This paper proposes an extension of compressed sensing that allows to express the sparsity prior in a dictionary of bases. This enables the use of the random sampling strategy of c...
Abstract. Separation of underdetermined mixtures is an important problem in signal processing that has attracted a great deal of attention over the years. Prior knowledge is requir...
According to the recent theory of compressed sensing, accurate reconstruction is possible even from data samples dramatically smaller than Nyquist sampling limit as long as the un...
The theory of compressed sensing shows that samples in the form of random projections are optimal for recovering sparse signals in high-dimensional spaces (i.e., finding needles ...
Rui M. Castro, Jarvis Haupt, Robert Nowak, Gil M. ...
Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of measurements. The results in the literature have focuse...
Dmitry M. Malioutov, Sujay Sanghavi, Alan S. Wills...
A stylized compressed sensing radar is proposed in which the timefrequency plane is discretized into an N × N grid. Assuming the number of targets K is small (i.e., K N2 ), then ...
We present a CMOS imager with built-in capability to perform Compressed Sensing coding by Random Convolution. It is achieved by a shift register set in a pseudo-random configurat...
Laurent Jacques, Pierre Vandergheynst, Alexandre B...
Recently, there has been growing interest in using compressed sensing to perform imaging. Most of these algorithms capture the image of a scene by taking projections of the imaged ...
Dr. Pradeep Sen is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of New Mexico. He received his B.S. in Computer and Electrical ...
We introduce Xampling, a design methodology for analog compressed sensing in which we sample analog bandlimited signals at rates far lower than Nyquist, without loss of informatio...