Digital Signal Processing with Matlab Examples, Volume 3

This is the third volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs.

This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed.

The book primarily focuses on filter banks, wavelets, and images. While the Fourier transform is adequate for periodic signals, wavelets are more suitable for other cases, such as short-duration signals: bursts, spikes, tweets, lung sounds, etc. Both Fourier and wavelet transforms decompose signals into components. Further, both are also invertible, so the original signals can be recovered from their components. Compressedsensing has emerged as a promising idea. One of the intended applications is networked devices or sensors, which are now becoming a reality; accordingly, this topic is also addressed. A selection of experiments that demonstrate image denoising applications are also included. In the interest of reader-friendliness, the longer programs have been grouped in an appendix; further, a second appendix on optimization has been added to supplement the content of the last chapter.

Offers a wealth of MATLAB programs for individual exploration Treats the theory succinctly and with a focus on practical applications Covers advanced filtering techniques and compressed sensing Includes supplementary material: sn.pub/extras

Autorentext

Prof. Jose M. Giron-Sierrawas born in Valladolid, Spain. He receive his Ph.D. in Physics in 1978, Universidad Complutense de Madrid, Spain. Prof. Giron-Sierra wrote more than 160 publications in various international journals. He is IEEE, AIAA, and Eurosim member and belongs to two IFAC Technical Committees.

Inhalt

Part VI- Model-based Actions: Filtering, Prediction, Smoothing.- Kalman Filter, Particle Filter and other Bayesian Filters.- Part VII Sparse Representation. Compressed Sensing.- Sparse Representations.- Appendices.- Selected Topics of Mathematical Optimization.- Long Programs.