Add to ORKGThese notes give a mathematical introduction to compressive sensing focusing on recovery using `1-minimization and structured random matrices. An emphasis is put on techniques for proving probabilistic estimates for condition numbers of structured random ma-trices. Estimates of this type are key to providing conditions that ensure exact or approximate recovery of sparse vectors using `1-minimization
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/2...
Abstract—Compressive sensing is a new methodology to cap-ture signals at sub-Nyquist rate. To guaran...
Abstract. Compressive sensing predicts that sufficiently sparse vectors can be recovered from highly...
Compressive (or compressed) sensing (CS) is an emerging methodology in computational signal processi...
We consider two theorems from the theory of compressive sensing. Mainly a theorem concerning uniform...
In compressive sensing practice, the choice of compression matrix reflects the important tradeoffs b...
The sparse signal recovery in the standard compressed sensing (CS) problem requires that the sensing...
Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using ...
Abstract—Compressive sensing is a methodology for the re-construction of sparse or compressible sign...
The sparse signal recovery in standard compressed sensing (CS) requires that the sensing matrix is e...
This paper introduces a simple and very general theory of compressive sensing. In this theory, the s...
For an m × N underdetermined system of linear equations with independent pre-Gaussian random coeffic...
We use deterministic and probabilistic methods to analyze the performance of compressed sensing matr...
Recall the setup in compressive sensing. There is an unknown signal z ∈ Rn, and we can only glean in...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/2...
Abstract—Compressive sensing is a new methodology to cap-ture signals at sub-Nyquist rate. To guaran...
Abstract. Compressive sensing predicts that sufficiently sparse vectors can be recovered from highly...
Compressive (or compressed) sensing (CS) is an emerging methodology in computational signal processi...
We consider two theorems from the theory of compressive sensing. Mainly a theorem concerning uniform...
In compressive sensing practice, the choice of compression matrix reflects the important tradeoffs b...
The sparse signal recovery in the standard compressed sensing (CS) problem requires that the sensing...
Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using ...
Abstract—Compressive sensing is a methodology for the re-construction of sparse or compressible sign...
The sparse signal recovery in standard compressed sensing (CS) requires that the sensing matrix is e...
This paper introduces a simple and very general theory of compressive sensing. In this theory, the s...
For an m × N underdetermined system of linear equations with independent pre-Gaussian random coeffic...
We use deterministic and probabilistic methods to analyze the performance of compressed sensing matr...
Recall the setup in compressive sensing. There is an unknown signal z ∈ Rn, and we can only glean in...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/2...
Abstract—Compressive sensing is a new methodology to cap-ture signals at sub-Nyquist rate. To guaran...