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TSP
2010
14 years 6 months ago
Distributed sparse linear regression
The Lasso is a popular technique for joint estimation and continuous variable selection, especially well-suited for sparse and possibly under-determined linear regression problems....
Gonzalo Mateos, Juan Andrés Bazerque, Georg...
TSP
2010
14 years 6 months ago
Sparse channel estimation for multicarrier underwater acoustic communication: from subspace methods to compressed sensing
Abstract--In this paper, we investigate various channel estimators that exploit channel sparsity in the time and/or Doppler domain for a multicarrier underwater acoustic system. We...
Christian R. Berger, Shengli Zhou, James C. Preisi...
CORR
2010
Springer
174views Education» more  CORR 2010»
14 years 12 months ago
Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks
Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio. Collaborative spectrum sensing among the cognitive radio n...
Jia Meng, Wotao Yin, Husheng Li, Ekram Hossain, Zh...
SIAMSC
2008
131views more  SIAMSC 2008»
14 years 11 months ago
Gramian-Based Model Reduction for Data-Sparse Systems
Model order reduction (MOR) is common in simulation, control and optimization of complex dynamical systems arising in modeling of physical processes and in the spatial discretizati...
Ulrike Baur, Peter Benner
SIAMIS
2011
14 years 6 months ago
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
Abstract. Accurate signal recovery or image reconstruction from indirect and possibly undersampled data is a topic of considerable interest; for example, the literature in the rece...
Stephen Becker, Jérôme Bobin, Emmanue...