Cellular Neural Networks and Visual Computing
Foundations and Applications

A unique undergraduate level textbook on Cellular Nonlinear/neural Networks (CNN) technology and analogic computing.

Leon O. Chua (Author), Tamas Roska (Author)

9780521018630, Cambridge University Press

Paperback, published 22 August 2005

412 pages, 50 tables 36 exercises
25.4 x 17.8 x 2.4 cm, 1.008 kg

"...rarely has a treatment of a new technology been so thoroughly researched and presented within the confines of a single book...an outstanding example of what a team of dedicated authors and a committed publisher can do towards exposing their potential readers to new technologies and development of new industries." Current Engineering Practice

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 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.

1. Once over lightly
2. Introduction - 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).

Subject Areas: Neural networks & fuzzy systems [UYQN]