We describe a connection between the identification problem for matrices with sparse representations in given matrix dictionaries and the problem of sparse signal recov-ery. This allows the application of novel compressed sensing techniques to operator identification problems such as the channel measurement problem in communica-tions engineering
This survey provides a brief introduction to compressed sensing as well as several major algorithms ...
Recovery of the sparsity pattern (or support) of an unknown sparse vector from a limited number of n...
Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using ...
We consider the problem of recovering a matrix from its action on a known vector in the setting wher...
Abstract Compressed sensing was introduced some ten years ago as an effective way of acquiring signa...
The problem of recovering sparse signals from a limited number of measurements is now ubiquitous in ...
In this thesis we give an overview of the notion of compressed sensing together with some special ty...
Recall the setup in compressive sensing. There is an unknown signal z ∈ Rn, and we can only glean in...
The sparse signal recovery in standard compressed sensing (CS) requires that the sensing matrix is e...
In the theory of compressed sensing (CS), the sparsity ‖x‖0 of the unknown signal x ∈ Rp is commonly...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/2...
This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cas...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
The sparse signal recovery in the standard compressed sensing (CS) problem requires that the sensing...
A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analy...
This survey provides a brief introduction to compressed sensing as well as several major algorithms ...
Recovery of the sparsity pattern (or support) of an unknown sparse vector from a limited number of n...
Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using ...
We consider the problem of recovering a matrix from its action on a known vector in the setting wher...
Abstract Compressed sensing was introduced some ten years ago as an effective way of acquiring signa...
The problem of recovering sparse signals from a limited number of measurements is now ubiquitous in ...
In this thesis we give an overview of the notion of compressed sensing together with some special ty...
Recall the setup in compressive sensing. There is an unknown signal z ∈ Rn, and we can only glean in...
The sparse signal recovery in standard compressed sensing (CS) requires that the sensing matrix is e...
In the theory of compressed sensing (CS), the sparsity ‖x‖0 of the unknown signal x ∈ Rp is commonly...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/2...
This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cas...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
The sparse signal recovery in the standard compressed sensing (CS) problem requires that the sensing...
A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analy...
This survey provides a brief introduction to compressed sensing as well as several major algorithms ...
Recovery of the sparsity pattern (or support) of an unknown sparse vector from a limited number of n...
Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using ...