This paper describes a parallel algorithm for solving the l(1)-compressive sensing problem. Its design takes advantage of shared memory, vectorized, parallel and many-core microprocessors such as Graphics Processing Units (GPUs) and standard vectorized multi-core processors (e.g. quad-core CPUs). Experiments are conducted on these architectures, showing evidence of the efficiency of our approach
This paper reports a field-programmable gate array (FPGA) design of compressed sensing (CS) using th...
International audienceMixture of Gaussians (MoG) and compressive sensing (CS) are two common approac...
This paper presents a novel real-time compressive sensing (CS) reconstruction which employs high den...
International audienceIn this paper we consider the l 1-compressive sensing problem. We propose an a...
Abstract An application specific programmable processor is designed based on the analysis of a set ...
Compressive Sensing (CS) is a technique which allows a signal to be compressed at the same time as i...
In this paper, a parallel implementation of a previously constrained hyperspectral coded aperture (C...
Compressive sensing (CS) is a new signal processing method, which was developed recent years. CS can...
This paper presents a new parallel implementation of a previously hyperspectral coded aperture (HYCA...
Compressive Sensing (CS) is a novel scheme, in which a signal that is sparse in a known transform do...
For appropriate matrix ensembles, greedy algorithms have proven to be an efficient means of solving ...
Today, a number of applications need to process large bandwidth signals. These applications frequent...
There are presently image sensors based around compressed sensing that apply the fundamental theory ...
Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resol...
This paper presents the design of a system, which can improve the reconstruction of Compressive Sens...
This paper reports a field-programmable gate array (FPGA) design of compressed sensing (CS) using th...
International audienceMixture of Gaussians (MoG) and compressive sensing (CS) are two common approac...
This paper presents a novel real-time compressive sensing (CS) reconstruction which employs high den...
International audienceIn this paper we consider the l 1-compressive sensing problem. We propose an a...
Abstract An application specific programmable processor is designed based on the analysis of a set ...
Compressive Sensing (CS) is a technique which allows a signal to be compressed at the same time as i...
In this paper, a parallel implementation of a previously constrained hyperspectral coded aperture (C...
Compressive sensing (CS) is a new signal processing method, which was developed recent years. CS can...
This paper presents a new parallel implementation of a previously hyperspectral coded aperture (HYCA...
Compressive Sensing (CS) is a novel scheme, in which a signal that is sparse in a known transform do...
For appropriate matrix ensembles, greedy algorithms have proven to be an efficient means of solving ...
Today, a number of applications need to process large bandwidth signals. These applications frequent...
There are presently image sensors based around compressed sensing that apply the fundamental theory ...
Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resol...
This paper presents the design of a system, which can improve the reconstruction of Compressive Sens...
This paper reports a field-programmable gate array (FPGA) design of compressed sensing (CS) using th...
International audienceMixture of Gaussians (MoG) and compressive sensing (CS) are two common approac...
This paper presents a novel real-time compressive sensing (CS) reconstruction which employs high den...