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 Tamàs Roska are both highly respected pioneers in the field. 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. Introduction -- 2. Notation, 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. Leon O. Chua And Tamás Roska. Includes Bibliographical Notes And Bibliography (p. 339-360) And Index. Content: 1. Introduction -- 2. Notation, 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. This is a unique undergraduate level textbook on Cellular Nonlinear/neural Networks (CNN) technology. The many examples and excercises, including a simulator accessible via the Internet, make this book an ideal introduction to CNNs and analogic cellular computing for students, researchers and engineers from a wide range of backgrounds. Abstract: A unique undergraduate level textbook on Cellular Nonlinear/neural Networks (CNN) technology and analogic computing. Read more... Recent history of the electronic and computer industry can be viewed as three waves of revolutionary processes.
