Cellular Neural Networks and Visual Computing
Cellular Neural Networks and Visual Computing
Tamas Roska, Hungarian Academy of Sciences, Budapest
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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.
- Undergraduate Cellular Neural Network textbook
- Web link to CNN simulation tools
- Author Leon Chua uniquely qualified as inventor of CNNs
Reviews & endorsements
"...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
Product details
August 2005Paperback
9780521018630
412 pages
254 × 178 × 24 mm
1.008kg
50 tables 36 exercises
Available
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).
