Projected-gradient-descent in rakeness-based compressed sensing with disturbance rejection

Mauro Mangia; Letizia Magenta; Alex Marchioni; Fablo Pareschi; Riccardo Rovatti; Gianluca Setti

doi:10.1109/NGCAS.2018.8572115

Projected-gradient-descent in rakeness-based compressed sensing with disturbance rejection

Mauro Mangia, Letizia Magenta, Alex Marchioni, Fablo Pareschi, Riccardo Rovatti, Gianluca Setti

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Compressed Sensing (CS) has recently emerged as an effective tool to simultaneously acquire and compress analog waveforms in low-resource sensing devices. Its mechanisms have been also extended by both adapting the sensing stage to the actual class of input signals, and granting it the ability to reject disturbances. Regrettably, the resulting design flow entails the solution of two optimization problems with a potentially huge number of variables. This work overcomes this impasse by proposing a Project-Gradient-Descend method algorithm that drastically reduces the required CPU time to obtain a solution.

Original language	English (US)
Title of host publication	2018 New Generation of CAS, NGCAS 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	78-81
Number of pages	4
ISBN (Print)	9781538676813
DOIs	https://doi.org/10.1109/NGCAS.2018.8572115
State	Published - Dec 10 2018
Externally published	Yes

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Generated from Scopus record by KAUST IRTS on 2023-02-15

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title = "Projected-gradient-descent in rakeness-based compressed sensing with disturbance rejection",

abstract = "Compressed Sensing (CS) has recently emerged as an effective tool to simultaneously acquire and compress analog waveforms in low-resource sensing devices. Its mechanisms have been also extended by both adapting the sensing stage to the actual class of input signals, and granting it the ability to reject disturbances. Regrettably, the resulting design flow entails the solution of two optimization problems with a potentially huge number of variables. This work overcomes this impasse by proposing a Project-Gradient-Descend method algorithm that drastically reduces the required CPU time to obtain a solution.",

author = "Mauro Mangia and Letizia Magenta and Alex Marchioni and Fablo Pareschi and Riccardo Rovatti and Gianluca Setti",

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T1 - Projected-gradient-descent in rakeness-based compressed sensing with disturbance rejection

AU - Mangia, Mauro

AU - Magenta, Letizia

AU - Marchioni, Alex

AU - Pareschi, Fablo

AU - Rovatti, Riccardo

AU - Setti, Gianluca

N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15

PY - 2018/12/10

Y1 - 2018/12/10

N2 - Compressed Sensing (CS) has recently emerged as an effective tool to simultaneously acquire and compress analog waveforms in low-resource sensing devices. Its mechanisms have been also extended by both adapting the sensing stage to the actual class of input signals, and granting it the ability to reject disturbances. Regrettably, the resulting design flow entails the solution of two optimization problems with a potentially huge number of variables. This work overcomes this impasse by proposing a Project-Gradient-Descend method algorithm that drastically reduces the required CPU time to obtain a solution.

AB - Compressed Sensing (CS) has recently emerged as an effective tool to simultaneously acquire and compress analog waveforms in low-resource sensing devices. Its mechanisms have been also extended by both adapting the sensing stage to the actual class of input signals, and granting it the ability to reject disturbances. Regrettably, the resulting design flow entails the solution of two optimization problems with a potentially huge number of variables. This work overcomes this impasse by proposing a Project-Gradient-Descend method algorithm that drastically reduces the required CPU time to obtain a solution.

UR - https://ieeexplore.ieee.org/document/8572115/

UR - http://www.scopus.com/inward/record.url?scp=85060203124&partnerID=8YFLogxK

U2 - 10.1109/NGCAS.2018.8572115

DO - 10.1109/NGCAS.2018.8572115

M3 - Conference contribution

SN - 9781538676813

SP - 78

EP - 81

BT - 2018 New Generation of CAS, NGCAS 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Projected-gradient-descent in rakeness-based compressed sensing with disturbance rejection

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