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ICASSP
2010
IEEE
14 years 11 months ago
Texas Hold 'Em algorithms for distributed compressive sensing
This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlati...
Stephen R. Schnelle, Jason N. Laska, Chinmay Hegde...
89
Voted
ICASSP
2008
IEEE
15 years 6 months ago
Distributed compressed sensing: Sparsity models and reconstruction algorithms using annihilating filter
Consider a scenario where a distributed signal is sparse and is acquired by various sensors that see different versions. Thus, we have a set of sparse signals with both some commo...
Ali Hormati, Martin Vetterli
SECON
2008
IEEE
15 years 6 months ago
Practical Algorithms for Gathering Stored Correlated Data in a Network
—Many sensing systems remotely monitor/measure an environment at several sites, and then report these observations to a central site. We propose and investigate several practical...
Ramin Khalili, James F. Kurose
98
Voted
ICASSP
2010
IEEE
14 years 11 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
115
Voted
CORR
2011
Springer
210views Education» more  CORR 2011»
14 years 6 months ago
Statistical Compressed Sensing of Gaussian Mixture Models
A novel framework of compressed sensing, namely statistical compressed sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribu...
Guoshen Yu, Guillermo Sapiro