## Cellular Neural Networks and Visual Computing: Foundations and ApplicationsCellular 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. |

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### Contents

1 | |

7 | |

3 Characteristics and analysis of simple CNN templates | 35 |

4 Simulation of the CNN dynamics | 100 |

5 Binary CNN characterization via Boolean functions | 115 |

unified theoryand applications | 139 |

7 Introduction to the CNN Universal Machine | 183 |

Nonlinear dynamics and complete stability | 205 |

12 Coupled CNN with linear synaptic weights | 276 |

13 Uncoupled standard CNNs with nonlinear synaptic weights | 290 |

14 Standard CNNs with delayed synaptic weights and motion analysis | 296 |

15 Visual microprocessors analog and digital VLSI implementation of the CNN Universal Machine | 303 |

16 CNN models in the visual pathwayand the Bionic Eye | 320 |

Notes | 339 |

348 | |

Exercises | 361 |

### Other editions - View all

Cellular Neural Networks and Visual Computing: Foundations and Applications Leon O. Chua,Tamas Roska No preview available - 2005 |

### Common terms and phrases

algorithm analogic CNN binary image black pixel Boolean function boundary conditions C(ij cell C(i cell-linking Cellular Neural Networks Chapter Circuit Theory Circuits and Systems cloning template CNN cell CNN template CNN truth table CNN Universal Machine completely stable computing converge corresponding defined denotes Digital Signal Processing dynamic route equilibrium point Fundamental Theory given Global task gray-scale Hence IEEE Transactions ij x ij implementation input image input output International Journal Journal of Circuit L.O. Chua layer linearly separable LOGAND logic Mathematical analysis maxterm microprocessor minterm Monostable neighborhood neighbors neuron node nonlinear operation optimal parameter pattern processors receptive field retina Roska rule shifted DP plot shown in Fig signal flow graph simulation slope solution standard CNN synaptic weight Theorem Theory and Applications trajectory Transactions on Circuits transient uncoupled CNN visual microprocessors white pixel zero