The theory of compressive sensing has shown that sparse signals can be reconstructed exactly from many fewer measurements than traditionally believed necessary. In [1], it was sho...
Without reconstructing the signal themselves, signal detection could be solved by detection algorithm, which directly processes sampling value obtained from compressive sensing si...
Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse respo...
Abstract--This paper describes performance bounds for compressed sensing (CS) where the underlying sparse or compressible (sparsely approximable) signal is a vector of nonnegative ...
Maxim Raginsky, Rebecca Willett, Zachary T. Harman...
The recently developed compressive sensing (CS) framework enables the design of sub-Nyquist analog-to-digital converters. Several architectures have been proposed for the acquisit...
John P. Slavinsky, Jason N. Laska, Mark A. Davenpo...