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Asilomar Conference on Signals, Systems & Computers

作者： W. Zhou D. Chakraborty N. Kovvali A. Papandreou-Suppappola D. Cochran A. Chattopadhyay Arizona State University USA

We propose an algorithm for the classification of structural damage based on the use of the continuous hidden Markov modeling (HMM) technique. Our approach employs HMMs to model time-frequency damage features extracted from structural data using the matching pursuit decomposition algorithm. We investigate modeling with continuous observation-density HMMs and discuss the trade-offs involved as compared to the discrete HMM case. A variational Bayesian method is employed to automatically estimate the HMM state number and adapt the classifier for real-time use. We present results that classify structural and material (fatigue) damage in a bolted-joint structure.

关键词： Monitoring Time frequency analysis Feature extraction Hidden Markov models Matching pursuit algorithms Classification algorithms Data mining pursuit algorithms Bayesian methods State estimation

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Sparse Gradient Image Reconstruction Done Faster

Sparse Gradient Image Reconstruction Done Faster

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IEEE International Conference on Image Processing

作者： R. Maleh A. C. Gilbert M. J. Strauss Department of Mathematics University of Michigan Ann Arbor MI USA

In a wide variety of imaging applications (especially medical imaging), we obtain a partial set or subset of the Fourier transform of an image. From these Fourier measurements, we want to reconstruct the entire original image. Convex optimization is a powerful, recent solution to this problem. Unfortunately, convex optimization in its myriad of implementations is computationally expensive and may be impractical for large images or for multiple images. Furthermore, some of these techniques assume that the image has a sparse gradient (i.e., that the gradient of the image consists of a few nonzero pixel values) or that the gradient is highly compressible. In this paper, we demonstrate that we can recover such images with GradientOMP, an efficient algorithm based upon Orthogonal Matching pursuit (OMP), more effectively than with convex optimization. We compare both the qualitative and quantitative performance of this algorithm to the optimization techniques.

关键词： Image reconstruction Matching pursuit algorithms Hyperspectral imaging Image coding Biomedical imaging Fourier transforms Hyperspectral sensors Mathematics Pixel pursuit algorithms

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Blind Non-independent Image Separation Based on Independent Component Analysis

Blind Non-independent Image Separation Based on Independent ...

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International Conference on Computational Intelligence and Security Workshops (CISW)

作者： Wu Guo Peng Zhang Runsheng Wang ATR Laboratory National University of Defense Technology Changsha Hunan China

Blind separation of mixture images which mutually independent has been solved efficiently by some independent component analysis(ICA) methods. But these methods often failed in case of the source images are statistically non-independent. A novel fixed-point FastICA algorithm based on complexity pursuit is presented in this paper and with the algorithm the mixed images which not mutually independent can be separated successfully. Experimental results demonstrate the efficiency of our proposed method.

关键词： Independent component analysis Signal processing algorithms Source separation pursuit algorithms Laboratories Biomedical signal processing Geophysical signal processing Speech Covariance matrix Computational intelligence

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Time-frequency analysis of position-dependent dynamics in a Motion System with ILC

Time-frequency analysis of position-dependent dynamics in a ...

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American Control Conference (ACC)

作者： Bart Hennen Iuliana Rotariu Maarten Steinbuch Department of Mechanical Engineering Eindhovan University of Technology Eindhoven Netherlands Australian Centre for Field Robotics University of Sydney NSW Australia

This paper exploits high-resolution time-frequency analysis of servo error signals in the investigation of position-dependent dynamics in a motion system with iterative learning control. Based on the matching pursuit algorithm signals are decomposed with respect to a multiple complex dictionary of atoms. A high-resolution signal energy distribution is derived in the time-frequency plane, which does not include cross-terms, like Wigner distribution. The proposed method, applied for analysis of servo errors of a real system, provides insights in the appearance and propagation of certain low energetic system features, which can be related to position-dependent dynamics.

关键词： Time frequency analysis Motion analysis Servomechanisms Matching pursuit algorithms Error correction Motion control Control systems Iterative algorithms pursuit algorithms Dictionaries

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Performance Evaluation of Latent Variable Models with Sparse Priors

Performance Evaluation of Latent Variable Models with Sparse...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： David Wipf Jason Palmer Bhaskar Rao Kenneth Kreutz-Delgado Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA

A variety of Bayesian methods have recently been introduced for finding sparse representations from overcomplete dictionaries of candidate features. These methods often capitalize on latent structure inherent in sparse distributions to perform standard MAP estimation, variational Bayes, approximation using convex duality, or evidence maximization. Despite their reliance on sparsity-inducing priors however, these approaches may or may not actually lead to sparse representations in practice, and so it is a challenging task to determine which algorithm and sparse prior is appropriate. Rather than justifying prior selections and modelling assumptions based on the credibility of the full Bayesian model as is commonly done, this paper bases evaluations on the actual cost functions that emerge from each method. Two minimal conditions are postulated that ideally any sparse learning objective should satisfy. Out of all possible cost functions that can be obtained from the methods described above using (virtually) any sparse prior, a unique function is derived that satisfies these conditions. Both sparse Bayesian learning (SBL) and basis pursuit (BP) are special cases. Later, all methods are shown to be performing MAP estimation using potentially non-factorable implicit priors, which suggests new sparse learning cost functions.

关键词： Bayesian methods Dictionaries Cost function Inverse problems Gaussian processes Laplace equations Context modeling pursuit algorithms

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Compressed Sensing Framework for EEG Compression

Compressed Sensing Framework for EEG Compression

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Selin Aviyente Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

Many applications in signal processing require the efficient representation and processing of data. The traditional approach to efficient signal representation is compression. In recent years, there has been a new approach to compression at the sensing level. Compressed sensing (CS) is an emerging field which is based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper, we propose an application of compressed sensing in the field of biomedical signal processing, particularly electroencophelogram (EEG) collection and storage. A compressed sensing framework is introduced for efficient representation of multichannel, multiple trial EEG data. The proposed framework is based on the revelation that EEG signals are sparse in a Gabor frame. The sparsity of EEG signals in a Gabor frame is utilized for compressed sensing of these signals. A simultaneous orthogonal matching pursuit algorithm is shown to be effective in the joint recovery of the original multiple trail EEG signals from a small number of projections.

关键词： Compressed sensing Electroencephalography Matching pursuit algorithms Sparse matrices Image reconstruction Biomedical signal processing pursuit algorithms Dictionaries Application software Signal representations

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Iterative Hard Thresholding and L0 Regularisation

Iterative Hard Thresholding and L0 Regularisation

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： T. Blumensath M. Yaghoobi M. E. Davies IDCOM & Joint Research Institute for Signal and Image Processing Edinburgh University Edinburgh UK

Sparse signal approximations are approximations that use only a small number of elementary waveforms to describe a signal. In this paper we proof the convergence of an iterative hard thresholding algorithm and show, that the fixed points of that algorithm are local minima of the sparse approximation cost function, which measures both, the reconstruction error and the number of elements in the representation. Simulation results suggest that the algorithm is comparable in performance to a commonly used alternative method.

关键词： Signal processing algorithms Iterative algorithms Equations Cost function Signal processing Convergence Approximation algorithms Matching pursuit algorithms Noise reduction pursuit algorithms

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Performance Study of Various Sparse Representation Methods Using Redundant Frames

Performance Study of Various Sparse Representation Methods U...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Mehmet Akcakaya Vahid Tarokh School of Engineering and Applied Sciences Harvard University Cambridge MA USA

Sparse representations have recently received wide attention because of their numerous potential applications. In this paper, we consider sparse representations of signals with at most L non-zero coefficients using a frame F of size M in C N . We bound the average distortion of such a representation for any arbitrary frame F by a numerical lower bound that is only a function of the sparsity epsiv = L/N of the representation, and the redundancy (r-1) = M/N-1 of F. This numerical lower bound is shown to be much stronger than the analytical and asymptotic bounds of in low dimensions (e.g. N = 6,8, 10), but it is much less straightforward to compute. We then study the performance of randomly generated frames with respect to this numerical lower bound, and to the analytical and asymptotic bounds. When the optimal sparse representation algorithm is used, it is observed that randomly generated frames perform about 2 dB away from the theoretical lower bound in low dimensions. We use the greedy orthogonal matching pursuit (OMP) algorithm to study the performance of randomly generated frames in higher dimensions. For small values of epsiv, randomly generated frames using OMP perform close to the lower bound and the results suggest that the loss of the sub-optimal search using orthogonal matching pursuit algorithm grows as a function of epsiv. As N grows, a concentration phenomenon for the performance of randomly generated frames about their average is observed in all cases, even when using the OMP algorithm.

关键词： Matching pursuit algorithms pursuit algorithms Vectors Decoding Signal analysis Performance analysis Dictionaries Sparse matrices Distortion measurement

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Generalized pursuit learning algorithms for shortest path routing tree computation

Generalized pursuit learning algorithms for shortest path ro...

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IEEE Symposium on Computers and Communications (ISCC)

作者： S. Misra B.J. Oommen School of Computer Science Carleton University Ottawa ONT Canada

This paper presents a new efficient solution to the dynamic single source shortest path routing problem, using the principles of generalized pursuit learning. It involves finding the shortest path in a stochastic network, where there are continuous probabilistically based updates in link-costs. The algorithm has been rigorously experimentally evaluated and has been found to be a few orders of magnitude superior to the algorithms available in the literature. It can be used to find the shortest path within the "statistical" average network, which converges irrespective of whether there are new changes in link-costs or not. On the other hand, the existing algorithms would fail to exhibit such a behavior and would recalculate the affected shortest paths after each link-cost update.

关键词： pursuit algorithms Spread spectrum communication Computer science Stochastic processes Heuristic algorithms IP networks Mobile ad hoc networks Routing protocols Computer networks Tree graphs

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Micro- and macrostructure of sleep EEG - A universal, adoptive time-frequency parameterization

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IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE 2006年第4期25卷 26-31页

作者： Malinowska, Urszula Durka, Piotr J. Blinowska, Katarzyna J. Szelenberger, Waldemar Wakarow, Andrzej Univ Warsaw Dept Biomed Phys PL-00681 Warsaw Poland

Electroencephalogram (EEG) provides important and unique information about the sleeping brain. Polysomnography was the major method of sleep analysis and the main diagnostic tool in sleep medicine. The standard interpretation of polysomnographic recordings describes their macrostructure in terms of sleep stages, delineated according to R&K scoring criteria. Several descriptors of sleep microstructure rely on the quantification of sleep spindles and slow wave activities, detection of arousals, etc. However, these descriptors are usually assessed by means of substantially different signal processing (or visual) methods. This hinders possibilities of combining their results into a coherent description of the sleep process. This study proposes a solution to these problems in terms of a framework based upon adaptive time-frequency approximations - a recent, advanced method of signal processing. The proposed approach provides compatibility with the visual EEG analysis and standard definitions of EEG structures and describes both the macro- and microstructure of sleep EEG. Adaptive time-frequency approximations of signals calculated by means of the matching pursuit (MP) algorithm allow for the discrimination between series of unrelated structures and oscillatory activity. The detection, parametrization, and description of all these features of sleep are based upon the same unifying approach.

关键词： Sleep Electroencephalography Matching pursuit algorithms Microstructure Adaptive signal processing Biomedical signal processing Time frequency analysis Signal processing algorithms Medical diagnostic imaging pursuit algorithms

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