Results 51 to 60 of about 794 (157)
Probability of correct reconstruction in compressive spectral imaging
Ingeniería e Investigación, 2016 Coded Aperture Snapshot Spectral Imaging (CASSI) systems capture the 3-dimensional (3D) spatio-spectral information of a scene using a set of 2-dimensional (2D) random coded Focal Plane Array (FPA) measurements.
Samuel Eduardo Pinilla +2 more
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Measurement Manipulation of the Matrix Sensing Problem to Improve Optimization Landscape
IEEE AccessThis work studies the matrix sensing (MS) problem through the lens of the Restricted Isometry Property (RIP). It has been shown in several recent papers that two different techniques of convex relaxations and local search methods for the MS problem both ...
Ying Chen, Javad Lavaei
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Deterministic Construction of Compressed Sensing Matrices via Vector Spaces Over Finite Fields
IEEE Access, 2020 Compressed Sensing (CS) is a new signal processing theory under the condition that the signal is sparse or compressible. One of the central problems in compressed sensing is the construction of sensing matrices.
Xuemei Liu, Lihua Jia
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Recovery of Sparse Signals via Modified Hard Thresholding Pursuit Algorithms
IET Signal Processing, 2023 In this paper, we propose a modified version of the hard thresholding pursuit algorithm, called modified hard thresholding pursuit (MHTP), using a convex combination of the current and previous points.
Li-Ping Geng +3 more
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A Novel Secure Data Transmission Scheme Using Chaotic Compressed Sensing
IEEE Access, 2018 In this paper, a novel secure data transmission scheme using chaotic compressed sensing, which has inherent encryption property without additional cost, is proposed based on a T-way Bernoulli shift chaotic system.
Hongping Gan, Song Xiao, Yimin Zhao
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Restricted Isometry Property in Quantized Network Coding of sparse messages [PDF]
2012 IEEE Global Communications Conference (GLOBECOM), 2012 6 ...
Mahdy Nabaee, Fabrice Labeau
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Restricted isometry properties and nonconvex compressive sensing
Inverse Problems, 2007 The recently emerged field known as compressive sensing has produced powerful results showing the ability to recover sparse signals from surprisingly few linear measurements, using l1 minimization. In previous work, numerical experiments showed that lp minimization with 0 < p < 1 recovers sparse signals from fewer linear measurements than does l1 ...
Rick Chartrand, Valentina Staneva
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Semirandom Planted Clique and the Restricted Isometry Property
2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS)22 pages, to appear FOCS ...
Błasiok, Jarosław +3 more
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IEEE AccessThe development of measurement matrices remains a pivotal focus within the domain of compressed sensing theory. This paper introduces an innovative methodology for the construction of a deterministic binary measurement matrix, harnessing the properties ...
Xuanwei Zhang, Huimin Yu
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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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