Cellular Neural Networks and Visual Computing: Foundations and Applications(平裝)

Description

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.

• First undergraduate Cellular Neural Network textbook

• Web link to CNN simulation tools

• Author Leon Chua uniquely qualified as inventor of CNNs

Table of Contents

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).