Add to ORKGThe paper discusses a novel frequency interpolation and super-resolution method for multitone waveform analysis, where a compressive sensing algorithm is employed to process data. Each signal acquisition involves a short data record, whose DFT coefficients are computed. A set of compressed measurements is obtained by taking records with different known starting instants, and employed to determine, by solving an orthogonal matching pursuit problem, the set of frequency components of the analysed waveform. Interpolation is presented as a compressed sensing problem and algorithm performances discussed
Conventional approach in acquisition and reconstruction of images from frequency domain strictly fol...
AbstractCompressive sensing (CS) has recently emerged as a framework for efficiently capturing signa...
Compressive Sensing (CS) is a new sensing paradigm which permits sampling of a signal at its intrins...
The paper discusses a novel frequency interpolation and super-resolution method for multitone wavefo...
Accurate measurement of a multisine waveform is a classic spectral analysis problem. Algorithms base...
The problem of resolving frequency components close to the Rayleigh threshold, while using time-doma...
Abstract. In this paper, we propose a low-cost algorithm for recovering multitone signals from compr...
In this paper we propose a method based on compressed sensing (CS) for estimating the spectrum of a...
In this work we propose a method based on compressive sensing (CS) for estimating the spectrum of a...
Compressed sensing (CS) is a theory that allows us to recover sparse or compressible signals from a...
This thesis develops algorithms and applications for compressive sensing, a topic in signal processi...
Spectral peak search is an essential part of the frequency domain parametric method. In this paper, ...
The classical shift retrieval problem considers two signals in vector form that are related by a shi...
While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit ...
We describe a method of integrating Karhunen-Loeve Transform (KLT) into compressive sensing, which c...
Conventional approach in acquisition and reconstruction of images from frequency domain strictly fol...
AbstractCompressive sensing (CS) has recently emerged as a framework for efficiently capturing signa...
Compressive Sensing (CS) is a new sensing paradigm which permits sampling of a signal at its intrins...
The paper discusses a novel frequency interpolation and super-resolution method for multitone wavefo...
Accurate measurement of a multisine waveform is a classic spectral analysis problem. Algorithms base...
The problem of resolving frequency components close to the Rayleigh threshold, while using time-doma...
Abstract. In this paper, we propose a low-cost algorithm for recovering multitone signals from compr...
In this paper we propose a method based on compressed sensing (CS) for estimating the spectrum of a...
In this work we propose a method based on compressive sensing (CS) for estimating the spectrum of a...
Compressed sensing (CS) is a theory that allows us to recover sparse or compressible signals from a...
This thesis develops algorithms and applications for compressive sensing, a topic in signal processi...
Spectral peak search is an essential part of the frequency domain parametric method. In this paper, ...
The classical shift retrieval problem considers two signals in vector form that are related by a shi...
While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit ...
We describe a method of integrating Karhunen-Loeve Transform (KLT) into compressive sensing, which c...
Conventional approach in acquisition and reconstruction of images from frequency domain strictly fol...
AbstractCompressive sensing (CS) has recently emerged as a framework for efficiently capturing signa...
Compressive Sensing (CS) is a new sensing paradigm which permits sampling of a signal at its intrins...