Emeritus Professor Univ. Grenoble-Alpes
From 1982 to 1989, I was associate-professor at ENSERG, electrical engineering department of Institut National Polytechnique de Grenoble. Since 1989, I am full professor at Université Joseph Fourier (UJF) in Grenoble which became University Grenoble-Alpes in 2016. Although my main academic duties were in Polytech’Grenoble in the electrical engineering department (IESE), I also presented lectures for graduate students preparing PhD (DEA and Master research) especially in Cognitive Sciences. Since September 2019, I am emeritus professor at Univ. Grenoble-Alpes, without regular academic duties.

In addition to my academic charge, I frequently presented lectures in other French universities (UFR IMA Grenoble, ENST Bretagne) and foreigner universities : Politechnico di Torino (Italy), Universitat Politechnica de Catalunya et Universitat de Valencia (Spain), Univ. of Campinas (Brazil), Sharif University of Technology (Iran), etc., mainly for MSc or PhD students and in relation with my research activities.  

Next paragraphs provide more information concerning my academic charge during the last years. The lecture documents and slides (in French) given to my students can be downloaded in this directory

    Design of analog systems

Devoted for (first year of Master) students in electrical engineering  (3i), this lecture on advance electronics aims to use theoretical knowledges for the design of a reliable electronical device, which is a key practical problem for a future engineer. This lecture mainly tries to answer to the following question: how design an electronic system which is as independent as possible of component parameters and external conditions, like power supply and temperature. I stopped to teach this lecture in 2007.

    Nonlinear servo-control

Devoted to last year students (second year of Master in electrical engineering) of 3i, option Automatic Control, this lecture is focused on nonlinear servo-controls, addressed using first harmonic method, and phase space method. The latter method is particularly interesting since it can be applied for studying stability of both physical systems and algorithms. I stopped to teach this lecture in 2000.

    Signal theory

This lecture is focused on mathematical tools for representing and analysing signals, deterministic as well as random. First, I present to the students simple applications in signal and image processing, for showing that they use everyday signal processing  tools. For instance, I speak them about spreading spectrum communications, IRM imaging, EEG and ECG, watermarking, noise cancellation and source separation. After the description of mathematical tools for both deterministic and random signals, the last chapter of this lecture presents usual principles of signal processing, like correlation methods, matched filtering and Wiener filtering.

    Optimal linear filtering

This lecture addresses the design of linear optimal filters, optimal in the sense of mean square error minimization. For sake of its shortness, the problem is restricted to discrete time signals and filters. After explaining principles of Wiener filtering, I show how  we can design practical algorithms, like LMS and RLS, including convergence study. The last chapter is focused on Kalman filter, which is illustrated by an application for 50 Hz power line rejection developed in the laboratory by one of my PhD students, Dr. R. Sameni.

    Detection, estimation and information theories

This lecture presents basics of statistical theories of decision: Bayes criterion, Neyman-Pearson criterion, Minimax de Bayes, de Neyman-Pearson, Minimax, and estimation: least mean square estimatiion, maximum a posteriori, maximum likelihood, Cramer-Rao bounds. The last part of the lecture concerns information theory: Shannon entropies and mutual information, Kraft and McMillan theorems, Shannon-Fano and Hufmann codes. This lecture (20 hours) is illustrated with 20 hours of exercices and problems in signal processing, communications, pattern recognition or neural networks. 

    Artificial neural networks and applications  

This lecture is a panorama of main neural algorithms (MLP, Kohonen self-organizing maps, Hopfield models, source separation), illustrated by applications in signal and image processing: image denoising and filtering, image compression, classification, pattern recognition, identification, prediction, estimation, source separation, etc. I especially insist on differences, advantages and drawbacks, between neural methods and classical methods in classification, identification, vectorial quantization, etc. This lecture was been first funded in the framework of an European project, in 1990, and presented in various cities in Europe (Louvain, Lausanne, Torino, Barcelona, Granada) and then presented to MSc students in electrical engineering and in cognitive sciences.

Grenoble Images Parole Signal Automatique laboratoire

UMR 5216 CNRS - Grenoble INP - Université Joseph Fourier - Université Stendhal