TY - GEN
T1 - Analog-to-information conversion of sparse and non-white signals: Statistical design of sensing waveforms
AU - Mangia, Mauro
AU - Rovatti, Riccardo
AU - Setti, Gianluca
N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15
PY - 2011/8/2
Y1 - 2011/8/2
N2 - Analog to Information conversion is a new paradigm in signal digitalization. In this framework, compressed sensing theory allows to reconstruct sparse signal from a limited number of measures. In this work, we will assume that the signal is not only sparse but also localized in a given domain, so that its energy is concentrated in a subspace. We will present a formal and quantitative discussion to explain how localization of sparse signals can be exploited to improve the quality of the reconstructed signal. © 2011 IEEE.
AB - Analog to Information conversion is a new paradigm in signal digitalization. In this framework, compressed sensing theory allows to reconstruct sparse signal from a limited number of measures. In this work, we will assume that the signal is not only sparse but also localized in a given domain, so that its energy is concentrated in a subspace. We will present a formal and quantitative discussion to explain how localization of sparse signals can be exploited to improve the quality of the reconstructed signal. © 2011 IEEE.
UR - http://ieeexplore.ieee.org/document/5938019/
UR - http://www.scopus.com/inward/record.url?scp=79960854398&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2011.5938019
DO - 10.1109/ISCAS.2011.5938019
M3 - Conference contribution
SN - 9781424494736
SP - 2129
EP - 2132
BT - Proceedings - IEEE International Symposium on Circuits and Systems
ER -