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TSP
2010
13 years 2 months ago
Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms
We present two simple methods for recovering sparse signals from a series of noisy observations. The theory of compressed sensing (CS) requires solving a convex constrained minimiz...
Avishy Carmi, Pini Gurfil, Dimitri Kanevsky
ACL
1993
13 years 9 months ago
Contextual Word Similarity and Estimation from Sparse Data
In recent years there is much interest in word cooccurrence relations, such as n-grams, verb-object combinations, or cooccurrence within a limited context. This paper discusses ho...
Ido Dagan, Shaul Marcus, Shaul Markovitch
ISCAS
2008
IEEE
135views Hardware» more  ISCAS 2008»
14 years 2 months ago
Semi-blind channel estimation of MIMO-OFDM systems with pulse shaping
Abstract— Most of the existing MIMO-OFDM channel estimation methods do not take into account the effect of the pulseshaping filter in the transmitter nor of the matched filter ...
Feng Wan, Wei-Ping Zhu, M. N. S. Swamy
ICASSP
2010
IEEE
13 years 7 months ago
Multiple frequency-hopping signal estimation via sparse regression
Frequency hopping (FH) signals have well-documented merits for commercial and military applications due to their near-far resistance and robustness to jamming. Estimating FH signa...
Daniele Angelosante, Georgios B. Giannakis, Nichol...
CORR
2010
Springer
116views Education» more  CORR 2010»
13 years 7 months ago
Estimation with Random Linear Mixing, Belief Propagation and Compressed Sensing
Abstract--We apply Guo and Wang's relaxed belief propagation (BP) method to the estimation of a random vector from linear measurements followed by a componentwise probabilisti...
Sundeep Rangan