Book contents
- Frontmatter
- Contents
- Acknowledgements
- 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
- 15 Visual microprocessors – analog and digital VLSI implementation of the CNN Universal Machine
- 16 CNN models in the visual pathway and the “Bionic Eye”
- Notes
- Bibliography
- Exercises
- Appendices
- Index
Appendices
Published online by Cambridge University Press: 28 May 2010
- Frontmatter
- Contents
- Acknowledgements
- 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
- 15 Visual microprocessors – analog and digital VLSI implementation of the CNN Universal Machine
- 16 CNN models in the visual pathway and the “Bionic Eye”
- Notes
- Bibliography
- Exercises
- Appendices
- Index
Summary
Appendix A: TEMLIB, a CNN Template Library
Under the name TEMLIB, within the Software Library for analogic cellular (CNN) computers, a set of fairly standard types of CNN template data are contained. The template names in TEMLIB can be used in the template and algorithm simulators defined in Appendix B.
Appendix B: TEMPO, template optimization tools
Under the name TEMMASTER, a student version of a program for template optimization and design is available. It is used mainly for Boolean CNN and for robust template design.
Appendix C: CANDY, a simulator for CNN templates and analogic CNN algorithms
Under the name CANDY (CNN Analogic Dynamics), a student version of a software simulator system is available. Multi-layer CNN templates as well as analogic CNN algorithms (defined on the CNN Universal Machine having a one layer, first-order dynamics CNN core) can be simulated. An easy to use Template Runner program as well as a high-level language compiler (Alpha) help the user to analyze complex spatial-temporal dynamics easily and with expressive visualization tools.
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- Information
- Cellular Neural Networks and Visual ComputingFoundations and Applications, pp. 389Publisher: Cambridge University PressPrint publication year: 2002