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JMLR
2012
11 years 7 months ago
Universal Measurement Bounds for Structured Sparse Signal Recovery
Standard compressive sensing results state that to exactly recover an s sparse signal in Rp , one requires O(s · log p) measurements. While this bound is extremely useful in prac...
Nikhil S. Rao, Ben Recht, Robert D. Nowak
CORR
2010
Springer
114views Education» more  CORR 2010»
13 years 5 months ago
Sequential Compressed Sensing
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 sens...
Dmitry M. Malioutov, Sujay Sanghavi, Alan S. Wills...
TSP
2010
12 years 11 months ago
Distributed sampling of signals linked by sparse filtering: theory and applications
We study the distributed sampling and centralized reconstruction of two correlated signals, modeled as the input and output of an unknown sparse filtering operation. This is akin ...
Ali Hormati, Olivier Roy, Yue M. Lu, Martin Vetter...
ICASSP
2010
IEEE
13 years 5 months ago
Kronecker product matrices for compressive sensing
Compressive sensing (CS) is an emerging approach for acquisition of signals having a sparse or compressible representation in some basis. While CS literature has mostly focused on...
Marco F. Duarte, Richard G. Baraniuk
DCC
2008
IEEE
14 years 4 months ago
Sublinear Recovery of Sparse Wavelet Signals
There are two main classes of decoding algorithms for "compressed sensing," those which run time time polynomial in the signal length and those which use sublinear resou...
Ray Maleh, Anna C. Gilbert