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CORR
2008
Springer
151views Education» more  CORR 2008»
13 years 5 months ago
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
Compressive sampling offers a new paradigm for acquiring signals that are compressible with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling...
Joel A. Tropp, Deanna Needell
NIPS
2008
13 years 6 months ago
Sparse Signal Recovery Using Markov Random Fields
Compressive Sensing (CS) combines sampling and compression into a single subNyquist linear measurement process for sparse and compressible signals. In this paper, we extend the th...
Volkan Cevher, Marco F. Duarte, Chinmay Hegde, Ric...
ISCAS
2007
IEEE
126views Hardware» more  ISCAS 2007»
13 years 11 months ago
Theory and Implementation of an Analog-to-Information Converter using Random Demodulation
— The new theory of compressive sensing enables direct analog-to-information conversion of compressible signals at subNyquist acquisition rates. We develop new theory, algorithms...
Jason N. Laska, Sami Kirolos, Marco F. Duarte, Tam...
DCC
2007
IEEE
14 years 4 months ago
Quantization of Sparse Representations
Compressive sensing (CS) is a new signal acquisition technique for sparse and compressible signals. Rather than uniformly sampling the signal, CS computes inner products with rand...
Petros Boufounos, Richard G. Baraniuk