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.

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

Mangia, Mauro;Marchioni, Alex;Rovatti, Riccardo;
2018

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.
2018 New Generation of CAS, NGCAS 2018
78
81
Mangia, Mauro; Magenta, Letizia; Marchioni, Alex; Pareschi, Fabio; Rovatti, Riccardo; Setti, Gianluca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/659699
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