scholarly journals The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding versus Dynamic Range

2012 ◽  
Vol 60 (9) ◽  
pp. 4628-4642 ◽  
Author(s):  
Mark A. Davenport ◽  
Jason N. Laska ◽  
John R. Treichler ◽  
Richard G. Baraniuk
Keyword(s):  
Compressive Sensing ◽  
Dynamic Range ◽  
Signal Acquisition ◽  
Wideband Signal ◽  
Pros And Cons ◽  
Noise Folding

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

scholarly journals Compressive sensing: A methodological approach to an efficient signal processing

DYNA ◽  
2015 ◽  
Vol 82 (192) ◽  
pp. 203-210 ◽  
Author(s):  
Evelio Astaiza Hoyos ◽  
Pablo Emilio Jojoa Gómez ◽  
Héctor Fabio Bermúdez Orozco
Keyword(s):  
Signal Processing ◽  
Dimensionality Reduction ◽  
Compressive Sensing ◽  
Methodological Approach ◽  
Signal Acquisition ◽  
Methodological Issues ◽  
New Paradigm ◽  
Signal Acquisition And Processing

Compressive Sensing (CS) is a new paradigm for signal acquisition and processing, which integrates sampling, compression, dimensionality reduction and optimization, which has caught the attention of a many researchers; SC allows the reconstruction of dispersed signals in a given domain from a set of measurements could be described as incomplete, due to that the rate at which the signal is sampled is much smaller than Nyquist's rate. This article presents an approach to address methodological issues in the field of processing signals from the perspective of SC.

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