Add to ORKGA spatially distributed system for signal sampling and reconstruction consists of huge amounts of small sensing devices with limited computing and telecommunication capabilities. In this paper, we discuss stability of such a sampling/reconstruction system and develop a distributed algorithm for fast reconstruction of high-dimensional signals
The purpose of this paper is to report on recent approaches to reconstruction problems based on anal...
Compressed sensing is a theory which guarantees the exact recovery of sparse signals from a small nu...
Compressed sensing has triggered a major evolution in signal acquisition. It consists of sampling a ...
Digital processing of signals f may start from sampling on a discrete set Γ, f →(f(ϒη))ϒηεΓ. The sam...
Consider the problem of sampling signals which are not bandlimited, but still have a finite number o...
This book presents a survey of the state-of-the art in the exciting and timely topic of compressed s...
This book presents a survey of the state-of-the art in the exciting and timely topic of compressed s...
A new lower bound on the average reconstruction error variance of multidimensional sampling and reco...
We give a new, very general, formulation of the compressed sensing problem in terms of coordinate pr...
Spatiotemporal signal reconstruction from samples randomly gathered in a multidimensional space with...
The purpose of this paper is to report on recent approaches to reconstruction problems based on anal...
Abstract—Spatial sampling is traditionally studied in a static setting where static sensors scattere...
Spatiotemporal signal reconstruction from samples randomly gathered in a multidimensional space with...
This paper studies the problem of reconstructing continuous-time signals from discrete-time uniforml...
Abstract—We study the compressed sensing reconstruction problem for a broad class of random, band-di...
The purpose of this paper is to report on recent approaches to reconstruction problems based on anal...
Compressed sensing is a theory which guarantees the exact recovery of sparse signals from a small nu...
Compressed sensing has triggered a major evolution in signal acquisition. It consists of sampling a ...
Digital processing of signals f may start from sampling on a discrete set Γ, f →(f(ϒη))ϒηεΓ. The sam...
Consider the problem of sampling signals which are not bandlimited, but still have a finite number o...
This book presents a survey of the state-of-the art in the exciting and timely topic of compressed s...
This book presents a survey of the state-of-the art in the exciting and timely topic of compressed s...
A new lower bound on the average reconstruction error variance of multidimensional sampling and reco...
We give a new, very general, formulation of the compressed sensing problem in terms of coordinate pr...
Spatiotemporal signal reconstruction from samples randomly gathered in a multidimensional space with...
The purpose of this paper is to report on recent approaches to reconstruction problems based on anal...
Abstract—Spatial sampling is traditionally studied in a static setting where static sensors scattere...
Spatiotemporal signal reconstruction from samples randomly gathered in a multidimensional space with...
This paper studies the problem of reconstructing continuous-time signals from discrete-time uniforml...
Abstract—We study the compressed sensing reconstruction problem for a broad class of random, band-di...
The purpose of this paper is to report on recent approaches to reconstruction problems based on anal...
Compressed sensing is a theory which guarantees the exact recovery of sparse signals from a small nu...
Compressed sensing has triggered a major evolution in signal acquisition. It consists of sampling a ...