Sciences	 		 			 Teaching
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Knowledge shall be accessible to all...

One will find on this page some details on the various courses I have been teaching over the years. Similar to some famous

institution such as MIT, I consider knowledge should be shared. I am concerned these days about our education system and

our universities, where they are operated as for-profit private companies and where training of future generations is

reduced to a mere business.  Trends show that nowaday education is simply exchanged against money.

This is sad, because knowledge makes people free and are necessary to self realization. Knowledge insures societies to

evolve and is a warranty to a healthy democracy. I have therefore no objection to share my personal lectures to anyone,

as long as my copyrights are respected and recognized.

Digital signal processing (in French) 

This is an undergraduate course on digital signal processing I have been giving  for a few years. Those chapters summarize

my lectures.

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Stochastic processes and statistical signal processing of random signals

This a graduate course specialized on stochastic processes, bayesian theory and statistical signal processing of random

signals and noise. I give this course since 2002, which has been improved over the years. The course is divided in 15 weeks. 

The course (in French) is intensive and aims to provide the students with the basic knowledge in the field using a toolbox

approach. The details are below.


Notions of probability, random variables and random vectors

Stochastic processes

Memoryless stochastic processes

Stochastic processes with memory

Transformations of a stochastic process


Detection and hypothesis testing


Estimation theory part I

Optimal filtering 

Estimation theory part II

Spectrum estimation



Intelligent vehicles and Intelligent transportation Systems

This a graduate course specialized on intelligent vehicles, ADAS and Intelligent transportation systems (ITS). I built that

course in 2015 while I was working at Cal Poly in San Luis Obispo California. This course written in English is divided in 10

weeks and provides an in-depth overview of the field with all the relevent knowledge using a toolbox approach. The

details are given below.

Introduction, context and applications

Vehicle dynamic modelling

Vehicle positionning and navigation

Perception and map building

Multi-sensor data fusion

Object detection, recognition and tracking

Vehicular control and ADAS systems

VANETS and connected vehicles

Multi vehicular cooperative and collaborative architectures

Autonomous vehicle and driving automation

Reliability, failure detection and identification

Vehicular infotainment and telematics

Vehicle self-diagnosis and maintenance issues

Driver modelling and human machine interactions

Intelligent vehicle testing validation and certification

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