This paper reports a field-programmable gate array (FPGA) design of compressed sensing (CS) using the orthogonal matching pursuit (OMP) algorithm. While solving the least-squares (LS) problem in the OMP algorithm, the complexity of the matrix inversion operation at every loop is reduced by the proposed partitioned inversion that utilizes the inversion result in the previous iteration. By the proposed matrix (n × n) inversion method inside the OMP, the number of operations is reduced down from O(n3) to O(n2). The OMP algorithm is implemented with a Xilinx Kintex UltraScale. The architecture with the proposed partitioned inversion involves 722 less DSP48E compared with the conventional method. It operates with a sample period of 4 ns, s...
The Shannon-Nyquist theorem enables signal acquisition with sampling frequency greater than or equal...
Compressive sensing(CS) is an emerging research field that has applications in signal processing, er...
Compressed Sensing (CS) is an elegant technique to acquire signals and reconstruct them efficiently ...
International audienceIn this paper, we present a novel architecture based on field-programmable gat...
Conventional sensing techniques often acquire the signals entirely using a lot of resources and then...
Today, a number of applications need to process large bandwidth signals. These applications frequent...
Orthogonal matching pursuit (OMP) is the most efficient algorithm used for the reconstruction of com...
Compressive Sensing (CS) is a novel scheme, in which a signal that is sparse in a known transform do...
This paper presents a novel real-time compressive sensing (CS) reconstruction which employs high den...
Abstract Compressed sensing‐based radio frequency signal acquisition systems call for higher reconst...
The conventional Shannon-Nyquist sampling theory sets the goal for a signal to be sampled at a rate ...
In this paper is presented a novel area efficient Fast Fourier transform (FFT) for real-time compres...
The theory and applications on Compressed Sensing is a promising, quickly developing area which garn...
Wireless monitoring of physiological signals is an evolving direction in personalized medicine and h...
Compressive sensing has opened up a new path to reconstruct images from a number of samples which is...
The Shannon-Nyquist theorem enables signal acquisition with sampling frequency greater than or equal...
Compressive sensing(CS) is an emerging research field that has applications in signal processing, er...
Compressed Sensing (CS) is an elegant technique to acquire signals and reconstruct them efficiently ...
International audienceIn this paper, we present a novel architecture based on field-programmable gat...
Conventional sensing techniques often acquire the signals entirely using a lot of resources and then...
Today, a number of applications need to process large bandwidth signals. These applications frequent...
Orthogonal matching pursuit (OMP) is the most efficient algorithm used for the reconstruction of com...
Compressive Sensing (CS) is a novel scheme, in which a signal that is sparse in a known transform do...
This paper presents a novel real-time compressive sensing (CS) reconstruction which employs high den...
Abstract Compressed sensing‐based radio frequency signal acquisition systems call for higher reconst...
The conventional Shannon-Nyquist sampling theory sets the goal for a signal to be sampled at a rate ...
In this paper is presented a novel area efficient Fast Fourier transform (FFT) for real-time compres...
The theory and applications on Compressed Sensing is a promising, quickly developing area which garn...
Wireless monitoring of physiological signals is an evolving direction in personalized medicine and h...
Compressive sensing has opened up a new path to reconstruct images from a number of samples which is...
The Shannon-Nyquist theorem enables signal acquisition with sampling frequency greater than or equal...
Compressive sensing(CS) is an emerging research field that has applications in signal processing, er...
Compressed Sensing (CS) is an elegant technique to acquire signals and reconstruct them efficiently ...