This book describes the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian approach; hidden Markov models; and metaheuristics (genetic algorithms, ant colony algorithms, cross-entropy, particle swarm optimization, estimation of distribution algorithms, and artificial immune systems).
Table des matières
1. BACKGROUND AND INTRODUCTION
2. DISCRETE TIME SIGNALS AND SYSTEMS
3. DISCRETE TIME SYSTEMS IN THE FREQUENCY DOMAIN
4. THE Z-TRANSFORM
5. DISCRETE FILTER DESIGN TECHNIQUES
6. COMPUTING THE DFT
7. MULTIRATE SIGNAL PROCESSING AND DEVICES
8. INTRODUCTION TO STOCHASTIC PROCESSES
9. WEINER FILTERS
10. ADAPTIVE FILTERS
11. FURTHER READING
A propos de l’auteur
Patrick Siarry is a Professor of Automatics and Informatics at the University of Paris-Est Créteil, where he leads the Image and Signal Processing team in the Laboratoire Images, Signaux et Systèmes Intelligents – Li SSi.
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