Compressive sensing is a new field in signal processing and applied mathematics. It allows one to simultaneously sample and compress signals which are known to have a sparse representation in a known basis or dictionary ...

This work offers a necessary and sufficient condition for a stationary and ergodic process to be l(p)-compressible in the sense proposed by Amini, Unser and Marvasti ["Compressibility of deterministic and random infinity ...

Compressive sensing has the ability of reconstruction of signal/image from the compressive measurements which are sensed with a much lower number of samples than a minimum requirement by Nyquist sampling theorem. The random ...

A novel technique for radar-imaging inversions is proposed which leverages ideas from the emerging field of compressed sensing. This new method takes advantage of the transform sparsity inherent in natural images. Theoretical ...

Compressed sensing is a novel theory of sampling and reconstruction that has emerged in the past several years. It seeks to leverage the inherent sparsity of natural images to reduce the number of necessary measurements ...

Compressed sensing, a method which relies on sparsity to reconstruct signals with relatively few measurements, provides a new approach to processing radar signals that is ideally suited to detailed imaging and identification ...

Computing the product of the (binary) adjacency matrix of a large graph with a real-valued vector is an important operation that lies at the heart of various graph analysis tasks, such as computing PageRank. In this paper ...

Compressed Sensing (CS) comprises a set of relatively new techniques that exploit the underlying structure of data sets allowing their reconstruction from compressed versions or incomplete information. CS reconstruction ...

Introducción: La resonancia magnética es una técnica de diagnóstico que permite obtener imágenes detalladas de la anatomía y fisiología, sin embargo, su gran desventaja es el largo tiempo de adquisición. La técnica Compressed ...

En este trabajo se construyen matrices determinísticas para compressed sensing basados en códigos lineales. Para cada código lineal se exhibe una matriz de ceros y unos que resulta ser altamente dispersa.El comportamiento ...