Concise Signal Models
by Michael Wakin
Publisher: Connexions 2009
Number of pages: 55
This book reviews fundamental concepts underlying the use of concise models for signal processing. Topics are presented from a geometric perspective and include low-dimensional linear, sparse, and manifold-based signal models, approximation, compression, dimensionality reduction, and Compressed Sensing.
Home page url
Download or read it online for free here:
by G. Larry Bretthorst - Springer
This work is a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis.
by Javier Prieto Tejedor (ed.) - InTech
This book takes a look at both theoretical foundations and practical implementations of Bayesian inference. It is intended as an introductory guide for the application of Bayesian inference in the fields of life sciences, engineering, and economics.
by M. Stiber, B.Z. Stiber, E.C. Larson - University of Washington Bothell
The specific topics we will cover include: physical properties of the source information, devices for information capture, digitization, compression, digital signal representation, digital signal processing and network communication.
by Michele Basseville, Igor V. Nikiforov - Prentice-Hall
This book presents mathematical tools and techniques for solving change detection problems in wide domains like signal processing, controlled systems and monitoring. The book is intended for engineers and researchers involved in signal processing.