes streak artifacts and improves soft-tissue contrast in postoperative CT images of brachytherapy seed implantations. Methods: The filter works by identifying voxels that are likely streaks and estimating more reflective voxel intensity by using voxel intensities in adjacent CT slices and applying a median filter over voxels not identified as seeds. Median values are computed over a 5x5x5 mm region of interest (ROI) within the CT volume. An acrylic phantom simulating a clinical seed implant arrangement and containing nonradioactive seeds was created. Low contrast subvolumes of tissuelike material were also embedded in the phantom. Pre- and postprocessed image quality metrics were compared using the standard deviation of ROIs between the seeds, the CT numbers of low contrast ROIs embedded within the phantom, the signal to noise ratio (SNR), and the contrast to noise ratio (CNR) of the low contrast ROIs. The method was demonstrated with a clinical postimplant CT dataset. Results: After the filter was applied, the standard deviation of CT values in streak artifact regions was significantly reduced from 76.5 to 7.2 HU. Within the observable low contrast plugs, the mean of all ROI standard deviations was significantly reduced from 60.5 to 3.9 HU, SNR significantly increased from 2.3 to 22.4, and CNR significantly increased from 0.2 to 4.1 (all P<0.01). The mean CT in the low contrast plugs remained within 5 HU of the original values. Conclusion: An efficient postprocessing filter that does not require access to projection data, which can be applied irrespective of CT scan parameters has been developed, provided the slice thickness and spacing is 3 mm or less.
NASA Astrophysics Data System (ADS)
Bordbar, Behzad; Farwell, Nathan H.; Vorontsov, Mikhail A.
2016-09-01
A novel scintillation resistant wavefront sensor based on a densely packed array of classical Zernike filters, referred to as the multi-aperture Zernike wavefront sensor (MAZ-WFS), is introduced and analyzed through numerical simulations. Wavefront phase reconstruction in the MAZ-WFS is performed using iterative algorithms that are optimized for phase aberration sensing in severe atmospheric turbulence conditions. The results demonstrate the potential of the MAZ-WFS for high-resolution retrieval of turbulence-induced phase aberrations in strong scintillation conditions for atmospheric sensing and adaptive optics applications.
Aboy, Mateo; Márquez, Oscar W; McNames, James; Hornero, Roberto; Trong, Tran; Goldstein, Brahm
2005-08-01
We describe an algorithm to estimate the instantaneous power spectral density (PSD) of nonstationary signals. The algorithm is based on a dual Kalman filter that adaptively generates an estimate of the autoregressive model parameters at each time instant. The algorithm exhibits superior PSD tracking performance in nonstationary signals than classical nonparametric methodologies, and does not assume local stationarity of the data. Furthermore, it provides better time-frequency resolution, and is robust to model mismatches. We demonstrate its usefulness by a sample application involving PSD estimation of intracranial pressure signals (ICP) from patients with traumatic brain injury (TBI).
SOGI-FLL Based Adaptive Filter for DSTATCOM Under Variable Supply Frequency
NASA Astrophysics Data System (ADS)
Puranik, Vishal; Arya, Sabha Raj
2016-12-01
This paper presents an adaptive filter based on second order generalized integrator-frequency locked loop (SOGI-FLL) for distribution static compensator (DSTATCOM) operating under variable supply frequency with nonlinear load. It is observed that under variable supply frequency, the FLL provides an excellent frequency tracking performance. Necessary compensation can be provided by DSTATCOM at any frequency with the help of SOGI-FLL. The MATLAB simulink model of DSTATCOM is developed with SOGI-FLL based control algorithm and rectifier based nonlinear load. This three wire system is simulated in power factor correction and zero voltage regulation mode under variable supply frequency.
Use of polarization to improve signal to clutter ratio in an outdoor active imaging system
NASA Astrophysics Data System (ADS)
Fontoura, Patrick F.; Giles, Michael K.; Padilla, Denise D.
2005-08-01
This paper describes the methodology and presents the results of the design of a polarization-sensitive system used to increase the signal-to-clutter ratio in a robust outdoor structured lighting sensor that uses standard CCD camera technology. This lighting sensor is intended to be used on an autonomous vehicle, looking down to the ground and horizontal to obstacles in an 8 foot range. The kinds of surfaces to be imaged are natural and man-made, such as asphalt, concrete, dirt and grass. The main problem for an outdoor eye-safe laser imaging system is that the reflected energy from background clutter tends to be brighter than the reflected laser energy. A narrow-band optical filter does not reduce significantly the background clutter in bright sunlight, and problems also occur when the surface is highly absorptive, like asphalt. Therefore, most of applications are limited to indoor and controlled outdoor conditions. A series of measurements was made for each of the materials studied in order to find the best configuration for the polarizing system and also to find out the potential improvement in the signal-to-clutter ratio (STC). This process was divided into three parts: characterization of the reflected sunlight, characterization of the reflected laser light, and measurement of the improvement in the STC. The results show that by using polarization properties it is possible to design an optical system that is able to increase the signal-to-clutter ratio from approximately 30% to 100% in the imaging system, depending on the kind of surface and on the incidence angle of the sunlight. The technique was also analyzed for indoor use, with the background clutter being the room illumination. For this specific case, polarization did not improve the signal-to-clutter ratio.
Adaptive Filtering Methods for Identifying Cross-Frequency Couplings in Human EEG
Van Zaen, Jérôme; Murray, Micah M.; Meuli, Reto A.; Vesin, Jean-Marc
2013-01-01
Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations. PMID:23560098
Adaptive filtering methods for identifying cross-frequency couplings in human EEG.
Van Zaen, Jérôme; Murray, Micah M; Meuli, Reto A; Vesin, Jean-Marc
2013-01-01
Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.
Computational models for synthetic marine infrared clutter
NASA Astrophysics Data System (ADS)
Constantikes, Kim T.; Zysnarski, Adam H.
1996-06-01
The next generation of ship defense missiles will need to engage stealthy, passive, sea-skimming missiles. Detection and guidance will occur against a background of sea surface and horizon which can present significant clutter problems for infrared seekers, particularly when targets are comparatively dim. We need a variety of sea clutter models: statistical image models for signal processing algorithm design, clutter occurrence models for systems effectiveness assessment, and constructive image models for synthesizing very large field-of-view (FOV) images with high spatial and temporal resolution. We have implemented and tested such a constructive model. First principle models of water waves and light transport provide a computationally intensive clutter model implemented as a raytracer. Our models include sea, sky, and solar radiance; reflectance; attenuating atmospheres; constructive solid geometry targets; target and water wave dynamics; and simple sensor image formation.
Adaptive Bloom Filter: A Space-Efficient Counting Algorithm for Unpredictable Network Traffic
NASA Astrophysics Data System (ADS)
Matsumoto, Yoshihide; Hazeyama, Hiroaki; Kadobayashi, Youki
The Bloom Filter (BF), a space-and-time-efficient hashcoding method, is used as one of the fundamental modules in several network processing algorithms and applications such as route lookups, cache hits, packet classification, per-flow state management or network monitoring. BF is a simple space-efficient randomized data structure used to represent a data set in order to support membership queries. However, BF generates false positives, and cannot count the number of distinct elements. A counting Bloom Filter (CBF) can count the number of distinct elements, but CBF needs more space than BF. We propose an alternative data structure of CBF, and we called this structure an Adaptive Bloom Filter (ABF). Although ABF uses the same-sized bit-vector used in BF, the number of hash functions employed by ABF is dynamically changed to record the number of appearances of a each key element. Considering the hash collisions, the multiplicity of a each key element on ABF can be estimated from the number of hash functions used to decode the membership of the each key element. Although ABF can realize the same functionality as CBF, ABF requires the same memory size as BF. We describe the construction of ABF and IABF (Improved ABF), and provide a mathematical analysis and simulation using Zipf's distribution. Finally, we show that ABF can be used for an unpredictable data set such as real network traffic.
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)
Steeb, P.; Krause, S.; Linke, P.; Hensen, C.; Dale, A. W.; Nuzzo, M.; Treude, T.
2014-11-01
Large amounts of methane are delivered by fluids through the erosive forearc of the convergent margin offshore Costa Rica and lead to the formation of cold seeps at the sediment surface. Besides mud extrusion, numerous cold seeps are created by landslides induced by seamount subduction or fluid migration along major faults. Most of the dissolved methane reaching the seafloor at cold seeps is oxidized within the benthic microbial methane filter by anaerobic oxidation of methane (AOM). Measurements of AOM and sulfate reduction as well as numerical modeling of porewater profiles revealed a highly active and efficient benthic methane filter at Quepos Slide site; a landslide on the continental slope between the Nicoya and Osa Peninsula. Integrated areal rates of AOM ranged from 12.9 ± 6.0 to 45.2 ± 11.5 mmol m-2 d-1, with only 1 to 2.5% of the upward methane flux being released into the water column. Additionally, two parallel sediment cores from Quepos Slide were used for in vitro experiments in a recently developed Sediment-F low-Through (SLOT) system to simulate an increased fluid and methane flux from the bottom of the sediment core. The benthic methane filter revealed a high adaptability whereby the methane oxidation efficiency responded to the increased fluid flow within 150-170 days. To our knowledge, this study provides the first estimation of the natural biogeochemical response of seep sediments to changes in fluid flow.
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
2016-10-10
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.
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. PMID:27818583
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
Real-time scale-adaptive correlation filters tracker with depth information to handle occlusion
NASA Astrophysics Data System (ADS)
Pi, Jiatian; Gu, Yuzhang; Hu, Keli; Cheng, Xiaoliu; Zhan, Yunlong; Wang, Yingguan
2016-07-01
In visual object tracking, occlusions significantly undermine the performance of tracking algorithms. RGB-D cameras, such as Microsoft Kinect or the related PrimeSense camera, are widely available to consumers. Great attention has been focused on exploiting depth information for object tracking in recent years. We propose an algorithm that improves the existing correlation filter-based tracker for scale-adaptive tracking. Moreover, we utilize depth information provided by the Kinect camera to handle various types of occlusions. First, the optimal location of the target is obtained by the conventional kernelized correlation filter tracker. Then, we make use of the discriminative correlation filter for scale estimation as an independent part. At last, to further improve the tracking performance under occlusions, we present a simple yet effective occlusion handling mechanism to detect occlusion and recovery. In this mechanism, cluster analysis and object segmentation by K-means method have been applied to depth data. Numerous experiments on Princeton RGB-D tracking dataset demonstrate that the proposed algorithm outperforms several state-of-the-art trackers by successfully dealing with occlusions.
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.
A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment
Chen, Xiao; Li, Yaan; Li, Yuxing; Yu, Jing; Li, Xiaohua
2016-01-01
The problem of data association for target tracking in a cluttered environment is discussed. In order to improve the real-time processing and accuracy of target tracking, based on a probabilistic data association algorithm, a novel data association algorithm using distance weighting was proposed, which can enhance the association probability of measurement originated from target, and then using a Kalman filter to estimate the target state more accurately. Thus, the tracking performance of the proposed algorithm when tracking non-maneuvering targets in a densely cluttered environment has improved, and also does better when two targets are parallel to each other, or at a small-angle crossing in a densely cluttered environment. As for maneuvering target issues, usually with an interactive multi-model framework, combined with the improved probabilistic data association method, we propose an improved algorithm using a combined interactive multiple model probabilistic data association algorithm to track a maneuvering target in a densely cluttered environment. Through Monte Carlo simulation, the results show that the proposed algorithm can be more effective and reliable for different scenarios of target tracking in a densely cluttered environment. PMID:27999347
Impact of Diverse Polarisations on Clutter Statistics
2005-09-29
Statistical inference ’ (Dover Publications Inc., Mineola, New York, 2003) 12 Dudewicz, E.J., and Mishra, S.N.: ‘Modern mathematical statistics ’ (John Wiley...Impact of diverse polarisations on clutter statistics M. Rangaswamy Abstract: The author addresses the impact of diverse polarisations on clutter... statistics in the context of waveform diversity for multi-functional operation from a specific platform as well as for multiple sensing from multiple
Lee, Jeon; Song, Mi-hye; Shin, Dong-gu; Lee, Kyoung-joung
2012-08-01
In this paper, an event synchronous adaptive filter (ESAF) is proposed to estimate atrial activity (AA) from a single-lead AF ECG in real time. The proposed ESAF is a kind of adaptive filter designed to have the reference fed with the impulse train synchronized with the R peak in a raw atrial fibrillation (AF) ECG and to input the timely delayed AF ECG into the primary input. To assess the performance, for ten simulated AF ECGs, the cross-correlation coefficient (ρ) and the normalized mean square error (NMSE) between estimated AAs and ten original simulated AAs were calculated and, for ten real AF ECGs, the ventricular residue (VR) in QRS interval and similarity (S) in non-QRS interval were computed. As a result, these four parameters were revealed as ρ = 0.938 ± 0.016 and NMSE = 0.243 ± 0.051 for simulated AF ECGs and VR = 1.190 ± 0.476 and S = 0.967 ± 0.041 for real AF ECGs. These results were found to be better than those of the averaged beat subtraction (ABS) method, which had been previously considered the only way to estimate AA automatically in real time. In conclusion, even with single-lead AF ECGs, the proposed method estimated AAs accurately and calculated the atrial fibrillatory frequencies, the most valuable index in AF maintenance and therapy evaluation, with a remarkably low computational cost.
An adaptive filter model of cerebellar zone C3 as a basis for safe limb control?
Dean, Paul; Anderson, Sean; Porrill, John; Jörntell, Henrik
2013-11-15
The review asks how the adaptive filter model of the cerebellum might be relevant to experimental work on zone C3, one of the most extensively studied regions of cerebellar cortex. As far as features of the cerebellar microcircuit are concerned, the model appears to fit very well with electrophysiological discoveries concerning the importance of molecular layer interneurons and their plasticity, the significance of long-term potentiation and the striking number of silent parallel fibre synapses. Regarding external connectivity and functionality, a key feature of the adaptive filter model is its use of the decorrelation algorithm, which renders it uniquely suited to problems of sensory noise cancellation. However, this capacity can be extended to the avoidance of sensory interference, by appropriate movements of, for example, the eyes in the vestibulo-ocular reflex. Avoidance becomes particularly important when painful signals are involved, and as the climbing fibre input to zone C3 is extremely responsive to nociceptive stimuli, it is proposed that one function of this zone is the avoidance of pain by, for example, adjusting movements of the body to avoid self-harm. This hypothesis appears consistent with evidence from humans and animals concerning the role of the intermediate cerebellum in classically conditioned withdrawal reflexes, but further experiments focusing on conditioned avoidance are required to test the hypothesis more stringently. The proposed architecture may also be useful for automatic self-adjusting damage avoidance in robots, an important consideration for next generation 'soft' robots designed to interact with people.
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.
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.
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
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
Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.
Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui
2017-01-01
To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.
Liu, Zong-Xiang; Wu, De-Hui; Xie, Wei-Xin; Li, Liang-Qun
2017-02-15
Tracking the target that maneuvers at a variable turn rate is a challenging problem. The traditional solution for this problem is the use of the switching multiple models technique, which includes several dynamic models with different turn rates for matching the motion mode of the target at each point in time. However, the actual motion mode of a target at any time may be different from all of the dynamic models, because these models are usually limited. To address this problem, we establish a formula for estimating the turn rate of a maneuvering target. By applying the estimation method of the turn rate to the multi-target Bayes (MB) filter, we develop a MB filter with an adaptive estimation of the turn rate, in order to track multiple maneuvering targets. Simulation results indicate that the MB filter with an adaptive estimation of the turn rate, is better than the existing filter at tracking the target that maneuvers at a variable turn rate.
NASA Astrophysics Data System (ADS)
Mahmood, Muhammad Tariq; Chu, Yeon-Ho; Choi, Young-Kyu
2016-06-01
This paper proposes a Rician noise reduction method for magnetic resonance (MR) images. The proposed method is based on adaptive non-local mean and guided image filtering techniques. In the first phase, a guidance image is obtained from the noisy image through an adaptive non-local mean filter. Sobel operators are applied to compute the strength of edges which is further used to control the spread of the kernel in non-local mean filtering. In the second phase, the noisy and the guidance images are provided to the guided image filter as input to restore the noise-free image. The improved performance of the proposed method is investigated using the simulated and real data sets of MR images. Its performance is also compared with the previously proposed state-of-the art methods. Comparative analysis demonstrates the superiority of the proposed scheme over the existing approaches.
Yuan, Qiangqiang; Zhang, Liangpei; Shen, Huanfeng
2013-06-01
Total variation is used as a popular and effective image prior model in the regularization-based image processing fields. However, as the total variation model favors a piecewise constant solution, the processing result under high noise intensity in the flat regions of the image is often poor, and some pseudoedges are produced. In this paper, we develop a regional spatially adaptive total variation model. Initially, the spatial information is extracted based on each pixel, and then two filtering processes are added to suppress the effect of pseudoedges. In addition, the spatial information weight is constructed and classified with k-means clustering, and the regularization strength in each region is controlled by the clustering center value. The experimental results, on both simulated and real datasets, show that the proposed approach can effectively reduce the pseudoedges of the total variation regularization in the flat regions, and maintain the partial smoothness of the high-resolution image. More importantly, compared with the traditional pixel-based spatial information adaptive approach, the proposed region-based spatial information adaptive total variation model can better avoid the effect of noise on the spatial information extraction, and maintains robustness with changes in the noise intensity in the super-resolution process.
Effects of yellow, orange and red filter glasses on the thresholds of a dark-adapted human eye.
Aarnisalo, E; Pehkonen, P
1990-04-01
Effects of 13 different yellow, orange and red (Schott) longpass filter glasses on the extrafoveal thresholds obtained by 3 normal subjects after dark-adaptation were measured using a Goldman-Weekers adaptometer. When filters GG400, GG420, GG435, GG455, GG475, GG495, OG515 and OG530 (cutting off radiation up to 527 nm) were used there was no significant change in the threshold value. However, significantly higher threshold values were obtained with the use of the filters OG550, OG570, OG590, RG610 and RG630.
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.
A model for the detection of moving targets in visual clutter inspired by insect physiology.
Wiederman, Steven D; Shoemaker, Patrick A; O'Carroll, David C
2008-07-30
We present a computational model for target discrimination based on intracellular recordings from neurons in the fly visual system. Determining how insects detect and track small moving features, often against cluttered moving backgrounds, is an intriguing challenge, both from a physiological and a computational perspective. Previous research has characterized higher-order neurons within the fly brain, known as 'small target motion detectors' (STMD), that respond robustly to moving features, even when the velocity of the target is matched to the background (i.e. with no relative motion cues). We recorded from intermediate-order neurons in the fly visual system that are well suited as a component along the target detection pathway. This full-wave rectifying, transient cell (RTC) reveals independent adaptation to luminance changes of opposite signs (suggesting separate ON and OFF channels) and fast adaptive temporal mechanisms, similar to other cell types previously described. From this physiological data we have created a numerical model for target discrimination. This model includes nonlinear filtering based on the fly optics, the photoreceptors, the 1(st) order interneurons (Large Monopolar Cells), and the newly derived parameters for the RTC. We show that our RTC-based target detection model is well matched to properties described for the STMDs, such as contrast sensitivity, height tuning and velocity tuning. The model output shows that the spatiotemporal profile of small targets is sufficiently rare within natural scene imagery to allow our highly nonlinear 'matched filter' to successfully detect most targets from the background. Importantly, this model can explain this type of feature discrimination without the need for relative motion cues.
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.
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
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.
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.
A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter.
Shabbouei Hagh, Yashar; Mohammadi Asl, Reza; Cocquempot, Vincent
2017-01-01
In this paper, a new hybrid robust fault tolerant control scheme is proposed. A robust H∞ control law is used in non-faulty situation, while a Non-Singular Terminal Sliding Mode (NTSM) controller is activated as soon as an actuator fault is detected. Since a linear robust controller is designed, the system is first linearized through the feedback linearization method. To switch from one controller to the other, a fuzzy based switching system is used. An Adaptive Joint Unscented Kalman Filter (AJUKF) is used for fault detection and diagnosis. The proposed method is based on the simultaneous estimation of the system states and parameters. In order to show the efficiency of the proposed scheme, a simulated 3-DOF robotic manipulator is used.
Adaptive wavelet packet-based de-speckling of ultrasound images with bilateral filter.
Esakkirajan, Sankaralingam; Vimalraj, Chinna Thambi; Muhammed, Rashad; Subramanian, Ganapathi
2013-12-01
A new adaptive wavelet packet-based approach to minimize speckle noise in ultrasound images is proposed. This method combines wavelet packet thresholding with a bilateral filter. Here, the best bases after wavelet packet decomposition are selected by comparing the first singular value of all sub-bands, and the noisy coefficients are thresholded using a modified NeighShrink technique. The algorithm is tested with various ultrasound images, and the results, in terms of peak signal-to-noise ratio and mean structural similarity values, are compared with those for some well-known de-speckling techniques. The simulation results indicate that the proposed method has better potential to minimize speckle noise and retain fine details of the ultrasound image.
Hongda Wang; Chiu-Sing Choy
2016-08-01
The ability of correlation integral for automatic seizure detection using scalp EEG data has been re-examined in this paper. To facilitate the detection performance and overcome the shortcoming of correlation integral, nonlinear adaptive denoising and Kalman filter have been adopted for pre-processing and post-processing. The three-stage algorithm has achieved 84.6% sensitivity and 0.087/h false detection rate, which are comparable to many machine learning based methods, but at much lower computational cost. Since this algorithm is tested with long-term scalp EEG, it has the potential to achieve higher performance with intracranial EEG. The clinical value of this algorithm includes providing a pre-judgement to assist the doctor's diagnosis procedure and acting as a reliable warning system in a wearable device for epilepsy patients.
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.
Adaptive update using visual models for lifting-based motion-compensated temporal filtering
NASA Astrophysics Data System (ADS)
Li, Song; Xiong, H. K.; Wu, Feng; Chen, Hong
2005-03-01
Motion compensated temporal filtering is a useful framework for fully scalable video compression schemes. However, when supposed motion models cannot represent a real motion perfectly, both the temporal high and the temporal low frequency sub-bands may contain artificial edges, which possibly lead to a decreased coding efficiency, and ghost artifacts appear in the reconstructed video sequence at lower bit rates or in case of temporal scaling. We propose a new technique that is based on utilizing visual models to mitigate ghosting artifacts in the temporal low frequency sub-bands. Specifically, we propose content adaptive update schemes where visual models are used to determine image dependent upper bounds on information to be updated. Experimental results show that the proposed algorithm can significantly improve subjective visual quality of the low-pass temporal frames and at the same time, coding performance can catch or exceed the classical update steps.
Adaptive fused Kalman filter based on imaging laser radar for TAN
NASA Astrophysics Data System (ADS)
Gong, Junbin; Xu, Hongbo; Tian, Jinwen; Cheng, Hua; Zhang, Jun
2007-11-01
Terrain aided navigation (TAN) is an efficient way to periodically correct the error accumulation of INS. The imaging laser radar is an ideal imaging sensor in TAN for the low-flying aircraft and unmanned air vehicles for the high precision multi-dimensional data acquisition capability and concealable attribute. In this paper, a new framework for applying the laser radar to terrain aided navigation is put forward. Then a new adaptive fused Kalman Filter is proposed to improve the accuracy and robustness. At last, the key factors affected the algorithm are analyzed and the comparative experimentations are presented. The simulating experiments show that the proposed algorithm improves the location accuracy, and has good initial error tolerance and fine robustness. It shows that this approach is a valid solution for the application.
Adaptive Kalman filtering for real-time mapping of the visual field.
Ward, B Douglas; Janik, John; Mazaheri, Yousef; Ma, Yan; DeYoe, Edgar A
2012-02-15
This paper demonstrates the feasibility of real-time mapping of the visual field for clinical applications. Specifically, three aspects of this problem were considered: (1) experimental design, (2) statistical analysis, and (3) display of results. Proper experimental design is essential to achieving a successful outcome, particularly for real-time applications. A random-block experimental design was shown to have less sensitivity to measurement noise, as well as greater robustness to error in modeling of the hemodynamic impulse response function (IRF) and greater flexibility than common alternatives. In addition, random encoding of the visual field allows for the detection of voxels that are responsive to multiple, not necessarily contiguous, regions of the visual field. Due to its recursive nature, the Kalman filter is ideally suited for real-time statistical analysis of visual field mapping data. An important feature of the Kalman filter is that it can be used for nonstationary time series analysis. The capability of the Kalman filter to adapt, in real time, to abrupt changes in the baseline arising from subject motion inside the scanner and other external system disturbances is important for the success of clinical applications. The clinician needs real-time information to evaluate the success or failure of the imaging run and to decide whether to extend, modify, or terminate the run. Accordingly, the analytical software provides real-time displays of (1) brain activation maps for each stimulus segment, (2) voxel-wise spatial tuning profiles, (3) time plots of the variability of response parameters, and (4) time plots of activated volume.
Zhao, Haiquan; Zhang, Jiashu
2010-02-01
A novel nonlinear adaptive filter with pipelined Chebyshev functional link artificial recurrent neural network (PCFLARNN) is presented in this paper, which uses a modification real-time recurrent learning algorithm. The PCFLARNN consists of a number of simple small-scale Chebyshev functional link artificial recurrent neural network (CFLARNN) modules. Compared to the standard recurrent neural network (RNN), those modules of PCFLARNN can simultaneously be performed in a pipelined parallelism fashion, and this would lead to a significant improvement in its total computational efficiency. Furthermore, contrasted with the architecture of a pipelined RNN (PRNN), each module of PCFLARNN is a CFLARNN whose nonlinearity is introduced by enhancing the input pattern with Chebyshev functional expansion, whereas the RNN of each module in PRNN utilizing linear input and first-order recurrent term only fails to utilize the high-order terms of inputs. Therefore, the performance of PCFLARNN can further be improved at the cost of a slightly increased computational complexity. In addition, due to the introduced nonlinear functional expansion of each module in PRNN, the number of input signals can be reduced. Computer simulations have demonstrated that the proposed filter performs better than PRNN and RNN for nonlinear colored signal prediction, nonstationary speech signal prediction, and chaotic time series prediction.
A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.
Zhao, Haiquan; Zhang, Jiashu
2009-12-01
To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.
Charisis, Vasileios S; Hadjileontiadis, Leontios J
2016-03-01
The aim of this Letter is to present a new capsule endoscopy (CE) image analysis scheme for the detection of small bowel ulcers that relate to Crohn's disease. More specifically, this scheme is based on: (i) a hybrid adaptive filtering (HAF) process, that utilises genetic algorithms to the curvelet-based representation of images for efficient extraction of the lesion-related morphological characteristics, (ii) differential lacunarity (DL) analysis for texture feature extraction from the HAF-filtered images and (iii) support vector machines for robust classification performance. For the training of the proposed scheme, namely HAF-DL, an 800-image database was used and the evaluation was based on ten 30-second long endoscopic videos. Experimental results, along with comparison with other related efforts, have shown that the HAF-DL approach evidently outperforms the latter in the field of CE image analysis for automated lesion detection, providing higher classification results. The promising performance of HAF-DL paves the way for a complete computer-aided diagnosis system that could support the physicians' clinical practice.
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.
Complex lung motion estimation via adaptive bilateral filtering of the deformation field.
Papiez, Bartlomiej W; Heinrich, Mattias Paul; Risser, Laurent; Schnabel, Julia A
2013-01-01
Estimation of physiologically plausible deformations is critical for several medical applications. For example, lung cancer diagnosis and treatment requires accurate image registration which preserves sliding motion in the pleural cavity, and the rigidity of chest bones. This paper addresses these challenges by introducing a novel approach for regularisation of non-linear transformations derived from a bilateral filter. For this purpose, the classic Gaussian kernel is replaced by a new kernel that smoothes the estimated deformation field with respect to the spatial position, intensity and deformation dissimilarity. The proposed regularisation is a spatially adaptive filter that is able to preserve discontinuity between the lungs and the pleura and reduces any rigid structures deformations in volumes. Moreover, the presented framework is fully automatic and no prior knowledge of the underlying anatomy is required. The performance of our novel regularisation technique is demonstrated on phantom data for a proof of concept as well as 3D inhale and exhale pairs of clinical CT lung volumes. The results of the quantitative evaluation exhibit a significant improvement when compared to the corresponding state-of-the-art method using classic Gaussian smoothing.
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.
NASA Astrophysics Data System (ADS)
Wu, Chunyan; Liu, Jian; Peng, Fuqiang; Yu, Dejie; Li, Rong
2013-07-01
When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.
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.
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.
A fast image super-resolution algorithm using an adaptive Wiener filter.
Hardie, Russell
2007-12-01
A computationally simple super-resolution algorithm using a type of adaptive Wiener filter is proposed. The algorithm produces an improved resolution image from a sequence of low-resolution (LR) video frames with overlapping field of view. The algorithm uses subpixel registration to position each LR pixel value on a common spatial grid that is referenced to the average position of the input frames. The positions of the LR pixels are not quantized to a finite grid as with some previous techniques. The output high-resolution (HR) pixels are obtained using a weighted sum of LR pixels in a local moving window. Using a statistical model, the weights for each HR pixel are designed to minimize the mean squared error and they depend on the relative positions of the surrounding LR pixels. Thus, these weights adapt spatially and temporally to changing distributions of LR pixels due to varying motion. Both a global and spatially varying statistical model are considered here. Since the weights adapt with distribution of LR pixels, it is quite robust and will not become unstable when an unfavorable distribution of LR pixels is observed. For translational motion, the algorithm has a low computational complexity and may be readily suitable for real-time and/or near real-time processing applications. With other motion models, the computational complexity goes up significantly. However, regardless of the motion model, the algorithm lends itself to parallel implementation. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using simulated and real video sequences. A computational analysis is also presented.
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.
Improved characterization of slow-moving landslides by means of adaptive NL-InSAR filtering
NASA Astrophysics Data System (ADS)
Albiol, David; Iglesias, Rubén.; Sánchez, Francisco; Duro, Javier
2014-10-01
Advanced remote sensing techniques based on space-borne Synthetic Aperture Radar (SAR) have been developed during the last decade showing their applicability for the monitoring of surface displacements in landslide areas. This paper presents an advanced Persistent Scatterer Interferometry (PSI) processing based on the Stable Point Network (SPN) technique, developed by the company Altamira-Information, for the monitoring of an active slowmoving landslide in the mountainous environment of El Portalet, Central Spanish Pyrenees. For this purpose, two TerraSAR-X data sets acquired in ascending mode corresponding to the period from April to November 2011, and from August to November 2013, respectively, are employed. The objective of this work is twofold. On the one hand, the benefits of employing Nonlocal Interferomtric SAR (NL-InSAR) adaptive filtering techniques over vegetated scenarios to maximize the chances of detecting natural distributed scatterers, such as bare or rocky areas, and deterministic point-like scatterers, such as man-made structures or poles, is put forward. In this context, the final PSI displacement maps retrieved with the proposed filtering technique are compared in terms of pixels' density and quality with classical PSI, showing a significant improvement. On the other hand, since SAR systems are only sensitive to detect displacements in the line-of-sight (LOS) direction, the importance of projecting the PSI displacement results retrieved along the steepest gradient of the terrain slope is discussed. The improvements presented in this paper are particularly interesting in these type of applications since they clearly allow to better determine the extension and dynamics of complex landslide phenomena.
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.
Wheeler, Alyssa R; Fulton, Kara A; Gaudette, Jason E; Simmons, Ryan A; Matsuo, Ikuo; Simmons, James A
2016-01-01
Big brown bats (Eptesicus fuscus) emit trains of brief, wideband frequency-modulated (FM) echolocation sounds and use echoes of these sounds to orient, find insects, and guide flight through vegetation. They are observed to emit sounds that alternate between short and long inter-pulse intervals (IPIs), forming sonar sound groups. The occurrence of these strobe groups has been linked to flight in cluttered acoustic environments, but how exactly bats use sonar sound groups to orient and navigate is still a mystery. Here, the production of sound groups during clutter navigation was examined. Controlled flight experiments were conducted where the proximity of the nearest obstacles was systematically decreased while the extended scene was kept constant. Four bats flew along a corridor of varying widths (100, 70, and 40 cm) bounded by rows of vertically hanging plastic chains while in-flight echolocation calls were recorded. Bats shortened their IPIs for more rapid spatial sampling and also grouped their sounds more tightly when flying in narrower corridors. Bats emitted echolocation calls with progressively shorter IPIs over the course of a flight, and began their flights by emitting shorter starting IPI calls when clutter was denser. The percentage of sound groups containing 3 or more calls increased with increasing clutter proximity. Moreover, IPI sequences having internal structure become more pronounced when corridor width narrows. A novel metric for analyzing the temporal organization of sound sequences was developed, and the results indicate that the time interval between echolocation calls depends heavily on the preceding time interval. The occurrence of specific IPI patterns were dependent upon clutter, which suggests that sonar sound grouping may be an adaptive strategy for coping with pulse-echo ambiguity in cluttered surroundings.
Wheeler, Alyssa R.; Fulton, Kara A.; Gaudette, Jason E.; Simmons, Ryan A.; Matsuo, Ikuo; Simmons, James A.
2016-01-01
Big brown bats (Eptesicus fuscus) emit trains of brief, wideband frequency-modulated (FM) echolocation sounds and use echoes of these sounds to orient, find insects, and guide flight through vegetation. They are observed to emit sounds that alternate between short and long inter-pulse intervals (IPIs), forming sonar sound groups. The occurrence of these strobe groups has been linked to flight in cluttered acoustic environments, but how exactly bats use sonar sound groups to orient and navigate is still a mystery. Here, the production of sound groups during clutter navigation was examined. Controlled flight experiments were conducted where the proximity of the nearest obstacles was systematically decreased while the extended scene was kept constant. Four bats flew along a corridor of varying widths (100, 70, and 40 cm) bounded by rows of vertically hanging plastic chains while in-flight echolocation calls were recorded. Bats shortened their IPIs for more rapid spatial sampling and also grouped their sounds more tightly when flying in narrower corridors. Bats emitted echolocation calls with progressively shorter IPIs over the course of a flight, and began their flights by emitting shorter starting IPI calls when clutter was denser. The percentage of sound groups containing 3 or more calls increased with increasing clutter proximity. Moreover, IPI sequences having internal structure become more pronounced when corridor width narrows. A novel metric for analyzing the temporal organization of sound sequences was developed, and the results indicate that the time interval between echolocation calls depends heavily on the preceding time interval. The occurrence of specific IPI patterns were dependent upon clutter, which suggests that sonar sound grouping may be an adaptive strategy for coping with pulse-echo ambiguity in cluttered surroundings. PMID:27445723
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.
State estimation for autonomous flight in cluttered environments
NASA Astrophysics Data System (ADS)
Langelaan, Jacob Willem
Safe, autonomous operation in complex, cluttered environments is a critical challenge facing autonomous mobile systems. The research described in this dissertation was motivated by a particularly difficult example of autonomous mobility: flight of a small Unmanned Aerial Vehicle (UAV) through a forest. In cluttered environments (such as forests or natural and urban canyons) signals from navigation beacons such as GPS may frequently be occluded. Direct measurements of vehicle position are therefore unavailable, and information required for flight control, obstacle avoidance, and navigation must be obtained using only on-board sensors. However, payload limitations of small UAVs restrict both the mass and physical dimensions of sensors that can be carried. This dissertation describes the development and proof-of-concept demonstration of a navigation system that uses only a low-cost inertial measurement unit and a monocular camera. Micro electromechanical inertial measurements units are well suited to small UAV applications and provide measurements of acceleration and angular rate. However, they do not provide information about nearby obstacles (needed for collision avoidance) and their noise and bias characteristics lead to unbounded growth in computed position. A monocular camera can provide bearings to nearby obstacles and landmarks. These bearings can be used both to enable obstacle avoidance and to aid navigation. Presented here is a solution to the problem of estimating vehicle state (position, orientation and velocity) as well as positions of obstacles in the environment using only inertial measurements and bearings to obstacles. This is a highly nonlinear estimation problem, and standard estimation techniques such as the Extended Kalman Filter are prone to divergence in this application. In this dissertation a Sigma Point Kalman Filter is implemented, resulting in an estimator which is able to cope with the significant nonlinearities in the system equations and
Model-based adaptive 3D sonar reconstruction in reverberating environments.
Saucan, Augustin-Alexandru; Sintes, Christophe; Chonavel, Thierry; Caillec, Jean-Marc Le
2015-10-01
In this paper, we propose a novel model-based approach for 3D underwater scene reconstruction, i.e., bathymetry, for side scan sonar arrays in complex and highly reverberating environments like shallow water areas. The presence of multipath echoes and volume reverberation generates false depth estimates. To improve the resulting bathymetry, this paper proposes and develops an adaptive filter, based on several original geometrical models. This multimodel approach makes it possible to track and separate the direction of arrival trajectories of multiple echoes impinging the array. Echo tracking is perceived as a model-based processing stage, incorporating prior information on the temporal evolution of echoes in order to reject cluttered observations generated by interfering echoes. The results of the proposed filter on simulated and real sonar data showcase the clutter-free and regularized bathymetric reconstruction. Model validation is carried out with goodness of fit tests, and demonstrates the importance of model-based processing for bathymetry reconstruction.
NASA Astrophysics Data System (ADS)
Olivares, A.; Górriz, J. M.; Ramírez, J.; Olivares, G.
2011-02-01
Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed.
Wiklund, Urban; Karlsson, Marcus; Ostlund, Nils; Berglin, Lena; Lindecrantz, Kaj; Karlsson, Stefan; Sandsjö, Leif
2007-06-01
Intermittent disturbances are common in ECG signals recorded with smart clothing: this is mainly because of displacement of the electrodes over the skin. We evaluated a novel adaptive method for spatio-temporal filtering for heartbeat detection in noisy multi-channel ECGs including short signal interruptions in single channels. Using multi-channel database recordings (12-channel ECGs from 10 healthy subjects), the results showed that multi-channel spatio-temporal filtering outperformed regular independent component analysis. We also recorded seven channels of ECG using a T-shirt with textile electrodes. Ten healthy subjects performed different sequences during a 10-min recording: resting, standing, flexing breast muscles, walking and pushups. Using adaptive multi-channel filtering, the sensitivity and precision was above 97% in nine subjects. Adaptive multi-channel spatio-temporal filtering can be used to detect heartbeats in ECGs with high noise levels. One application is heartbeat detection in noisy ECG recordings obtained by integrated textile electrodes in smart clothing.
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
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.
Facilitating Joint Chaos and Fractal Analysis of Biosignals through Nonlinear Adaptive Filtering
Gao, Jianbo; Hu, Jing; Tung, Wen-wen
2011-01-01
Background Chaos and random fractal theories are among the most important for fully characterizing nonlinear dynamics of complicated multiscale biosignals. Chaos analysis requires that signals be relatively noise-free and stationary, while fractal analysis demands signals to be non-rhythmic and scale-free. Methodology/Principal Findings To facilitate joint chaos and fractal analysis of biosignals, we present an adaptive algorithm, which: (1) can readily remove nonstationarities from the signal, (2) can more effectively reduce noise in the signals than linear filters, wavelet denoising, and chaos-based noise reduction techniques; (3) can readily decompose a multiscale biosignal into a series of intrinsically bandlimited functions; and (4) offers a new formulation of fractal and multifractal analysis that is better than existing methods when a biosignal contains a strong oscillatory component. Conclusions The presented approach is a valuable, versatile tool for the analysis of various types of biological signals. Its effectiveness is demonstrated by offering new important insights into brainwave dynamics and the very high accuracy in automatically detecting epileptic seizures from EEG signals. PMID:21915312
Fault detection method for railway wheel flat using an adaptive multiscale morphological filter
NASA Astrophysics Data System (ADS)
Li, Yifan; Zuo, Ming J.; Lin, Jianhui; Liu, Jianxin
2017-02-01
This study explores the capacity of the morphology analysis for railway wheel flat fault detection. A dynamic model of vehicle systems with 56 degrees of freedom was set up along with a wheel flat model to calculate the dynamic responses of axle box. The vehicle axle box vibration signal is complicated because it not only contains the information of wheel defect, but also includes track condition information. Thus, how to extract the influential features of wheels from strong background noise effectively is a typical key issue for railway wheel fault detection. In this paper, an algorithm for adaptive multiscale morphological filtering (AMMF) was proposed, and its effect was evaluated by a simulated signal. And then this algorithm was employed to study the axle box vibration caused by wheel flats, as well as the influence of track irregularity and vehicle running speed on diagnosis results. Finally, the effectiveness of the proposed method was verified by bench testing. Research results demonstrate that the AMMF extracts the influential characteristic of axle box vibration signals effectively and can diagnose wheel flat faults in real time.
Robust optical flow using adaptive Lorentzian filter for image reconstruction under noisy condition
NASA Astrophysics Data System (ADS)
Kesrarat, Darun; Patanavijit, Vorapoj
2017-02-01
In optical flow for motion allocation, the efficient result in Motion Vector (MV) is an important issue. Several noisy conditions may cause the unreliable result in optical flow algorithms. We discover that many classical optical flows algorithms perform better result under noisy condition when combined with modern optimized model. This paper introduces effective robust models of optical flow by using Robust high reliability spatial based optical flow algorithms using the adaptive Lorentzian norm influence function in computation on simple spatial temporal optical flows algorithm. Experiment on our proposed models confirm better noise tolerance in optical flow's MV under noisy condition when they are applied over simple spatial temporal optical flow algorithms as a filtering model in simple frame-to-frame correlation technique. We illustrate the performance of our models by performing an experiment on several typical sequences with differences in movement speed of foreground and background where the experiment sequences are contaminated by the additive white Gaussian noise (AWGN) at different noise decibels (dB). This paper shows very high effectiveness of noise tolerance models that they are indicated by peak signal to noise ratio (PSNR).
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-08
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.
Robust Scale Adaptive Tracking by Combining Correlation Filters with Sequential Monte Carlo
Ma, Junkai; Luo, Haibo; Hui, Bin; Chang, Zheng
2017-01-01
A robust and efficient object tracking algorithm is required in a variety of computer vision applications. Although various modern trackers have impressive performance, some challenges such as occlusion and target scale variation are still intractable, especially in the complex scenarios. This paper proposes a robust scale adaptive tracking algorithm to predict target scale by a sequential Monte Carlo method and determine the target location by the correlation filter simultaneously. By analyzing the response map of the target region, the completeness of the target can be measured by the peak-to-sidelobe rate (PSR), i.e., the lower the PSR, the more likely the target is being occluded. A strict template update strategy is designed to accommodate the appearance change and avoid template corruption. If the occlusion occurs, a retained scheme is allowed and the tracker refrains from drifting away. Additionally, the feature integration is incorporated to guarantee the robustness of the proposed approach. The experimental results show that our method outperforms other state-of-the-art trackers in terms of both the distance precision and overlap precision on the publicly available TB-50 dataset. PMID:28273840
Array Processing for Radar Clutter Reduction and Imaging of Ice-Bed Interface
NASA Astrophysics Data System (ADS)
Gogineni, P.; Leuschen, C.; Li, J.; Hoch, A.; Rodriguez-Morales, F.; Ledford, J.; Jezek, K.
2007-12-01
A major challenge in sounding of fast-flowing glaciers in Greenland and Antarctica is surface clutter, which masks weak returns from the ice-bed interface. The surface clutter is also a major problem in sounding and imaging sub-surface interfaces on Mars and other planets. We successfully applied array-processing techniques to reduce clutter and image ice-bed interfaces of polar ice sheets. These techniques and tools have potential applications to planetary observations. We developed a radar with array-processing capability to measure thickness of fast-flowing outlet glaciers and image the ice-bed interface. The radar operates over the frequency range from 140 to 160 MHz with about an 800- Watt peak transmit power with transmit and receive antenna arrays. The radar is designed such that pulse width and duration are programmable. The transmit-antenna array is fed with a beamshaping network to obtain low sidelobes. We designed the receiver such that it can process and digitize signals for each element of an eight- channel array. We collected data over several fast-flowing glaciers using a five-element antenna array, limited by available hardpoints to mount antennas, on a Twin Otter aircraft during the 2006 field season and a four-element array on a NASA P-3 aircraft during the 2007 field season. We used both adaptive and non-adaptive signal-processing algorithms to reduce clutter. We collected data over the Jacobshavn Isbrae and other fast-flowing outlet glaciers, and successfully measured the ice thickness and imaged the ice-bed interface. In this paper, we will provide a brief description of the radar, discuss clutter-reduction algorithms, present sample results, and discuss the application of these techniques to planetary observations.
NASA Astrophysics Data System (ADS)
Wang, Xudong; Syrmos, Vassilis L.
2004-07-01
In this paper, an adaptive reconfigurable control system based on extended Kalman filter approach and eigenstructure assignments is proposed. System identification is carried out using an extended Kalman filter (EKF) approach. An eigenstructure assignment (EA) technique is applied for reconfigurable feedback control law design to recover the system dynamic performance. The reconfigurable feedforward controllers are designed to achieve the steady-state tracking using input weighting approach. The proposed scheme can identify not only actuator and sensor variations, but also changes in the system structures using the extended Kalman filtering method. The overall design is robust with respect to uncertainties in the state-space matrices of the reconfigured system. To illustrate the effectiveness of the proposed reconfigurable control system design technique, an aircraft longitudinal vertical takeoff and landing (VTOL) control system is used to demonstrate the reconfiguration procedure.
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.
NASA Astrophysics Data System (ADS)
Ushaq, Muhammad; Fang, Jiancheng
2013-10-01
Integrated navigation systems for various applications, generally employs the centralized Kalman filter (CKF) wherein all measured sensor data are communicated to a single central Kalman filter. The advantage of CKF is that there is a minimal loss of information and high precision under benign conditions. But CKF may suffer computational overloading, and poor fault tolerance. The alternative is the federated Kalman filter (FKF) wherein the local estimates can deliver optimal or suboptimal state estimate as per certain information fusion criterion. FKF has enhanced throughput and multiple level fault detection capability. The Standard CKF or FKF require that the system noise and the measurement noise are zero-mean and Gaussian. Moreover it is assumed that covariance of system and measurement noises remain constant. But if the theoretical and actual statistical features employed in Kalman filter are not compatible, the Kalman filter does not render satisfactory solutions and divergence problems also occur. To resolve such problems, in this paper, an adaptive Kalman filter scheme strengthened with fuzzy inference system (FIS) is employed to adapt the statistical features of contributing sensors, online, in the light of real system dynamics and varying measurement noises. The excessive faults are detected and isolated by employing Chi Square test method. As a case study, the presented scheme has been implemented on Strapdown Inertial Navigation System (SINS) integrated with the Celestial Navigation System (CNS), GPS and Doppler radar using FKF. Collectively the overall system can be termed as SINS/CNS/GPS/Doppler integrated navigation system. The simulation results have validated the effectiveness of the presented scheme with significantly enhanced precision, reliability and fault tolerance. Effectiveness of the scheme has been tested against simulated abnormal errors/noises during different time segments of flight. It is believed that the presented scheme can be
NASA Astrophysics Data System (ADS)
Deng, Feiyue; Yang, Shaopu; Tang, Guiji; Hao, Rujiang; Zhang, Mingliang
2017-04-01
Wheel bearings are essential mechanical components of trains, and fault detection of the wheel bearing is of great significant to avoid economic loss and casualty effectively. However, considering the operating conditions, detection and extraction of the fault features hidden in the heavy noise of the vibration signal have become a challenging task. Therefore, a novel method called adaptive multi-scale AVG-Hat morphology filter (MF) is proposed to solve it. The morphology AVG-Hat operator not only can suppress the interference of the strong background noise greatly, but also enhance the ability of extracting fault features. The improved envelope spectrum sparsity (IESS), as a new evaluation index, is proposed to select the optimal filtering signal processed by the multi-scale AVG-Hat MF. It can present a comprehensive evaluation about the intensity of fault impulse to the background noise. The weighted coefficients of the different scale structural elements (SEs) in the multi-scale MF are adaptively determined by the particle swarm optimization (PSO) algorithm. The effectiveness of the method is validated by analyzing the real wheel bearing fault vibration signal (e.g. outer race fault, inner race fault and rolling element fault). The results show that the proposed method could improve the performance in the extraction of fault features effectively compared with the multi-scale combined morphological filter (CMF) and multi-scale morphology gradient filter (MGF) methods.
Liu, Zong-xiang; Wu, De-hui; Xie, Wei-xin; Li, Liang-qun
2017-01-01
Tracking the target that maneuvers at a variable turn rate is a challenging problem. The traditional solution for this problem is the use of the switching multiple models technique, which includes several dynamic models with different turn rates for matching the motion mode of the target at each point in time. However, the actual motion mode of a target at any time may be different from all of the dynamic models, because these models are usually limited. To address this problem, we establish a formula for estimating the turn rate of a maneuvering target. By applying the estimation method of the turn rate to the multi-target Bayes (MB) filter, we develop a MB filter with an adaptive estimation of the turn rate, in order to track multiple maneuvering targets. Simulation results indicate that the MB filter with an adaptive estimation of the turn rate, is better than the existing filter at tracking the target that maneuvers at a variable turn rate. PMID:28212291
NASA Astrophysics Data System (ADS)
Piretzidis, Dimitrios; Sideris, Michael G.
2017-03-01
Filtering and signal processing techniques have been widely used in the processing of satellite gravity observations to reduce measurement noise and correlation errors. The parameters and types of filters used depend on the statistical and spectral properties of the signal under investigation. Filtering is usually applied in a non-real-time environment. The present work focuses on the implementation of an adaptive filtering technique to process satellite gravity gradiometry data for gravity field modeling. Adaptive filtering algorithms are commonly used in communication systems, noise and echo cancellation, and biomedical applications. Two independent studies have been performed to introduce adaptive signal processing techniques and test the performance of the least mean-squared (LMS) adaptive algorithm for filtering satellite measurements obtained by the gravity field and steady-state ocean circulation explorer (GOCE) mission. In the first study, a Monte Carlo simulation is performed in order to gain insights about the implementation of the LMS algorithm on data with spectral behavior close to that of real GOCE data. In the second study, the LMS algorithm is implemented on real GOCE data. Experiments are also performed to determine suitable filtering parameters. Only the four accurate components of the full GOCE gravity gradient tensor of the disturbing potential are used. The characteristics of the filtered gravity gradients are examined in the time and spectral domain. The obtained filtered GOCE gravity gradients show an agreement of 63-84 mEötvös (depending on the gravity gradient component), in terms of RMS error, when compared to the gravity gradients derived from the EGM2008 geopotential model. Spectral-domain analysis of the filtered gradients shows that the adaptive filters slightly suppress frequencies in the bandwidth of approximately 10-30 mHz. The limitations of the adaptive LMS algorithm are also discussed. The tested filtering algorithm can be
NASA Astrophysics Data System (ADS)
Pan, M.-Ch.; Chu, W.-Ch.; Le, Duc-Do
2016-12-01
The paper presents an alternative Vold-Kalman filter order tracking (VKF_OT) method, i.e. adaptive angular-velocity VKF_OT technique, to extract and characterize order components in an adaptive manner for the condition monitoring and fault diagnosis of rotary machinery. The order/spectral waveforms to be tracked can be recursively solved by using Kalman filter based on the one-step state prediction. The paper comprises theoretical derivation of computation scheme, numerical implementation, and parameter investigation. Comparisons of the adaptive VKF_OT scheme with two other ones are performed through processing synthetic signals of designated order components. Processing parameters such as the weighting factor and the correlation matrix of process noise, and data conditions like the sampling frequency, which influence tracking behavior, are explored. The merits such as adaptive processing nature and computation efficiency brought by the proposed scheme are addressed although the computation was performed in off-line conditions. The proposed scheme can simultaneously extract multiple spectral components, and effectively decouple close and crossing orders associated with multi-axial reference rotating speeds.
Application of phase coherent transform to cloud clutter suppression
Ng, L.C.
1994-11-15
This paper describes a tracking algorithm using frame-to-frame correlation with frequency domain clutter suppression. Clutter suppression was mechanized via a `Phase Coherent Transform` (PCT) approach. This approach was applied to explore the feasibility of tracking a post-boost rocket from a low earth orbit satellite with real cloud background data. Simulation results show that the PCT/correlation tracking algorithm can perform satisfactorily at signal-to-clutter ratio (SCR) as low as 5 or 7 dB.
NASA Astrophysics Data System (ADS)
Yu, Lifeng; Manduca, Armando; Jacobsen, Megan; Trzasko, Joshua D.; Fletcher, Joel G.; DeLone, David R.; McCollough, Cynthia H.
2010-04-01
We have recently developed a locally-adaptive method for noise control in CT based upon bilateral filtering. Different from the previous adaptive filters, which were locally adaptive by adjusting the filter strength according to local photon statistics, our use of bilateral filtering in projection data incorporates a practical CT noise model and takes into account the local structural characteristics, and thus can preserve edge information in the projection data and maintain the spatial resolution. Despite the incorporation of the CT noise model and local structural characteristics in the bilateral filtering, the noise-resolution properties of the filtered image are still highly dependent on predefined parameters that control the weighting factors in the bilateral filtering. An inappropriate selection of these parameters may result in a loss of spatial resolution or an insufficient reduction of noise. In this work, we employed an adaptive strategy to modulate the bilateral filtering strength according to the noise-equivalent photon numbers determined from each projection measurement. We applied the proposed technique to head/neck angiographic CT exams, which had highly non-uniform attenuation levels during the scan. The results demonstrated that the technique can effectively reduce the noise and streaking artifacts caused by high attenuation, while maintaining the reconstruction accuracy in less attenuating regions.
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.
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
A Front End Filter Subsystem for an Adaptive Radar Signal Processor
1991-07-12
Miscellaneous Front End Module Functions 39 3. PROGRAMMING THE FRONT END SUBSYSTEM 47 3.1 Configuring the FIR Filters 47 3.2 The Discrete Control Register...Front end filter address definition. 48 24 Discrete Control Register address definition. 56 25 Beamformer dual-port RAM address definition. 58 ix LIST...Front End Module Identification Bits 41 8 Decoding the A100 Select Field 41 9 Front End Module Memory Map 43 10 Format of the Discrete Control Register 44
NASA Astrophysics Data System (ADS)
Liu, Tong; Min, Rui; Pi, Yiming; Long, Keyu; Huang, Zhongtao
2016-12-01
In this study, to detect person-borne concealed threats in range profiles under the circumstance of unknown clutter, we propose a binary integration nonparametric detection method based on the generalized sign (GS) detector for range-spread targets in a distributed terahertz radar network (DTRN). In the detection, the length of range-spread targets and the number of dominant scatterers on range-spread targets are considered and adaptively estimated. Furthermore, the GS detection method is applied to maintain a constant false alarm rate (CFAR) under the circumstance of unknown clutter. The detection performance of the proposed method for single terahertz radar and DTRN are both examined with the data synthesized by real range-spread targets data and real clutter data. Experimental results show that the proposed method is effective, and for a given false alarm probability, the DTRN exhibits better detection performance than the single terahertz radar.
NASA Astrophysics Data System (ADS)
Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.
2012-04-01
Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.
An Adaptive Non-Local-Means Filter for Real-Time MR-Thermometry.
Zachiu, Cornel; Ries, Mario; Moonen, Chrit; de Senneville, Baudouin Denis
2017-04-01
Proton resonance frequency shift-based magnetic resonance thermometry is a currently used technique for monitoring temperature during targeted thermal therapies. However, in order to provide temperature updates with very short latency times, fast MR acquisition schemes are usually employed, which in turn might lead to noisy temperature measurements. This will, in general, have a direct impact on therapy control and endpoint detection. In this paper, we address this problem through an improved non-local filtering technique applied on the temperature images. Compared with previous non-local filtering methods, the proposed approach considers not only spatial information but also exploits temporal redundancies. The method is fully automatic and designed to improve the precision of the temperature measurements while at the same time maintaining output accuracy. In addition, the implementation was optimized in order to ensure real-time availability of the temperature measurements while having a minimal impact on latency. The method was validated in three complementary experiments: a simulation, an ex-vivo and an in-vivo study. Compared to the original non-local means filter and two other previously employed temperature filtering methods, the proposed approach shows considerable improvement in both accuracy and precision of the filtered data. Together with the low computational demands of the numerical scheme, the proposed filtering technique shows great potential for improving temperature measurements during real-time MR thermometry dedicated to targeted thermal therapies.
Wereszczak, Andrew; Jadaan, Osama; Modugno, Max; ...
2017-01-18
In this paper, the apparent fracture toughness of a porous cordierite ceramic was estimated using a large specimen whose geometry was inspired by the ASTM-C1421-standardized chevron-notch beam. In this paper, using the same combination of experiment and analysis used to develop the standardized chevron-notch test for small, monolithic ceramic bend bars, an apparent fracture toughness of 0.6 and 0.9 MPa√m were estimated for an unaged and aged cordierite diesel particulate filter structure, respectively. Finally, the effectiveness and simplicity of this adapted specimen geometry and test method lends itself to the evaluation of (macroscopic) apparent fracture toughness of an entire porous-ceramic,more » diesel particulate filter structure.« less
NASA Astrophysics Data System (ADS)
Zeng, Bangze; Zhu, Youpan; Li, Zemin; Hu, Dechao; Luo, Lin; Zhao, Deli; Huang, Juan
2014-11-01
Duo to infrared image with low contrast, big noise and unclear visual effect, target is very difficult to observed and identified. This paper presents an improved infrared image detail enhancement algorithm based on adaptive histogram statistical stretching and gradient filtering (AHSS-GF). Based on the fact that the human eyes are very sensitive to the edges and lines, the author proposed to extract the details and textures by using the gradient filtering. New histogram could be acquired by calculating the sum of original histogram based on fixed window. With the minimum value for cut-off point, author carried on histogram statistical stretching. After the proper weights given to the details and background, the detail-enhanced results could be acquired finally. The results indicate image contrast could be improved and the details and textures could be enhanced effectively as well.
NASA Astrophysics Data System (ADS)
Jiang, Xiao-Yu; Zong, Yan-Tao; Wang, Xi; Chen, Zhuo; Liu, Zhong-Xuan
2010-11-01
MEMS gyro is used in inertial measuring fields more and more widely, but random drift is considered as an important error restricting the precision of it. Establishing the proper models closed to actual state of movement and random drift, and designing a kind of effective filter are available to enhance the precision of the MEMS gyro. The dynamic model of angle movement is studied, the ARMA model describing random drift is established based on time series analysis method, and a modified self-adapted Kalman filter is designed for the signal processing. Finally, the random drift is distinguished and analyzed clearly by Allan variance. It is included that the above method can effectively eliminate the random drift and improve the precision of MEMS gyro.
Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng
2017-01-01
Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.
Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng
2017-01-01
Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness. PMID:28296902
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.
ERIC Educational Resources Information Center
Farrell, Lindsey M.; Blanchet, Paul G.; Tillery, Kim L.
2015-01-01
Background: Previous research suggests a negative stereotype toward people with fluency disorders (i.e. stuttering and/or cluttering), although recent findings suggest that exposure to an actual person who stutters (e.g. a live or video presentation) leads to more positive perceptions of some personality traits. However, there is a paucity of…
Polarimetric clutter modeling: Theory and application
NASA Technical Reports Server (NTRS)
Kong, J. A.; Lin, F. C.; Borgeaud, M.; Yueh, H. A.; Swartz, A. A.; Lim, H. H.; Shim, R. T.; Novak, L. M.
1988-01-01
The two-layer anisotropic random medium model is used to investigate fully polarimetric scattering properties of earth terrain media. The polarization covariance matrices for the untilted and tilted uniaxial random medium are evaluated using the strong fluctuation theory and distorted Born approximation. In order to account for the azimuthal randomness in the growth direction of leaves in tree and grass fields, an averaging scheme over the azimuthal direction is also applied. It is found that characteristics of terrain clutter can be identified through the analysis of each element of the covariance matrix. Theoretical results are illustrated by the comparison with experimental data provided by MIT Lincoln Laboratory for tree and grass fields.
Okumura, Miwa; Ota, Takamasa; Kainuma, Kazuhisa; Sayre, James W.; McNitt-Gray, Michael; Katada, Kazuhiro
2008-01-01
Objective. For the multislice CT (MSCT) systems with a larger number of detector rows, it is essential to employ dose-reduction techniques. As reported in previous studies, edge-preserving adaptive image filters, which selectively eliminate only the noise elements that are increased when the radiation dose is reduced without affecting the sharpness of images, have been developed. In the present study, we employed receiver operating characteristic (ROC) analysis to assess the effects of the quantum denoising system (QDS), which is an edge-preserving adaptive filter that we have developed, on low-contrast resolution, and to evaluate to what degree the radiation dose can be reduced while maintaining acceptable low-contrast resolution. Materials and Methods. The low-contrast phantoms (Catphan 412) were scanned at various tube current settings, and ROC analysis was then performed for the groups of images obtained with/without the use of QDS at each tube current to determine whether or not a target could be identified. The tube current settings for which the area under the ROC curve (Az value) was approximately 0.7 were determined for both groups of images with/without the use of QDS. Then, the radiation dose reduction ratio when QDS was used was calculated by converting the determined tube current to the radiation dose. Results. The use of the QDS edge-preserving adaptive image filter allowed the radiation dose to be reduced by up to 38%. Conclusion. The QDS was found to be useful for reducing the radiation dose without affecting the low-contrast resolution in MSCT studies. PMID:19043565
Karmas, E; Turk, K
1975-07-18
A gravimetric adaptation of the filter paper press method for the determination of water-binding capacity in meat was developed and its sensitivity was compared to that of the conventional planimetric technique of the method. Both the gravimetric and planimetric techniques were applied to samples of cooked fish treated with various water binders. The mean results of the samples were grouped and compared using an analysis of variance. In all comparisons, the gravimetric data produced higher F-values than did the planimetric data for the same samples. This indicated greater senstivity for the gravimetric technique.
Capturing dynamics on multiple timescales: a hybrid approach for cluttered electromagnetic data
Pawley, Norma H; Myers, Kary L; Galbraith, John M; Brumby, Steven P
2009-01-01
Many problems in electromagnetic signal analysis exhibit dynamics on a wide range of time scales against nonstationary clutter and noise. We consider a problem in which the relevant time scales can range from nanoseconds to hours or days (12 or 13 orders of magnitude). We present a hybrid algorithm currently designed to capture the dynamic behavior at scales from nanoseconds to milliseconds (6 orders of magnitude) while remaining robust to clutter and noise. We draw from techniques of adaptive feature extraction, statistical machine learning, and discrete process modeling and present results on a simulated multimode problem. Our goals are to find a representation of the signal that allows us to identify which pulses were produced by a target emitter and to determine the operational mode of the target.
NASA Astrophysics Data System (ADS)
Chun, Joohwan; Kailath, Thomas; Son, Jung-Young
2000-03-01
The systems such as infrared search and trackers (IRST's), forward looking infrared systems (FLIR's), sonars, and 2-D radars consist of two functional blocks; a detection unit and a tracker. The detection unit which has matched filters followed by a threshold device generates a set of multiple two-dimensional points or detects at every sampling time. For a radar or sonar, each generated detect has polar coordinates, the range and azimuth while an IRST or FLIR produces detects in cartesian coordinates. In practice, the detection unit always has a non-zero false alarm rate, and therefore, the set of detects usually contains clutter points as well as the target. In this paper, we shall present a new target tracking algorithm for clutter environment applicable to a wide range of tracking systems. More specifically, the two-dimensional tracking problem in clutter environment is solved in the discrete-time Bayes optimal (nonlinear, and non-Gaussian) estimation framework. The proposed method recursively finds the entire probability density functions of the target position and velocity. With our approach, the nonlinear estimation problem is converted into simpler linear convolution operations, which can efficiently be implemented with optical devices such as lenses, CCD's (charge coupled devices), SLM's (spatial light modulators) and films.
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.
Specification for a Standard Radar Sea Clutter Model
1990-09-01
7, •.i;j i :"Y Technical Document 1917 00 September 1990 0 N• Specification for a Standard Radar Sea I Clutter Model Richard A. Paulus .OTIC SELECTE...2 2.2 Radar Param eters ............................................................. 3 2.3 O utput...4 3.1 Grazing Angle at Sea Surface ................................................... 4 3.2 Radar Clutter Cross Section
Optimal-adaptive filters for modelling spectral shape, site amplification, and source scaling
Safak, Erdal
1989-01-01
This paper introduces some applications of optimal filtering techniques to earthquake engineering by using the so-called ARMAX models. Three applications are presented: (a) spectral modelling of ground accelerations, (b) site amplification (i.e., the relationship between two records obtained at different sites during an earthquake), and (c) source scaling (i.e., the relationship between two records obtained at a site during two different earthquakes). A numerical example for each application is presented by using recorded ground motions. The results show that the optimal filtering techniques provide elegant solutions to above problems, and can be a useful tool in earthquake engineering.
The remarkable success of adaptive cosine estimator in hyperspectral target detection
NASA Astrophysics Data System (ADS)
Manolakis, D.; Pieper, M.; Truslow, E.; Cooley, T.; Brueggeman, M.; Lipson, S.
2013-05-01
A challenging problem of major importance in hyperspectral imaging applications is the detection of subpixel targets of military and civilian interest. The background clutter surrounding the target, acts as an interference source that simultaneously distorts the target spectrum and reduces its strength. Two additional limiting factors are the spectral variability of the background clutter and the spectral variability of the target. Since a result in applied statistics is only as reliable as the assumptions from which it is derived, it is important to investigate whether the basic assumptions used for the derivation of matched filter and adaptive cosine estimator algorithms are a reasonable description of the physical situation. Careful examination of the linear signal model used to derive these algorithms and the replacement signal model, which is a more realistic model for subpixel targets, reveals a serious discrepancy between modeling assumptions and the physical world. Despite this discrepancy and additional mismatches between assumed and actual signal and clutter models, the adaptive cosine estimator shows an amazing effectiveness in practical target detection applications. The objective of this paper is an attempt to explain this unbelievable effectiveness using a combination of classical statistical detection theory, geometrical interpretations, and a novel realistic performance prediction model for the adaptive cosine estimator.
Gullman, L.S.; Low, T.P.
1990-08-01
An adapter has been developed to allow an air filter to be easily attached and removed from the exhaust port of the M43A1 Chemical Agent Detector. The adapter is to replace a threaded coupling presently in use, provide reliable filter retention, and ease of attachment and removal. The adapter was designed for low-cost production in quantities of 100,000. Three design alternatives are presented. Production, testing, and evaluation of each design is described. An ultrasonically assembled two-part design with an internal Nitrile ring is selected. The components are injection molded of Acetal Resin.
Distributed Fuzzy CFAR Detection for Weibull Clutter
NASA Astrophysics Data System (ADS)
Zaimbashi, Amir; Taban, Mohammad Reza; Nayebi, Mohammad Mehdi
In Distributed detection systems, restricting the output of the local decision to one bit certainly implies a substantial information loss. In this paper, we consider the fuzzy detection, which uses a function called membership function for mapping the observation space of each local detector to a value between 0 and 1, indicating the degree of assurance about presence or absence of a signal. In this case, we examine the problem of distributed Maximum Likelihood (ML) and Order Statistic (OS) constant false alarm rate (CFAR) detections using fuzzy fusion rules such as “Algebraic Product” (AP), “Algebraic Sum” (AS), “Union” (Un) and “Intersection” (IS) in the fusion centre. For the Weibull clutter, the expression of the membership function based on the ML or OS CFAR processors in the local detectors is also obtained. For comparison, we consider a binary distributed detector, which uses the Maximum Likelihood and Algebraic Product (MLAP) or Order Statistic and Algebraic Product (OSAP) CFAR processors as the local detectors. In homogenous and non homogenous situations, multiple targets or clutter edge, the performances of the fuzzy and binary distributed detectors are analyzed and compared. The simulation results indicate the superior and robust performance of the distributed systems using fuzzy detection in the homogenous and non homogenous situations.
NASA Astrophysics Data System (ADS)
Steeb, P.; Krause, S.; Linke, P.; Hensen, C.; Dale, A. W.; Nuzzo, M.; Treude, T.
2015-11-01
Large amounts of methane are delivered by fluids through the erosive forearc of the convergent margin offshore of Costa Rica and lead to the formation of cold seeps at the sediment surface. Besides mud extrusion, numerous cold seeps are created by landslides induced by seamount subduction or fluid migration along major faults. Most of the dissolved methane migrating through the sediments of cold seeps is oxidized within the benthic microbial methane filter by anaerobic oxidation of methane (AOM). Measurements of AOM and sulfate reduction as well as numerical modeling of porewater profiles revealed a highly active and efficient benthic methane filter at the Quepos Slide site, a landslide on the continental slope between the Nicoya and Osa Peninsula. Integrated areal rates of AOM ranged from 12.9 ± 6.0 to 45.2 ± 11.5 mmol m-2 d-1, with only 1 to 2.5 % of the upward methane flux being released into the water column. Additionally, two parallel sediment cores from Quepos Slide were used for in vitro experiments in a recently developed sediment-flow-through (SLOT) system to simulate an increased fluid and methane flux from the bottom of the sediment core. The benthic methane filter revealed a high adaptability whereby the methane oxidation efficiency responded to the increased fluid flow within ca. 170 d. To our knowledge, this study provides the first estimation of the natural biogeochemical response of seep sediments to changes in fluid flow.
An adaptive non-local means filter for denoising live-cell images and improving particle detection.
Yang, Lei; Parton, Richard; Ball, Graeme; Qiu, Zhen; Greenaway, Alan H; Davis, Ilan; Lu, Weiping
2010-12-01
Fluorescence imaging of dynamical processes in live cells often results in a low signal-to-noise ratio. We present a novel feature-preserving non-local means approach to denoise such images to improve feature recovery and particle detection. The commonly used non-local means filter is not optimal for noisy biological images containing small features of interest because image noise prevents accurate determination of the correct coefficients for averaging, leading to over-smoothing and other artifacts. Our adaptive method addresses this problem by constructing a particle feature probability image, which is based on Haar-like feature extraction. The particle probability image is then used to improve the estimation of the correct coefficients for averaging. We show that this filter achieves higher peak signal-to-noise ratio in denoised images and has a greater capability in identifying weak particles when applied to synthetic data. We have applied this approach to live-cell images resulting in enhanced detection of end-binding-protein 1 foci on dynamically extending microtubules in photo-sensitive Drosophila tissues. We show that our feature-preserving non-local means filter can reduce the threshold of imaging conditions required to obtain meaningful data.
NASA Astrophysics Data System (ADS)
Zhang, Weige; Shi, Wei; Ma, Zeyu
2015-09-01
Accurate estimations of battery energy and available power capability are of great of importance for realizing an efficient and reliable operation of electric vehicles. To improve the estimation accuracy and reliability for battery state of energy and power capability, a novel model-based joint estimation approach has been proposed against uncertain external operating conditions and internal degradation status of battery cells. Firstly, it proposes a three-dimensional response surface open circuit voltage model to calibrate the estimation inaccuracies of battery state of energy. Secondly, the adaptive unscented Kalman filter (AUKF) is employed to develop a novel model-based joint state estimator for battery state of energy and power capability. The AUKF algorithm utilizes the well-known features of the Kalman filter but employs the method of unscented transform (UT) and adaptive error covariance matching technology to improve the state estimation accuracy. Thirdly, the proposed joint estimator has been verified by a LiFePO4 lithium-ion battery cell under different operating temperatures and aging levels. The result indicates that the estimation errors of battery voltage and state-of-energy are less than 2% even if given a large erroneous initial value, which makes the state of available power capability predict more accurate and reliable for the electric vehicles application.
NASA Astrophysics Data System (ADS)
Zheng, Shiqiang; Feng, Rui
2016-03-01
This paper introduces a feedforward control strategy combined with a novel adaptive notch filter to solve the problem of rotor imbalance in high-speed Magnetically Suspended Centrifugal Compressors (MSCCs). Unbalance vibration force of rotor in MSCC is mainly composed of current stiffness force and displacement stiffness force. In this paper, the mathematical model of the unbalance vibration with the proportional-integral-derivative (PID) control laws is presented. In order to reduce the unbalance vibration, a novel adaptive notch filter is proposed to identify the synchronous frequency displacement of the rotor as a compensation signal to eliminate the current stiffness force. In addition, a feedforward channel from position component to control output is introduced to compensate displacement stiffness force to achieve a better performance. A simplified inverse model of power amplifier is included in the feedforward channel to reject the degrade performance caused by its low-pass characteristic. Simulation and experimental results on a MSCC demonstrate a significant effect on the synchronous vibration suppression of the magnetically suspended rotor at a high speed.
Wang, L; Zand, M R; Söderberg, P G
1994-08-01
The effect of red pass goggles (cut off wavelength = 650 nm) imitating dark adaptation on measurement of visual readaptation after flash exposure was investigated in humans. The results showed that there is no statistically significant difference between visual readaptation time measured with ordinary dark adaptation and that with goggles for adaptation. No statistically significant difference was found between females and males. It is suggested that red pass goggles can be practicably used to simulate dark adaptation in measuring visual readaptation time. Visual readaptation time was measured as the interval between the triggering of a green flash and the reappearance of optokinetic nystagmus. Optokinetic nystagmus was induced by a moving vertical grating and recorded by DC EOG.
Noise adaptive fading Kalman filter for free-space laser communication beacon tracking.
Li, Lixing; Huang, Yongmei; Wang, Qiang; Yang, Fasheng
2016-10-20
We proposed a prediction algorithm for laser communication pointing, acquisition, and tracking (PAT) subsystems in order to further improve PAT accuracy and reduce the effect of processing delay. In terms of this prediction algorithm, a fading Kalman filter is employed, with the observation noise obtained by the gray value distribution of the laser images. Moreover, to better fit the dynamics of a laser target, the two-stage dynamic model has been chosen as the state transition model for Kalman filtering. In addition, the two-stage dynamic model has been modified by accommodating its form to a change of time lag, thereby compensating the effect of time delay. A series of horizontal path (17 km) experiments under different atmospheric conditions were conducted in the fields. According to the experimental results, the algorithm we proposed could effectively reduce the tracking error and improve pointing accuracy.
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.
Multi-Target Tracking Using an Improved Gaussian Mixture CPHD Filter
Si, Weijian; Wang, Liwei; Qu, Zhiyu
2016-01-01
The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the full multi-target Bayesian filter for tracking multiple targets. However, although the joint propagation of the posterior intensity and cardinality distribution in its recursion allows more reliable estimates of the target number than the PHD filter, the CPHD filter suffers from the spooky effect where there exists arbitrary PHD mass shifting in the presence of missed detections. To address this issue in the Gaussian mixture (GM) implementation of the CPHD filter, this paper presents an improved GM-CPHD filter, which incorporates a weight redistribution scheme into the filtering process to modify the updated weights of the Gaussian components when missed detections occur. In addition, an efficient gating strategy that can adaptively adjust the gate sizes according to the number of missed detections of each Gaussian component is also presented to further improve the computational efficiency of the proposed filter. Simulation results demonstrate that the proposed method offers favorable performance in terms of both estimation accuracy and robustness to clutter and detection uncertainty over the existing methods. PMID:27886106
Clutter in the GMTI range-velocity map.
Doerry, Armin Walter
2009-04-01
Ground Moving Target Indicator (GMTI) radar maps echo data to range and range-rate, which is a function of a moving target's velocity and its position within the antenna beam footprint. Even stationary clutter will exhibit an apparent motion spectrum and can interfere with moving vehicle detections. Consequently it is very important for a radar to understand how stationary clutter maps into radar measurements of range and velocity. This mapping depends on a wide variety of factors, including details of the radar motion, orientation, and the 3-D topography of the clutter.
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.
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.
Zhao, Qinglin; Hu, Bin; Shi, Yujun; Li, Yang; Moore, Philip; Sun, Minghou; Peng, Hong
2014-06-01
Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.
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
Using Morphological Filters for Pupil Detection in Infrared Videos
2010-01-05
detect the subjects’ pupils, with manual parameterization of the filter coefficients. False detections due to background clutter from eyeglasses and...brightness of the pupil images • The subject’s eyeglasses can sometimes block the reflection from the pupils, resulting in a failure to detect the...to remove false detections due to background clutter ( eyeglasses and jewelry) is to compute the image difference between two alternate IR illuminations
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.
Berset, Torfinn; Geng, Di; Romero, Iñaki
2012-01-01
Noise from motion artifacts is currently one of the main challenges in the field of ambulatory ECG recording. To address this problem, we propose the use of two different approaches. First, an adaptive filter with electrode-skin impedance as a reference signal is described. Secondly, a multi-channel ECG algorithm based on Independent Component Analysis is introduced. Both algorithms have been designed and further optimized for real-time work embedded in a dedicated Digital Signal Processor. We show that both algorithms improve the performance of a beat detection algorithm when applied in high noise conditions. In addition, an efficient way of choosing this methods is suggested with the aim of reduce the overall total system power consumption.
Zheng, Dongliang; Da, Feipeng; Kemao, Qian; Seah, Hock Soon
2017-03-06
Phase-shifting profilometry combined with Gray-code patterns projection has been widely used for 3D measurement. In this technique, a phase-shifting algorithm is used to calculate the wrapped phase, and a set of Gray-code binary patterns is used to determine the unwrapped phase. In the real measurement, the captured Gray-code patterns are no longer binary, resulting in phase unwrapping errors at a large number of erroneous pixels. Although this problem has been attended and well resolved by a few methods, it remains challenging when a measured object has step-heights and the captured patterns contain invalid pixels. To effectively remove unwrapping errors and simultaneously preserve step-heights, in this paper, an effective method using an adaptive median filter is proposed. Both simulations and experiments can demonstrate its effectiveness.
Empirical performance of the spectral independent morphological adaptive classifier
NASA Astrophysics Data System (ADS)
Montgomery, Joel B.; Montgomery, Christine T.; Sanderson, Richard B.; McCalmont, John F.
2008-04-01
Effective missile warning and countermeasures continue to be an unfulfilled goal for the Air Force including the wider military and civilian aerospace community. To make the necessary detection and jamming timeframes dictated by today's proliferated missiles and near-term upgraded threats, sensors with required sensitivity, field of regard, and spatial resolution are being pursued in conjunction with advanced processing techniques allowing for detection and discrimination beyond 10 km. The greatest driver of any missile warning system is detection and correct declaration, in which all targets need to be detected with a high confidence and with very few false alarms. Generally, imaging sensors are limited in their detection capability by the presence of heavy background clutter, sun glints, and inherent sensor noise. Many threat environments include false alarm sources like burning fuels, flares, exploding ordinance, and industrial emitters. Spectral discrimination has been shown to be one of the most effective methods of improving the performance of typical missile warning sensors, particularly for heavy clutter situations. Its utility has been demonstrated in the field and on-board multiple aircraft. Utilization of the background and clutter spectral content, coupled with additional spatial and temporal filtering techniques, have yielded robust adaptive real-time algorithms to increase signal-to-clutter ratios against point targets, and thereby to increase detection range. The algorithm outlined is the result of continued work with reported results against visible missile tactical data. The results are summarized and compared in terms of computational cost expected to be implemented on a real-time field-programmable gate array (FPGA) processor.
PMHT Approach for Multi-Target Multi-Sensor Sonar Tracking in Clutter.
Li, Xiaohua; Li, Yaan; Yu, Jing; Chen, Xiao; Dai, Miao
2015-11-06
Multi-sensor sonar tracking has many advantages, such as the potential to reduce the overall measurement uncertainty and the possibility to hide the receiver. However, the use of multi-target multi-sensor sonar tracking is challenging because of the complexity of the underwater environment, especially the low target detection probability and extremely large number of false alarms caused by reverberation. In this work, to solve the problem of multi-target multi-sensor sonar tracking in the presence of clutter, a novel probabilistic multi-hypothesis tracker (PMHT) approach based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) is proposed. The PMHT can efficiently handle the unknown measurements-to-targets and measurements-to-transmitters data association ambiguity. The EKF and UKF are used to deal with the high degree of nonlinearity in the measurement model. The simulation results show that the proposed algorithm can improve the target tracking performance in a cluttered environment greatly, and its computational load is low.
NASA Astrophysics Data System (ADS)
Sbarufatti, Claudio; Corbetta, Matteo; Giglio, Marco; Cadini, Francesco
2017-03-01
Lithium-Ion rechargeable batteries are widespread power sources with applications to consumer electronics, electrical vehicles, unmanned aerial and spatial vehicles, etc. The failure to supply the required power levels may lead to severe safety and economical consequences. Thus, in view of the implementation of adequate maintenance strategies, the development of diagnostic and prognostic tools for monitoring the state of health of the batteries and predicting their remaining useful life is becoming a crucial task. Here, we propose a method for predicting the end of discharge of Li-Ion batteries, which stems from the combination of particle filters with radial basis function neural networks. The major innovation lies in the fact that the radial basis function model is adaptively trained on-line, i.e., its parameters are identified in real time by the particle filter as new observations of the battery terminal voltage become available. By doing so, the prognostic algorithm achieves the flexibility needed to provide sound end-of-discharge time predictions as the charge-discharge cycles progress, even in presence of anomalous behaviors due to failures or unforeseen operating conditions. The method is demonstrated with reference to actual Li-Ion battery discharge data contained in the prognostics data repository of the NASA Ames Research Center database.
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.
Mie Light-Scattering Granulometer with an Adaptive Numerical Filtering Method. II. Experiments.
Hespel, L; Delfour, A; Guillame, B
2001-02-20
A nephelometer is presented that theoretically requires no absolute calibration. This instrument is used for determining the particle-size distribution of various scattering media (aerosols, fogs, rocket exhausts, engine plumes, and the like) from angular static light-scattering measurements. An inverse procedure is used, which consists of a least-squares method and a regularization scheme based on numerical filtering. To retrieve the distribution function one matches the experimental data with theoretical patterns derived from Mie theory. The main principles of the inverse method are briefly presented, and the nephelometer is then described with the associated partial calibration procedure. Finally, the whole granulometer system (inverse method and nephelometer) is validated by comparison of measurements of scattering media with calibrated monodisperse or known size distribution functions.
Adaptive time-domain filtering for real-time spectral discrimination in a Michelson interferometer.
Bhalotra, Sameer R; Kung, Helen L; Jiao, Yang; Miller, David A B
2002-07-01
We present a method of spectral discrimination that employs time-domain processing instead of the typical frequency-domain analysis and implement the method in a Michelson interferometer with a nonlinear mirror scan. The technique yields one analog output value per scan instead of a complete interferogram by directly filtering a measured scan with a reference function in the time domain. Such a procedure drastically reduces data-processing requirements downstream. Additionally, using prerecorded interferograms as references eliminates the need to compensate for scan nonlinearities, which broadens the field of usable components for implementation in miniaturized sensing systems. With our efficient use of known spectral signatures, we demonstrate real-time discrimination of 633- and 663-nm laser sources with a mirror scan length of 1 microm , compared with the Rayleigh criterion of 7 microm.
Limits to Clutter Cancellation in Multi-Aperture GMTI Data
Doerry, Armin W.; Bickel, Douglas L.
2015-03-01
Multi-aperture or multi-subaperture antennas are fundamental to Ground Moving Target Indicator (GMTI) radar systems in order to detect slow-moving targets with Doppler characteristics similar to clutter. Herein we examine the performance of several subaperture architectures for their clutter cancelling performance. Significantly, more antenna phase centers isn’t always better, and in fact is sometimes worse, for detecting targets.
51. WEST ACROSS CLUTTER TO WEST WALL OF WELLSERVICE SHED ...
51. WEST ACROSS CLUTTER TO WEST WALL OF WELL-SERVICE SHED ADDITION ON REAR OF FACTORY BUILDING. AT LOWER RIGHT FOREGROUND IS 1960S PICKUP TRUCK, THE LAST MOTOR VEHICLE USED IN WELL SERVICE BY THE KREGEL WINDMILL COMPANY. MOST OF THE OBJECTS VISIBLE IN THIS VIEW ARE CLUTTER NOT RELATED TO THE WELL SERVICE BUSINESS. - Kregel Windmill Company Factory, 1416 Central Avenue, Nebraska City, Otoe County, NE
Lian, Feng; Zhang, Guang-Hua; Duan, Zhan-Sheng; Han, Chong-Zhao
2016-01-01
The error bound is a typical measure of the limiting performance of all filters for the given sensor measurement setting. This is of practical importance in guiding the design and management of sensors to improve target tracking performance. Within the random finite set (RFS) framework, an error bound for joint detection and estimation (JDE) of multiple targets using a single sensor with clutter and missed detection is developed by using multi-Bernoulli or Poisson approximation to multi-target Bayes recursion. Here, JDE refers to jointly estimating the number and states of targets from a sequence of sensor measurements. In order to obtain the results of this paper, all detectors and estimators are restricted to maximum a posteriori (MAP) detectors and unbiased estimators, and the second-order optimal sub-pattern assignment (OSPA) distance is used to measure the error metric between the true and estimated state sets. The simulation results show that clutter density and detection probability have significant impact on the error bound, and the effectiveness of the proposed bound is verified by indicating the performance limitations of the single-sensor probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters for various clutter densities and detection probabilities. PMID:26828499
Statistical model of clutter suppression in tissue harmonic imaging.
Yan, Xiang; Hamilton, Mark F
2011-03-01
A statistical model is developed for the suppression of clutter in tissue harmonic imaging (THI). Tissue heterogeneity is modeled as a random phase screen that is characterized by its correlation length and variance. With the autocorrelation function taken to be Gaussian and for small variance, statistical solutions are derived for the mean intensities at the fundamental and second-harmonic frequencies in the field of a focused sound beam that propagates through the phase screen. The statistical solutions are verified by comparison with ensemble averaging of direct numerical simulations. The model demonstrates that THI reduces the aberration clutter appearing in the focal region regardless of the depth of the aberrating layer, with suppression of the clutter most effective when the layer is close to the source. The model is also applied to the reverberation clutter that is transmitted forward along the axis of the beam. As with aberration clutter, suppression of such reverberation clutter by THI is most pronounced when the tissue heterogeneity is located close to the source.
Gray, Morgan; Petit, Cyril; Rodionov, Sergey; Bocquet, Marc; Bertino, Laurent; Ferrari, Marc; Fusco, Thierry
2014-08-25
We propose a new algorithm for an adaptive optics system control law, based on the Linear Quadratic Gaussian approach and a Kalman Filter adaptation with localizations. It allows to handle non-stationary behaviors, to obtain performance close to the optimality defined with the residual phase variance minimization criterion, and to reduce the computational burden with an intrinsically parallel implementation on the Extremely Large Telescopes (ELTs).
Space Time Adaptive Processing and Clutter Classification Integration and Evaluation
2002-03-01
S UL³CQEY¯¼z²9ºZ»yÿRGL¼z¼¶Y¯K5ÞGLKWmULHEJ5P\\³CT\\º¯[5HRHRP\\³"N = U�GLT\\TVU¼u²9º¯Y¯Q�JzHEJY¯Q"S/GI³E³�G#»!QkULµ.GLK/HRP"KKGIP¨GI³ EPq ²9º¯GLTVP"W...34Q¨Y¯KàHRUg²9³EU f TVP"Q�QEY¯K5ÞÞL³�U[5²9Q¡HEJGIH¡GI³ EPq [QRP"WmY¯KzHEJ5PqWYüôP\\³EP"K/H×²9YZ²P"º¯Y¯KPCQEHEGIÞLP"Q\\N = JY¯Q¡GLº¯ºZU6°yQ×HEJ5Pq...HEJ5P\\»0GI³�P Q�Y¯¼u²9ºZ»,K5UK f J5U¼zULÞLP"K5P\\U[Q¡³EP\\HE[5³�KQ"N D µYZH×°kP\\³ EPq ²UQ�QEYZõ9ºZPCHRU,á/[GLK/HEYZµ1»AÿR[QEH£J5U#°ÆHEJ5PqGLWW
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.
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.
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
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.
Cox, Patrick H.
2015-01-01
The ability to recognize objects in clutter is crucial for human vision, yet the underlying neural computations remain poorly understood. Previous single-unit electrophysiology recordings in inferotemporal cortex in monkeys and fMRI studies of object-selective cortex in humans have shown that the responses to pairs of objects can sometimes be well described as a weighted average of the responses to the constituent objects. Yet, from a computational standpoint, it is not clear how the challenge of object recognition in clutter can be solved if downstream areas must disentangle the identity of an unknown number of individual objects from the confounded average neuronal responses. An alternative idea is that recognition is based on a subpopulation of neurons that are robust to clutter, i.e., that do not show response averaging, but rather robust object-selective responses in the presence of clutter. Here we show that simulations using the HMAX model of object recognition in cortex can fit the aforementioned single-unit and fMRI data, showing that the averaging-like responses can be understood as the result of responses of object-selective neurons to suboptimal stimuli. Moreover, the model shows how object recognition can be achieved by a sparse readout of neurons whose selectivity is robust to clutter. Finally, the model provides a novel prediction about human object recognition performance, namely, that target recognition ability should show a U-shaped dependency on the similarity of simultaneously presented clutter objects. This prediction is confirmed experimentally, supporting a simple, unifying model of how the brain performs object recognition in clutter. SIGNIFICANCE STATEMENT The neural mechanisms underlying object recognition in cluttered scenes (i.e., containing more than one object) remain poorly understood. Studies have suggested that neural responses to multiple objects correspond to an average of the responses to the constituent objects. Yet, it is
Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images.
Zhao, Haiying; Liu, Yong; Xie, Xiaojia; Liao, Yiyi; Liu, Xixi
2016-07-05
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.
NASA Astrophysics Data System (ADS)
Ozden, Mehmet Tahir
2013-12-01
A multichannel characterization for autoregressive moving average (ARMA) spectrum estimation in subbands is considered in this article. The fullband ARMA spectrum estimation can be realized in two-channels as a special form of this characterization. A complete orthogonalization of input multichannel data is accomplished using a modified form of sequential processing multichannel lattice stages. Matrix operations are avoided, only scalar operations are used, and a multichannel ARMA prediction filter with a highly modular and suitable structure for VLSI implementations is achieved. Lattice reflection coefficients for autoregressive (AR) and moving average (MA) parts are simultaneously computed. These coefficients are then converted to process parameters using a newly developed Levinson-Durbin type multichannel conversion algorithm. Hence, a novel method for spectrum estimation in subbands as well as in fullband is developed. The computational complexity is given in terms of model order parameters, and comparisons with the complexities of nonparametric methods are provided. In addition, the performance is visually and statistically compared against those of the nonparametric methods under both stationary and nonstationary conditions.
Accurate three-dimensional pose recognition from monocular images using template matched filtering
NASA Astrophysics Data System (ADS)
Picos, Kenia; Diaz-Ramirez, Victor H.; Kober, Vitaly; Montemayor, Antonio S.; Pantrigo, Juan J.
2016-06-01
An accurate algorithm for three-dimensional (3-D) pose recognition of a rigid object is presented. The algorithm is based on adaptive template matched filtering and local search optimization. When a scene image is captured, a bank of correlation filters is constructed to find the best correspondence between the current view of the target in the scene and a target image synthesized by means of computer graphics. The synthetic image is created using a known 3-D model of the target and an iterative procedure based on local search. Computer simulation results obtained with the proposed algorithm in synthetic and real-life scenes are presented and discussed in terms of accuracy of pose recognition in the presence of noise, cluttered background, and occlusion. Experimental results show that our proposal presents high accuracy for 3-D pose estimation using monocular images.
GM-PHD filter multitarget tracking in sonar images
NASA Astrophysics Data System (ADS)
Clark, Daniel; Vo, Ba-Ngu; Bell, Judith
2006-05-01
The Gaussian Mixure Probability Hypothesis Density (GM-PHD) Multi-target Tracker was developed as an extension to the GM-PHD filter to provide track continuity. The algorithm is demonstrated on forward-looking sonar data with clutter and is compared with the results from the Particle PHD filter.
Clutter suppression interferometry system design and processing
NASA Astrophysics Data System (ADS)
Knight, Chad; Deming, Ross; Gunther, Jake
2015-05-01
Clutter suppression interferometry (CSI) has received extensive attention due to its multi-modal capability to detect slow-moving targets, and concurrently form high-resolution synthetic aperture radar (SAR) images from the same data. The ability to continuously augment SAR images with geo-located ground moving target indicators (GMTI) provides valuable real-time situational awareness that is important for many applications. CSI can be accomplished with minimal hardware and processing resources. This makes CSI a natural candidate for applications where size, weight and power (SWaP) are constrained, such as unmanned aerial vehicles (UAVs) and small satellites. This paper will discuss the theory for optimal CSI system configuration focusing on sparse time-varying transmit and receive array manifold due to SWaP considerations. The underlying signal model will be presented and discussed as well as the potential benefits that a sparse time-varying transmit receive manifold provides. The high-level processing objectives will be detailed and examined on simulated data. Then actual SAR data collected with the Space Dynamic Laboratory (SDL) FlexSAR radar system will be analyzed. The simulated data contrasted with actual SAR data helps illustrate the challenges and limitations found in practice vs. theory. A new novel approach incorporating sparse signal processing is discussed that has the potential to reduce false- alarm rates and improve detections.
Signal Quality Estimation With Multichannel Adaptive Filtering in Intensive Care Settings
Lee, Joon; Mark, Roger G.
2013-01-01
A signal quality estimate of a physiological waveform can be an important initial step for automated processing of real-world data. This paper presents a new generic point-by-point signal quality index (SQI) based on adaptive multichannel prediction that does not rely on ad hoc morphological feature extraction from the target waveform. An application of this new SQI to photoplethysmograms (PPG), arterial blood pressure (ABP) measurements, and ECG showed that the SQI is monotonically related to signal-to-noise ratio (simulated by adding white Gaussian noise) and to subjective human quality assessment of 1361 multichannel waveform epochs. A receiver-operating-characteristic (ROC) curve analysis, with the human “bad” quality label as positive and the “good” quality label as negative, yielded areas under the ROC curve of 0.86 (PPG), 0.82 (ABP), and 0.68 (ECG). PMID:22717504
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
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.
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.
Global Infrasound Association Based on Probabilistic Clutter Categorization
NASA Astrophysics Data System (ADS)
Arora, N. S.; Mialle, P.
2015-12-01
The IDC collects waveforms from a global network of infrasound sensors maintained by the IMS, and automatically detects signal onsets and associates them to form event hypotheses. However, a large number of signal onsets are due to local clutter sources such as microbaroms (from standing waves in the oceans), waterfalls, dams, gas flares, surf (ocean breaking waves) etc. These sources are either too diffuse or too local to form events. Worse still, the repetitive nature of this clutter leads to a large number of false event hypotheses due to the random matching of clutter at multiple stations. Previous studies, for example [1], have worked on categorization of clutter using long term trends on detection azimuth, frequency, and amplitude at each station. In this work we continue the same line of reasoning to build a probabilistic model of clutter that is used as part of NET-VISA [2], a Bayesian approach to network processing. The resulting model is a fusion of seismic, hydro-acoustic and infrasound processing built on a unified probabilistic framework. Notes: The attached figure shows all the unassociated arrivals detected at IMS station I09BR for 2012 distributed by azimuth and center frequency. (The title displays the bandwidth of the kernel density estimate along the azimuth and frequency dimensions).This plot shows multiple micro-barom sources as well as other sources of infrasound clutter. A diverse clutter-field such as this one is quite common for most IMS infrasound stations, and it highlights the dangers of forming events without due consideration of this source of noise. References: [1] Infrasound categorization Towards a statistics-based approach. J. Vergoz, P. Gaillard, A. Le Pichon, N. Brachet, and L. Ceranna. ITW 2011 [2] NET-VISA: Network Processing Vertically Integrated Seismic Analysis. N. S. Arora, S. Russell, and E. Sudderth. BSSA 2013.
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.
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
Coherent CFAR detection in compound Gaussian clutter with inverse gamma texture
NASA Astrophysics Data System (ADS)
Weinberg, Graham V.
2013-12-01
Recent publications have explored coherent radar detection in a compound Gaussian clutter environment with inverse gamma texture, since the latter clutter model has been validated for X-band high-resolution maritime surveillance radar clutter returns. This paper explores the development of coherent constant false alarm rate (CFAR) detectors for this scenario. In the first instance, a detector is constructed with explicit knowledge of the clutter parameters. It is then shown that the probability of false alarm/threshold relationship does not vary with the clutter power. To achieve a CFAR detector, clutter parameter approximations are then introduced, and the cost associated with this is then analysed.
De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering
NASA Astrophysics Data System (ADS)
Pande-Chhetri, Roshan; Abd-Elrahman, Amr
2011-09-01
Hyperspectral imagers are built line-by-line similar to images acquired by pushbroom sensors. They can experience striping artifacts due to variations in detector response to incident imagery. In this research, a method for hyperspectral image de-striping based on wavelet analysis and adaptive Fourier zero-frequency amplitude normalization has been developed. The algorithm was tested against three other de-striping algorithms. Hyperspectral image bands of different scenes with significant striping and random noise, as well as an image with simulated noise, were used in the testing. The results were assessed visually and quantitatively using frequency domain Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE) and/or Peak Signal-to-Ratio (PSNR). The results demonstrated the superiority of our proposed algorithm in de-striping hyperspectral images without introducing unwanted artifacts, yet preserving image details. In the noise-induced image results, the proposed method reduced RMSE error and improved PSNR by 3.5 dB which is better than other tested methods. A Combined method, integrating the proposed algorithm with a generic wavelet-based de-noising algorithm, showed significant random noise suppression in addition to stripe reduction with a PSNR value of 4.3 dB. These findings make the algorithm a candidate for practical implementation on remote sensing images including high resolution hyperspectral images contaminated with stripe and random noise.
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.
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.
Global Infrasound Association Based on Probabilistic Clutter Categorization
NASA Astrophysics Data System (ADS)
Arora, Nimar; Mialle, Pierrick
2016-04-01
The IDC advances its methods and continuously improves its automatic system for the infrasound technology. The IDC focuses on enhancing the automatic system for the identification of valid signals and the optimization of the network detection threshold by identifying ways to refine signal characterization methodology and association criteria. An objective of this study is to reduce the number of associated infrasound arrivals that are rejected from the automatic bulletins when generating the reviewed event bulletins. Indeed, a considerable number of signal detections are due to local clutter sources such as microbaroms, waterfalls, dams, gas flares, surf (ocean breaking waves) etc. These sources are either too diffuse or too local to form events. Worse still, the repetitive nature of this clutter leads to a large number of false event hypotheses due to the random matching of clutter at multiple stations. Previous studies, for example [1], have worked on categorization of clutter using long term trends on detection azimuth, frequency, and amplitude at each station. In this work we continue the same line of reasoning to build a probabilistic model of clutter that is used as part of NETVISA [2], a Bayesian approach to network processing. The resulting model is a fusion of seismic, hydroacoustic and infrasound processing built on a unified probabilistic framework. References: [1] Infrasound categorization Towards a statistics based approach. J. Vergoz, P. Gaillard, A. Le Pichon, N. Brachet, and L. Ceranna. ITW 2011 [2] NETVISA: Network Processing Vertically Integrated Seismic Analysis. N. S. Arora, S. Russell, and E. Sudderth. BSSA 2013
Ultrawideband radar clutter measurements of forested terrain, 1991--1992
Sheen, D.M.; Severtsen, R.H.; Prince, J.M.; Davis, K.C.; Collins, H.D.
1993-06-01
The ultrawideband (UWB) radar clutter measurements project was conducted to provide radar clutter data for new ultrawideband radar systems which are currently under development. A particular goal of this project is to determine if conventional narrow band clutter data may be extrapolated to the UWB case. This report documents measurements conducted in 1991 and additional measurements conducted in 1992. The original project consisted of clutter measurements of forested terrain in the Olympic National Forest near Sequim, WA. The impulse radar system used a 30 kW peak impulse source with a 2 Gigasample/second digitizer to form a UHF (300--1000 MHz) ultrawideband impulse radar system. Additional measurements were conducted in parallel using a Systems Planning Corporation (SPC) step-chirp radar system. This system utilized pulse widths of 1330 nanoseconds over a bandwidth of 300--1000 MHz to obtain similar resolution to the impulse system. Due to the slow digitizer data throughput in the impulse radar system, data collection rates were significantly higher using the step-chirp system. Additional forest clutter measurements were undertaken in 1992 to increase the amount of data available, and especially to increase the amount of data from the impulse radar system.
Big brown bats (Eptesicus fuscus) reveal diverse strategies for sonar target tracking in clutter.
Mao, Beatrice; Aytekin, Murat; Wilkinson, Gerald S; Moss, Cynthia F
2016-09-01
Bats actively adjust the acoustic features of their sonar calls to control echo information specific to a given task and environment. A previous study investigated how bats adapted their echolocation behavior when tracking a moving target in the presence of a stationary distracter at different distances and angular offsets. The use of only one distracter, however, left open the possibility that a bat could reduce the interference of the distracter by turning its head. Here, bats tracked a moving target in the presence of one or two symmetrically placed distracters to investigate adaptive echolocation behavior in a situation where vocalizing off-axis would result in increased interference from distracter echoes. Both bats reduced bandwidth and duration but increased sweep rate in more challenging distracter conditions, and surprisingly, made more head turns in the two-distracter condition compared to one, but only when distracters were placed at large angular offsets. However, for most variables examined, subjects showed distinct strategies to reduce clutter interference, either by (1) changing spectral or temporal features of their calls, or (2) producing large numbers of sonar sound groups and consistent head-turning behavior. The results suggest that individual bats can use different strategies for target tracking in cluttered environments.
Reliable motion detection of small targets in video with low signal-to-clutter ratios
Nichols, S.A.; Naylor, R.B.
1995-07-01
Studies show that vigilance decreases rapidly after several minutes when human operators are required to search live video for infrequent intrusion detections. Therefore, there is a need for systems which can automatically detect targets in live video and reserve the operator`s attention for assessment only. Thus far, automated systems have not simultaneously provided adequate detection sensitivity, false alarm suppression, and ease of setup when used in external, unconstrained environments. This unsatisfactory performance can be exacerbated by poor video imagery with low contrast, high noise, dynamic clutter, image misregistration, and/or the presence of small, slow, or erratically moving targets. This paper describes a highly adaptive video motion detection and tracking algorithm which has been developed as part of Sandia`s Advanced Exterior Sensor (AES) program. The AES is a wide-area detection and assessment system for use in unconstrained exterior security applications. The AES detection and tracking algorithm provides good performance under stressing data and environmental conditions. Features of the algorithm include: reliable detection with negligible false alarm rate of variable velocity targets having low signal-to-clutter ratios; reliable tracking of targets that exhibit motion that is non-inertial, i.e., varies in direction and velocity; automatic adaptation to both infrared and visible imagery with variable quality; and suppression of false alarms caused by sensor flaws and/or cutouts.
Reliable motion detection of small targets in video with low signal-to-clutter ratios
NASA Astrophysics Data System (ADS)
Nichols, Scott A.; Naylor, R. B.
1995-09-01
Studies show that vigilance decreases rapidly after several minutes when human operators are required to search live video for infrequent intrusion detections. Therefore, there is a need for systems which can automatically detect targets in live video and reserve the operator's attention for assessment only. Thus far, automated systems have not simultaneously provided adequate detection sensitivity, false alarm suppression, and ease of setup when used in external, unconstrained environments. This unsatisfactory performance can be exacerbated by poor video imagery with low contrast, high noise, dynamic clutter, image misregistration, and/or the presence of small, slow, or erratically moving targets. This paper describes a highly adaptive video motion detection and tracking algorithm provides good performance under stressing data and environmental conditions. Features of the algorithm include: reliable detection with negligible false alarm rate of variable velocity targets having low signal-to- clutter ratios; reliable tracking of targets that exhibit motion that is non-inertial, i.e. varies in direction and velocity; automatic adaptation to both infrared and visible imagery with variable quality; and suppression of false alarms caused by sensor flaws and/or cutouts.
NASA Astrophysics Data System (ADS)
Klett, Karl K., Jr.
2010-04-01
An analysis was performed, using MODTRAN, to determine the best filters to use for detecting the muzzle flash of an AK-47 in daylight conditions in the desert. Filters with bandwidths of 0.05, 0.1, 0.5, 1.0, 3.0, and 5.0 nanometers (nm) were analyzed to understand how the optical bandwidth affects the signal-to-solar clutter ratio. These filters were evaluated near the potassium D1 and D2 doublet emission lines that occur at 769.89 and 766.49 nm respectively that are observed where projectile propellants are used. The maximum spectral radiance, from the AK-47 muzzle flash, is 1.88 x 10-2 W/cm2 str micron, and is approximately equal to the daytime atmospheric spectral radiance. The increased emission, due to the potassium doublet lines, and decreased atmospheric transmission, due to oxygen absorption, combine to create a condition where the signal-to-solar clutter ratio is greater than 1. The 3 nm filter, has a signal-to-solar clutter ratio of 2.09 when centered at 765.37 nm and provides the best combination of both cost and signal sensitivity.
NASA Astrophysics Data System (ADS)
Lu, Lu; Zhao, Haiquan
2016-03-01
The filtered-x least mean lp-norm (FxLMP) algorithm is proven to be useful for nonlinear active noise control (NANC) systems. However, its performance deteriorates when the impulsive noises are presented in NANC systems. To surmount this shortcoming, a new nonlinear adaptive algorithm based on Volterra expansion model (VFxlogLMP) is developed in this paper, which is derived by minimizing the lp-norm of logarithmic cost. It is found that the FxLMP and VFxlogLMP require to select an appropriate value of p according to the prior information on noise characteristics, which prohibit their practical applications. Based on VFxlogLMP algorithm, we proposed a continuous lp-norm algorithm with logarithmic cost (VFxlogCLMP), which does not need the parameter selection and thresholds estimation. Benefiting from the various error norms for 1≤p≤2, it remains the robustness of VFxlogLMP. Moreover, the convergence behavior of VFxlogCLMP for moving average secondary paths and stochastic input signals is performed. Compared to the existing algorithms, two versions of the proposed algorithms have much better convergence and stability in impulsive noise environments.
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.
Niu, Ben; Liu, Yanjun; Zong, Guangdeng; Han, Zhaoyu; Fu, Jun
2017-01-16
In this paper, a new adaptive approximation-based tracking controller design approach is developed for a class of uncertain nonlinear switched lower-triangular systems with an output constraint using neural networks (NNs). By introducing a novel barrier Lyapunov function (BLF), the constrained switched system is first transformed into a new system without any constraint, which means the control objectives of the both systems are equivalent. Then command filter technique is applied to solve the so-called "explosion of complexity" problem in traditional backstepping procedure, and radial basis function NNs are directly employed to model the unknown nonlinear functions. The designed controller ensures that all the closed-loop variables are ultimately boundedness, while the output limit is not transgressed and the output tracking error can be reduced arbitrarily small. Furthermore, the use of an asymmetric BLF is also explored to handle the case of asymmetric output constraint as a generalization result. Finally, the control performance of the presented control schemes is illustrated via two examples.
Identification of cluttering and stuttering by the public in four countries.
St Louis, Kenneth O; Filatova, Yulia; Coşkun, Mehmet; Topbaş, Seyhun; Ozdemır, Sertan; Georgieva, Dobrinka; McCaffrey, Elise; George, Reshella D
2010-12-01
The investigators sought to explore and compare the identification of cluttering vs stuttering in four different country samples. After reading lay definitions of the two fluency disorders in their own language, convenience samples of 60-90 adult respondents from Turkey, Bulgaria, Russia, and the US identified 51-119 children or adults who either cluttered, stuttered, or both. They also indicated whether or not they, themselves, cluttered or stuttered. The majority of respondents in all four samples identified at least one person who cluttered, stuttered, or cluttered and stuttered. The average respondent identified one person with a fluency disorder, most likely a stutterer, less likely a clutterer, and least likely a clutterer-stutterer. Both similarities and differences characterized those identified in the three groups, e.g., the sex ratios were not the same. As with stuttering, the public apparently is aware of cluttering individuals and can identify such persons.
Reverberation clutter induced by nonlinear internal waves in shallow water.
Henyey, Frank S; Tang, Dajun
2013-10-01
Clutter is related to false alarms for active sonar. It is demonstrated that, in shallow water, target-like clutter in reverberation signals can be caused by nonlinear internal waves. A nonlinear internal wave is modeled using measured stratification on the New Jersey shelf. Reverberation in the presence of the internal wave is modeled numerically. Calculations show that acoustic energy propagating near a sound speed minimum is deflected as a high intensity, higher angle beam into the bottom, where it is backscattered along the reciprocal path. The interaction of sound with the internal wave is isolated in space, hence resulting in a target-like clutter, which is found to be greater than 10 dB above the mean reverberation level.