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Resume: Le principal objet de cette communication est de faire une retro perspective succincte de lutilisation de lentropie et du principe du maximum dentropie dans le domaine du traitement du signal. Apr`es un bref rappel de quelques definitions et du principe du maximum dentropie, nous verrons successivement comment lentropie est utilisee en separation de sources, en modelisation de signaux, en analyse spectrale et pour la resolution des probl`emes inverses lineaires. Mots cles : Entropie, Entropie croisee, Distance de Kullback, Information mutuelle, Estimation spectrale, Probl`emes inverses Abstract: The main object of this work is to give a brief overview of the different ways the entropy has been used in signal and image processing. After a short introduction of different quantities related to the entropy and the maximum entropy principle, we will study their use in different fields of signal processing such as: source separation, model order selection, spectral estimation and, finally, general linear inverse problems. Keywords : Entropy, Relative entropy, Kullback distance, Mutual information, Spectral estimation, Inverse problems.
The irreversibility of trajectories in stochastic dynamical systems is linked to the structure of their causal representation in terms of Bayesian networks. We consider stochastic maps resulting from a time discretization with interval tau of signal-
A general method is presented to explicitly compute autocovariance functions for non-Poisson dichotomous noise based on renewal theory. The method is specialized to a random telegraph signal of Mittag-Leffler type. Analytical predictions are compared
The 1-bit compressed sensing framework enables the recovery of a sparse vector x from the sign information of each entry of its linear transformation. Discarding the amplitude information can significantly reduce the amount of data, which is highly b
An algorithm for optimization of signal significance or any other classification figure of merit suited for analysis of high energy physics (HEP) data is described. This algorithm trains decision trees on many bootstrap replicas of training data with
Signal estimation in the presence of background noise is a common problem in several scientific disciplines. An On/Off measurement is performed when the background itself is not known, being estimated from a background control sample. The frequentist