IEEE Journal of Selected Topics in Signal Processing

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Publication Year: 2010, Page(s):C1 - C4
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• IEEE Journal of Selected Topics in Signal Processing publication information

Publication Year: 2010, Page(s): C2
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• Introduction to the Issue on Compressive Sensing

Publication Year: 2010, Page(s):241 - 243
Cited by:  Papers (2)
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Publication Year: 2010, Page(s):244 - 254
Cited by:  Papers (191)
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In this paper, we introduce a new synthetic aperture radar (SAR) imaging modality which can provide a high-resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. This new imaging scheme, requires no new hardware components and allows the aperture to be compressed. It also presents many ... View full abstract»

• Compressive Estimation of Doubly Selective Channels in Multicarrier Systems: Leakage Effects and Sparsity-Enhancing Processing

Publication Year: 2010, Page(s):255 - 271
Cited by:  Papers (126)
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We consider the application of compressed sensing (CS) to the estimation of doubly selective channels within pulse-shaping multicarrier systems (which include orthogonal frequency-division multiplexing (OFDM) systems as a special case). By exploiting sparsity in the delay-Doppler domain, CS-based channel estimation allows for an increase in spectral efficiency through a reduction of the num... View full abstract»

• Compressive Sensing for Missing Data Imputation in Noise Robust Speech Recognition

Publication Year: 2010, Page(s):272 - 287
Cited by:  Papers (46)  |  Patents (3)
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An effective way to increase the noise robustness of automatic speech recognition is to label noisy speech features as either reliable or unreliable (missing), and to replace (impute) the missing ones by clean speech estimates. Conventional imputation techniques employ parametric models and impute the missing features on a frame-by-frame basis. At low signal-to-noise ratios (SNRs), these technique... View full abstract»

• A Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial Fourier Data

Publication Year: 2010, Page(s):288 - 297
Cited by:  Papers (221)  |  Patents (2)
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Recent compressive sensing results show that it is possible to accurately reconstruct certain compressible signals from relatively few linear measurements via solving nonsmooth convex optimization problems. In this paper, we propose the use of the alternating direction method - a classic approach for optimization problems with separable variables (D. Gabay and B. Mercier, ??A dual algorithm for th... View full abstract»

• Normalized Iterative Hard Thresholding: Guaranteed Stability and Performance

Publication Year: 2010, Page(s):298 - 309
Cited by:  Papers (157)
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Sparse signal models are used in many signal processing applications. The task of estimating the sparsest coefficient vector in these models is a combinatorial problem and efficient, often suboptimal strategies have to be used. Fortunately, under certain conditions on the model, several algorithms could be shown to efficiently calculate near-optimal solutions. In this paper, we study one of these ... View full abstract»

• Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit

Publication Year: 2010, Page(s):310 - 316
Cited by:  Papers (303)  |  Patents (4)
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We demonstrate a simple greedy algorithm that can reliably recover a vector v ?? ??d from incomplete and inaccurate measurements x = ??v + e. Here, ?? is a N x d measurement matrix with N<<d, and e is an error vector. Our algorithm, Regularized Orthogonal Matching Pursuit (ROMP), seeks to provide the benefits of the two major... View full abstract»

• Iterative Reweighted $ell_1$ and $ell_2$ Methods for Finding Sparse Solutions

Publication Year: 2010, Page(s):317 - 329
Cited by:  Papers (141)
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A variety of practical methods have recently been introduced for finding maximally sparse representations from overcomplete dictionaries, a central computational task in compressive sensing applications as well as numerous others. Many of the underlying algorithms rely on iterative reweighting schemes that produce more focal estimates as optimization progresses. Two such variants are iterative rew... View full abstract»

• Optimally Tuned Iterative Reconstruction Algorithms for Compressed Sensing

Publication Year: 2010, Page(s):330 - 341
Cited by:  Papers (138)  |  Patents (1)
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We conducted an extensive computational experiment, lasting multiple CPU-years, to optimally select parameters for two important classes of algorithms for finding sparse solutions of underdetermined systems of linear equations. We make the optimally tuned implementations available at sparselab.stanford.edu; they run ??out of the box?? with no user tuning: it is not necessary to select thresholds o... View full abstract»

• General Deviants: An Analysis of Perturbations in Compressed Sensing

Publication Year: 2010, Page(s):342 - 349
Cited by:  Papers (144)  |  Patents (1)
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We analyze the Basis Pursuit recovery of signals with general perturbations. Previous studies have only considered partially perturbed observations Ax + e. Here, x is a signal which we wish to recover, A is a full-rank matrix with more columns than rows, and e is simple additive noise. Our model also incorporates perturbations E to the matrix A which result in multiplicative noise. This completely... View full abstract»

• $ell_{2}/ell_{1}$ -Optimization in Block-Sparse Compressed Sensing and Its Strong Thresholds

Publication Year: 2010, Page(s):350 - 357
Cited by:  Papers (23)
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It has been known for a while that l1-norm relaxation can in certain cases solve an under-determined system of linear equations. Recently, E. Candes ("Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Information Theory, vol. 52, no. 12, pp. 489-509, Dec. 2006) and D. Donoho ("High-dimensional centrally symmetric polyto... View full abstract»

• Construction of a Large Class of Deterministic Sensing Matrices That Satisfy a Statistical Isometry Property

Publication Year: 2010, Page(s):358 - 374
Cited by:  Papers (113)  |  Patents (3)
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Compressed Sensing aims to capture attributes of k-sparse signals using very few measurements. In the standard compressed sensing paradigm, the N ?? C measurement matrix ?? is required to act as a near isometry on the set of all k-sparse signals (restricted isometry property or RIP). Although it is known that certain probabilistic processes generate N ?? C matrices that satisfy RIP with high proba... View full abstract»

• From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals

Publication Year: 2010, Page(s):375 - 391
Cited by:  Papers (474)  |  Patents (15)
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Conventional sub-Nyquist sampling methods for analog signals exploit prior information about the spectral support. In this paper, we consider the challenging problem of blind sub-Nyquist sampling of multiband signals, whose unknown frequency support occupies only a small portion of a wide spectrum. Our primary design goals are efficient hardware implementation and low computational load on the sup... View full abstract»

• Robust Sampling and Reconstruction Methods for Sparse Signals in the Presence of Impulsive Noise

Publication Year: 2010, Page(s):392 - 408
Cited by:  Papers (57)
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Recent results in compressed sensing show that a sparse or compressible signal can be reconstructed from a few incoherent measurements. Since noise is always present in practical data acquisition systems, sensing, and reconstruction methods are developed assuming a Gaussian (light-tailed) model for the corrupting noise. However, when the underlying signal and/or the measurements are corrupted by i... View full abstract»

• A Stochastic Gradient Approach on Compressive Sensing Signal Reconstruction Based on Adaptive Filtering Framework

Publication Year: 2010, Page(s):409 - 420
Cited by:  Papers (67)
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Based on the methodological similarity between sparse signal reconstruction and system identification, a new approach for sparse signal reconstruction in compressive sensing (CS) is proposed in this paper. This approach employs a stochastic gradient-based adaptive filtering framework, which is commonly used in system identification, to solve the sparse signal reconstruction problem. Two typical al... View full abstract»

• Dynamic Updating for $ell_{1}$ Minimization

Publication Year: 2010, Page(s):421 - 434
Cited by:  Papers (51)
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The theory of compressive sensing (CS) has shown us that under certain conditions, a sparse signal can be recovered from a small number of linear incoherent measurements. An effective class of reconstruction algorithms involve solving a convex optimization program that balances the l1 norm of the solution against a data fidelity term. Tremendous progress has been made in recent years on... View full abstract»

• Sequential Compressed Sensing

Publication Year: 2010, Page(s):435 - 444
Cited by:  Papers (62)
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Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of random measurements. Existing results in compressed sensing literature have focused on characterizing the achievable performance by bounding the number of samples required for a given level of signal sparsity. However, using these bounds to minimize the number of samples requ... View full abstract»

• Signal Processing With Compressive Measurements

Publication Year: 2010, Page(s):445 - 460
Cited by:  Papers (270)  |  Patents (3)
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The recently introduced theory of compressive sensing enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist-rate samples. Interestingly, it has been shown that random projections are a near-optimal measurement scheme. This has inspired the design of h... View full abstract»

• IEEE Journal of Selected Topics in Signal Processing Information for authors

Publication Year: 2010, Page(s):461 - 462
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• Special issue on New Frontier in Rich Transcription

Publication Year: 2010, Page(s): 463
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Publication Year: 2010, Page(s): 464
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• IEEE Signal Processing Society Information

Publication Year: 2010, Page(s): C3
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Aims & Scope

The Journal of Selected Topics in Signal Processing (J-STSP) solicits special issues on topics that cover the entire scope of the IEEE Signal Processing Society including the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques.

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Meet Our Editors

Editor-in-Chief

Shrikanth (Shri) S. Narayanan
Viterbi School of Engineering
University of Southern California
Los Angeles, CA 90089 USA
shri@sipi.usc.edu