Results 1 to 10 of about 337 (161)
The restricted isometry property of block diagonal matrices for group-sparse signal recovery
Applied and Computational Harmonic Analysis, 2022 Group-sparsity is a common low-complexity signal model with widespread application across various domains of science and engineering. The recovery of such signal ensembles from compressive measurements has been extensively studied in the literature under the assumption that measurement operators are modeled as densely populated random matrices. In this
Arash Behboodi
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The restricted isometry property for random block diagonal matrices
Applied and Computational Harmonic Analysis, 2015 In Compressive Sensing, the Restricted Isometry Property (RIP) ensures that robust recovery of sparse vectors is possible from noisy, undersampled measurements via computationally tractable algorithms. It is by now well-known that Gaussian (or, more generally, sub-Gaussian) random matrices satisfy the RIP under certain conditions on the number of ...
Han Lun Yap +2 more
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A new bound on the block restricted isometry constant in compressed sensing [PDF]
Journal of Inequalities and Applications, 2017 This paper focuses on the sufficient condition of block sparse recovery with the l 2 / l 1 $l_{2}/l_{1}$ -minimization. We show that if the measurement matrix satisfies the block restricted isometry property with δ 2 s | I < 0.6246 $\delta_{2s|\mathcal{I}
Yi Gao, Mingde Ma
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An Extended Block Restricted Isometry Property for Sparse Recovery with Non-Gaussian Noise
Journal of Computational Mathematics, 2020 We study the recovery conditions of weighted mixed l2/lp minimization for block sparse signal reconstruction from compressed measurements when partial block supportinformation is available.
Klara Leffler, Zhiyong Zhou, Jun Yu
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Non-convex block-sparse compressed sensing with coherent tight frames
EURASIP Journal on Advances in Signal Processing, 2020 In this paper, we present a non-convex ℓ 2/ℓ q ...
Xiaohu Luo +4 more
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On Recovery of Block Sparse Signals via Block Compressive Sampling Matching Pursuit
IEEE Access, 2019 Compressive sampling matching pursuit (CoSaMP) is an efficient reconstruction algorithm for sparse signal. When the signal is block sparse, i.e., the non-zero elements are presented in clusters, some block sparse reconstruction algorithms have been ...
Xiaobo Zhang +4 more
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Sparse Recovery With Block Multiple Measurement Vectors Algorithm
IEEE Access, 2019 This paper investigates the performance of the block multiple measurement vectors (BMMV) algorithm in reconstructing block joint sparse matrices. We prove that if 41) obeys block restricted isometry property with 8 K+1 <; Nf +1 , then BMMV perfectly ...
Yanli Shi, Libo Wang, Rong Luo
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A Note on Block-Sparse Signal Recovery with Coherent Tight Frames
Discrete Dynamics in Nature and Society, 2013 This note discusses the recovery of signals from undersampled data in the situation that such signals are nearly block sparse in terms of an overcomplete and coherent tight frame D. By introducing the notion of block D-restricted isometry property (D-RIP)
Yao Wang, Jianjun Wang, Zongben Xu
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On Novel RIP of Windowed Compressed Spectrum Sensing
IEEE AccessCompressed spectrum sensing (CSS) offers great advantages in spectral analysis through sub-Nyquist sampling. However, conventional CSS approaches have not sufficiently addressed the effect of spectral leakage (SL) on sensing performance, a problem that ...
Huiguang Zhang, Baoguo Liu, Wei Feng
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Advanced Healthcare Materials, EarlyView.This review examines how cellular behavior is regulated by mechanical cues transmitted through soft biomaterials, from single‐cell mechanosensing to tissue‐level adaptation. It highlights why physiological relevance, rather than model complexity alone, is critical for translational mechanobiology and introduces a scoring framework linking material ...
Mathias Polz +9 more
wiley +1 more source