Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals that can be well approximated by just K N elements from a...
Richard G. Baraniuk, Volkan Cevher, Marco F. Duart...
—In this paper we look at the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a data gathering point. The techniqu...
Riccardo Masiero, Giorgio Quer, Daniele Munaretto,...
Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-N...
Dror Baron, Shriram Sarvotham, Richard G. Baraniuk
The advent of Compressive Sensing has provided significant mathematical tools to enhance the sensing capabilities of hardware devices. In this paper we apply Compressive Sensing ...
— The new theory of compressive sensing enables direct analog-to-information conversion of compressible signals at subNyquist acquisition rates. We develop new theory, algorithms...
Jason N. Laska, Sami Kirolos, Marco F. Duarte, Tam...