Results 21 to 30 of about 279,028 (324)
Lower Bounds for Sparse Recovery [PDF]
Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms, 2010 11 pages.
Indyk, Piotr +3 more
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Low-Complexity DCD-Based Sparse Recovery Algorithms
IEEE Access, 2017 Sparse recovery techniques find applications in many areas. Real-time implementation of such techniques has been recently an important area for research.
Yuriy V. Zakharov +3 more
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Compressed Sensing of Extracellular Neurophysiology Signals: A Review
Frontiers in Neuroscience, 2021 This article presents a comprehensive survey of literature on the compressed sensing (CS) of neurophysiology signals. CS is a promising technique to achieve high-fidelity, low-rate, and hardware-efficient neural signal compression tasks for wireless ...
Biao Sun, Wenfeng Zhao
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Beamformers for sparse recovery
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013 In sparse recovery from measurement data a common approach is to use greedy pursuit reconstruction algorithms. Most of these algorithms have a correlation filter for detecting active components in the sparse data. In this paper, we show how modifications can be made for the greedy pursuit algorithms so that they use beamformers instead of the standard ...
Sundin, Martin +2 more
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(1 + eps)-Approximate Sparse Recovery [PDF]
2011 IEEE 52nd Annual Symposium on Foundations of Computer Science, 2011 21 pages; appeared at FOCS ...
Price, Eric, Woodruff, David P.
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A Space-time Adaptive Processing Algorithm Based on Joint Sparse Recovery
Leida xuebao, 2014 Sparse recovery Space-Time Adaptive Processing (STAP) methods for obtaining the clutter spectrum require few training samples and can effectively suppress clutter in nonhomogeneous clutter environments.
Duan Ke-qing +4 more
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Scaling Law for Recovering the Sparsest Element in a Subspace [PDF]
, 2014 We address the problem of recovering a sparse $n$-vector within a given subspace. This problem is a subtask of some approaches to dictionary learning and sparse principal component analysis.
Demanet, Laurent, Hand, Paul
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An Armijo-Type Hard Thresholding Algorithm for Joint Sparse Recovery
IEEE Access, 2021 Joint sparse recovery (JSR) in compressed sensing simultaneously recovers sparse signals with a common sparsity structure from their multiple measurement vectors obtained through a common sensing matrix.
Lili Pan, Xunzhi Zhu
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Sparse signal recovery from sparsely corrupted measurements [PDF]
2011 IEEE International Symposium on Information Theory Proceedings, 2011 We investigate the recovery of signals exhibiting a sparse representation in a general (i.e., possibly redundant or incomplete) dictionary that are corrupted by additive noise admitting a sparse representation in another general dictionary. This setup covers a wide range of applications, such as image inpainting, super-resolution, signal separation ...
Christoph Studer +3 more
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