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Standard compressive sensing results state that to exactly recover an s sparse signal in Rp , one requires O(s · log p) measurements. While this bound is extremely useful in prac...
We consider the problem of recursively and causally reconstructing time sequences of sparse signals (with unknown and time-varying sparsity patterns) from a limited number of noisy...
In this paper we describe an automatic procedure for successively reducing the set of possible nonzeros in a Jacobian matrix until eventually the exact sparsity pattern is obtained...
The problem of recovering the sparsity pattern of a fixed but unknown vector β∗ ∈ Rp based on a set of n noisy observations arises in a variety of settings, including subset...
Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an 1-regularized linear regression prob...
In Cognitive Radio scenarios channelization information from primary network may be available to the spectral monitor. Under this assumption we propose a spectral estimation algor...
This paper addresses the problem of sparsity pattern detection for unknown ksparse n-dimensional signals observed through m noisy, random linear measurements. Sparsity pattern rec...
Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal
We consider the problem of reconstructing time sequences of spatially sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear "incohe...