Cellular Neural Networks and Visual Computing: Foundations and Applications

ISBN-13: 9780521652476 / Angielski / Twarda / 2002 / 410 str.

Leon O. Chua; Tamas Roska; Tamas Roska

Opis

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. 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. Leon Chua, co-inventor of the CNN, and Tamas Roska are both highly respected pioneers in the field.

Recenzje

"...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

Spis treści

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).