Beschreibung:
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).
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).
1. BACKGROUND AND INTRODUCTION2. DISCRETE TIME SIGNALS AND SYSTEMS3. DISCRETE TIME SYSTEMS IN THE FREQUENCY DOMAIN4. THE Z-TRANSFORM5. DISCRETE FILTER DESIGN TECHNIQUES6. COMPUTING THE DFT7. MULTIRATE SIGNAL PROCESSING AND DEVICES8. INTRODUCTION TO STOCHASTIC PROCESSES9. WEINER FILTERS10. ADAPTIVE FILTERS11. FURTHER READING