Cellular Neural Networks and Visual Computing

Foundations and Applications

Leon O. Chua, Tamas Roska

396 pages, parution le 24/05/2002

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Résumé

Cellular Nonlinear/neural Network (CNN) technology is both a revolutionary concept and an experimentally proven new computing paradigm. Analogic cellular computers based on CNNs are set to change the way analog signals are processed and are paving the way to an entire new analog computing industry. This unique undergraduate level textbook includes many examples and exercises, including CNN simulator and development software accessible via the Internet. It is an ideal introduction to CNNs and analogic cellular computing for students, researchers and engineers from a wide range of disciplines. Although its prime focus is on visual computing, the concepts and techniques described in the book will be of great interest to those working in other areas of research including modeling of biological, chemical and physical processes. Leon Chua, co-inventor of the CNN, and Tamás Roska are both highly respected pioneers in the field.

Contents

1. Introduction
2. Notations, definitions and mathematical foundation
3. Characteristics and analysis of simple CNN templates
4. Simulation of the CNN dynamics
5. Binary CNN characterization via Boolean functions
6. Uncoupled CNNs: unified theory and applications
7. Introduction to the CNN universal machine
8. Back to basics: nonlinear dynamics and complete stability
9. The CNN universal machine (CNN - UM)
10. Template design tools
11. CNNs for linear image processing
12. Coupled CNN with linear synaptic weights
13. Uncoupled standard CNNs with nonlinear synaptic weights
14. Standard CNNs with delayed synaptic weights and motion analysis
15. Visual microprocessors - analog and digital VLSI implementation of the CNN universal machine
16. CNN models in the visual pathway and the ‘bionic eye'
Appendix A. A CNN template library
Appendix B. Using a simple multi-layer CNN analogic dynamic template and algorithm simulator (CANDY)
Appendix C. A program for binary CNN template design and optimization (TEMPO)

Caractéristiques techniques

	PAPIER
Éditeur(s)	Cambridge University Press
Auteur(s)	Leon O. Chua, Tamas Roska
Parution	24/05/2002
Nb. de pages	396
Format	18 x 25,5
Couverture	Relié
Poids	1001g
Intérieur	Noir et Blanc
EAN13	9780521652476
ISBN13	978-0-521-65247-6