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 proceedingConference contribution


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 languageEnglish (US)
Title of host publication2018 New Generation of CAS, NGCAS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Print)9781538676813
StatePublished - Dec 10 2018
Externally publishedYes

Bibliographical note

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


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