Results 21 to 30 of about 277,175 (273)
On sharp performance bounds for robust sparse signal recoveries [PDF]
, 2009 It is well known in compressive sensing that l_1 minimization can recover the sparsest solution for a large class of underdetermined systems of linear equations, provided the signal is sufficiently sparse.
Hassibi, Babak, Xu, Weiyu
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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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Sparse Recovery Analysis of Preconditioned Frames via Convex Optimization [PDF]
, 2017 Orthogonal Matching Pursuit and Basis Pursuit are popular reconstruction algorithms for recovery of sparse signals. The exact recovery property of both the methods has a relation with the coherence of the underlying redundant dictionary, i.e. a frame.
Jampana, Phanindra +3 more
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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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Sequential Compressed Sensing [PDF]
, 2009 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 ...
Malioutov, Dmitry +2 more
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IEEE Access, 2023 Reconfigurable intelligent surfaces (RIS) are passive controllable arrays of small reflectors that direct electromagnetic energy towards or away from the target nodes, thereby allowing better management of signals and interference in a wireless network ...
Bharath Shamasundar +2 more
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