Journal ArticleDOI
Adaptive Sparsity Matching Pursuit Algorithm for Sparse Reconstruction
Honglin Wu,Shu Wang +1 more
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This letter presents a new greedy method, called Adaptive Sparsity Matching Pursuit (ASMP), for sparse solutions of underdetermined systems with a typical/random projection matrix, which can extract information on sparsity of the target signal adaptively with a well-designed stagewise approach.Abstract:
This letter presents a new greedy method, called Adaptive Sparsity Matching Pursuit (ASMP), for sparse solutions of underdetermined systems with a typical/random projection matrix Unlike anterior greedy algorithms, ASMP can extract information on sparsity of the target signal adaptively with a well-designed stagewise approach Moreover, it takes advantage of backtracking to refine the chosen supports and the current approximation in the process With these improvements, ASMP provides even more attractive results than the state-of-the-art greedy algorithm CoSaMP without prior knowledge of the sparsity level Experiments validate the proposed algorithm works well for both noiseless signals and noisy signals, with the recovery quality often outperforming that of l1-minimization and other greedy algorithmsread more
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Compressive Sensing Image Sensors-Hardware Implementation
TL;DR: Considering the recent advances in CMOS (complementary metal–oxide–semiconductor) technologies and the feasibility of performing on-chip signal processing, important practical issues in the implementation of CS inCMOS sensors are emphasized and the CS coding for video capture is discussed.
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Blind Sub-Nyquist Spectrum Sensing With Modulated Wideband Converter
TL;DR: A blind SNSS algorithm, referred to as the residual energy ratio based detector (RERD), is proposed, which bypasses the need for the above-mentioned prior knowledge and performs spectrum sensing in a more autonomous way.
Journal ArticleDOI
Sparsity Adaptive Estimation of Memory Polynomial Based Models for Power Amplifier Behavioral Modeling
TL;DR: Experimental results show that the RSAMP algorithm can efficiently construct a sparse behavioral model with very few terms, but almost have the same model performance with the full model.
MonographDOI
Sparse representation of visual data for compression and compressed sensing
TL;DR: The ongoing advances in computational photography have introduced a range of new imaging techniques for capturing multidimensional visual data such as light fields, BRDFs, BTFs, and more.
Journal ArticleDOI
Composite Plate Phased Array Structural Health Monitoring Signal Reconstruction Based on Orthogonal Matching Pursuit Algorithm
TL;DR: The experiment result indicated that the orthogonal matching pursuit algorithm can reconstruct the signal completely and accurately as defined by Nyquist sampling theorem for a large number of data and reconstructed in high probability.
References
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Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Journal ArticleDOI
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.
Journal ArticleDOI
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.
Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit: The Gaussian Case
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: In this paper, a greedy algorithm called Orthogonal Matching Pursuit (OMP) was proposed to recover a signal with m nonzero entries in dimension 1 given O(m n d) random linear measurements of that signal.
Journal ArticleDOI
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: If the objects of interest are sparse in a fixed basis or compressible, then it is possible to reconstruct f to within very high accuracy from a small number of random measurements by solving a simple linear program.