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Sparse Signal Recovery via l1 Minimization
2006 40th Annual Conference on Information Sciences and Systems, 2006The purpose of this paper is to give a brief overview of the main results for sparse recovery via L optimization. Given a set of K linear measurements y=Ax where A is a Ktimes;N matrix, the recovery is performed by solving the convex program minparxpar1 subject to Ax=y, where parxpar1:=Sigma t=0 N-1|x(t)|.
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Cancer risk among World Trade Center rescue and recovery workers: A review
Ca-A Cancer Journal for Clinicians, 2022Paolo Boffetta +2 more
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Sparse Signal Recovery via Tail-FISTA
2022 34th Chinese Control and Decision Conference (CCDC), 2022Qianjin Zhao +3 more
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Planning for post‐pandemic cancer care delivery: Recovery or opportunity for redesign?
Ca-A Cancer Journal for Clinicians, 2021Pelin Cinar +2 more
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Session TP3b: Sparse signal recovery
2014 48th Asilomar Conference on Signals, Systems and Computers, 2014openaire +1 more source
Sparse Signal Recovery via Convex Optimization
2016We propose recovering 1D piecewice linear signal using a sparsity-based method consisting of two steps. The first step is signal segmentation via optimization algorithms solving sparsity based model. Second step consists of applying an ordinary mean square method on each detected segment of the signal. We show results of our experiments on two types of
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Signal Recovery on Graphs: Variation Minimization
IEEE Transactions on Signal Processing, 2015Siheng Chen +2 more
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