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pro vyhledávání: '"Fosson, Sophie M."'
The development of algorithms for secure state estimation in vulnerable cyber-physical systems has been gaining attention in the last years. A consolidated assumption is that an adversary can tamper a relatively small number of sensors. In the litera
Externí odkaz:
http://arxiv.org/abs/2405.20209
This paper addresses the problem of recursive set-membership identification for linear time varying (LTV) systems when both input and output measurements are affected by bounded additive noise. First we formulate the problem of online computation of
Externí odkaz:
http://arxiv.org/abs/2107.01714
Autor:
Fosson, Sophie M.
Recovering the digital input of a time-discrete linear system from its (noisy) output is a significant challenge in the fields of data transmission, deconvolution, channel equalization, and inverse modeling. A variety of algorithms have been develope
Externí odkaz:
http://arxiv.org/abs/2012.01339
Autor:
Fosson, Sophie M.
The development of online algorithms to track time-varying systems has drawn a lot of attention in the last years, in particular in the framework of online convex optimization. Meanwhile, sparse time-varying optimization has emerged as a powerful too
Externí odkaz:
http://arxiv.org/abs/2001.11939
We consider the problem of the recovery of a k-sparse vector from compressed linear measurements when data are corrupted by a quantization noise. When the number of measurements is not sufficiently large, different $k$-sparse solutions may be present
Externí odkaz:
http://arxiv.org/abs/1909.03705
Autor:
Fosson, Sophie M., Abuabiah, Mohammad
The recovery of signals with finite-valued components from few linear measurements is a problem with widespread applications and interesting mathematical characteristics. In the compressed sensing framework, tailored methods have been recently propos
Externí odkaz:
http://arxiv.org/abs/1905.13181
The sparse linear regression problem is difficult to handle with usual sparse optimization models when both predictors and measurements are either quantized or represented in low-precision, due to non-convexity. In this paper, we provide a novel line
Externí odkaz:
http://arxiv.org/abs/1903.07156
Publikováno v:
In IFAC PapersOnLine 2023 56(2):10390-10395
Autor:
Fosson, Sophie M.
l1 reweighting algorithms are very popular in sparse signal recovery and compressed sensing, since in the practice they have been observed to outperform classical l1 methods. Nevertheless, the theoretical analysis of their convergence is a critical p
Externí odkaz:
http://arxiv.org/abs/1812.02990
Autor:
Fosson, Sophie M.
In this paper, we bring together two trends that have recently emerged in sparse signal recovery: the problem of sparse signals that stem from finite alphabets and the techniques that introduce concave penalties. Specifically, we show that using a mi
Externí odkaz:
http://arxiv.org/abs/1811.03864