Signaux aléatoires

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    In brief

  • Code : N7EE10A


The objective of this course is to review some concepts investigated in the first year of EEEA for the analysis of deterministic signals and to study how they can be generalized to random signals (also called random processes or stochastic processes). These generalizations include 1) the definition of autocorrelation functions and power spectral densities, 2) Linear filtering, 3) Sampling, 4) Nonlinear filtering. A specific attention will be devoted to define specific filters widely used in signal and image processing, namely the matched filter (for telecommunications) and the Wiener filter (for image processing). Part of the course will also address some properties of Poisson processes.


-          Spectral analysis (autocorrelation and spectral density), filtering and sampling of random signals with a particular attention to the matched filter and the Wiener filter.

-          Non-linear transformations of random signals.

-          Poisson processes


  • A.        Papoulis and S. U. Pillai, Probability, Random Variables and             Stochastic Processes, 4th             edition, McGraw-Hill Higher Education, New-York, NY USA, 2002.
  • J. Max and      J.-L. Lacoume, Méthodes et Techniques de Traitement du Signal, 5ème     édition, Dunod, Paris France, 2004.


The National Institute of Electrical engineering, Electronics, Computer science,Fluid mechanics & Telecommunications and Networks

2, rue Charles Camichel - BP 7122
31071 Toulouse Cedex 7, France

+33 (0)5 34 32 20 00


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