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NIPS
2008
14 years 11 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...
IPSN
2009
Springer
15 years 4 months ago
Near-optimal Bayesian localization via incoherence and sparsity
This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in a sensor network. We introduce a Bayesian framework...
Volkan Cevher, Petros Boufounos, Richard G. Barani...
CORR
2010
Springer
225views Education» more  CORR 2010»
14 years 9 months ago
Sensing Matrix Optimization for Block-Sparse Decoding
Recent work has demonstrated that using a carefully designed sensing matrix rather than a random one, can improve the performance of compressed sensing. In particular, a welldesign...
Kevin Rosenblum, Lihi Zelnik-Manor, Yonina C. Elda...
NIPS
2008
14 years 11 months ago
Inferring rankings under constrained sensing
Motivated by applications like elections, web-page ranking, revenue maximization etc., we consider the question of inferring popular rankings using constrained data. More specific...
Srikanth Jagabathula, Devavrat Shah
ICASSP
2011
IEEE
14 years 1 months ago
Cooperative spectrum sensing based on matrix rank minimization
In cognitive radio (CR) networks, multi-CR cooperation typically takes place during spectrum sensing, to cope with wireless fading effects and the hidden terminal problem. The use...
Yue Wang, Zhi Tian, Chunyan Feng