Digital Signal and Image Processing using Matlab® – 2nd edition
Volume 3 – Advances and Applications: The Stochastic Case
Publication Date: October 2015 Hardback 362 pp.
Volume 3 of the second edition of the fully revised and updated Digital Signal and Image Processing using MATLAB®, after the first two volumes on the fundamentals and applications of Image and Signal Processing, focuses on the stochastic case. It will be of particular benefit to readers who already possess a good knowledge of MATLAB® and a command of the fundamental elements of digital signal processing, who are familiar with the fundamentals of continuous-spectrum spectral analysis and who have a certain mathematical knowledge concerning Hilbert spaces.
This volume focuses on applications but also provides a good presentation of the principles. A number of elements closer in nature to statistics than to signal processing itself are widely discussed. This choice comes from a current tendency of signal processing to use techniques from this field.
More than 200 programs and functions are provided in the MATLAB® language, with useful comments and guidance, to enable numerical experiments to be carried out, thus allowing readers to develop a deeper understanding of both the theoretical and practical aspects of this subject.
1. Mathematical Concepts.
2. Statistical Inferences.
3. Monte-Carlo Simulation.
4. Second Order Stationary Process.
5. Inferences on HMM.
6. Selected Topics.
7. Hints and Solutions.
About the Authors
Gérard Blanchet is Professor at Telecom ParisTech, France. In addition to his research, teaching and consulting activities, he is the author of several books on automatic control systems, digital signal processing and computer architecture. He also develops tools and methodologies to improve knowledge acquisition in various fields.
Maurice Charbit is Professor at Telecom ParisTech, France. He is a teacher in probability theory, signal processing, communication theory and statistics for data processing. With regard to research, his main areas of interest are: (i) the Bayesian approach for hidden Markov models, (ii) the 3D model-based approach for face tracking, and (iii) processing for multiple sensor arrays with applications to infrasonic systems.