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TSP
2008
101views more  TSP 2008»
14 years 11 months ago
Bayesian Compressive Sensing
The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be recons...
Shihao Ji, Ya Xue, Lawrence Carin
INFOCOM
2010
IEEE
14 years 10 months ago
Incorporating Random Linear Network Coding for Peer-to-Peer Network Diagnosis
—Recent studies show that network coding improves multicast session throughput. In this paper, we demonstrate how random linear network coding can be incorporated to provide netw...
Elias Kehdi, Baochun Li
CDC
2008
IEEE
200views Control Systems» more  CDC 2008»
15 years 6 months ago
Maximum-likelihood Kalman filtering for switching discrete-time linear systems
— State estimation is addressed for a class of discrete-time systems that may switch among different modes taken from a finite set. The system and measurement equations of each ...
Angelo Alessandri, Marco Baglietto, Giorgio Battis...
TIT
2010
128views Education» more  TIT 2010»
14 years 6 months ago
Shannon-theoretic limits on noisy compressive sampling
In this paper, we study the number of measurements required to recover a sparse signal in M with L nonzero coefficients from compressed samples in the presence of noise. We conside...
Mehmet Akçakaya, Vahid Tarokh
CORR
2010
Springer
114views Education» more  CORR 2010»
14 years 11 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...