Simulation of a novel, optically addressable, switched-current/continuous-time, current-mode cellular neural network

G. O'Sullivan; E. McCabe; J. Hegarty; P. Horan; B. Kelly; S. Kakizaki

doi:10.1049/el:19950352

Simulation of a novel, optically addressable, switched-current/continuous-time, current-mode cellular neural network

Electronics Letters ◽

10.1049/el:19950352 ◽

1995 ◽

Vol 31 (7) ◽

pp. 563-564 ◽

Cited By ~ 1

Author(s):

G. O'Sullivan ◽

E. McCabe ◽

J. Hegarty ◽

P. Horan ◽

B. Kelly ◽

...

Keyword(s):

Neural Network ◽

Continuous Time ◽

Current Mode ◽

Cellular Neural Network ◽

Switched Current

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A continuous-time cellular neural network chip for direction-selectable connected component detection with optical image acquisition

Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems ◽

10.1109/icmnn.1994.593734 ◽

2002 ◽

Cited By ~ 1

Author(s):

S. Espejo ◽

R. Dominguez-Castro ◽

R. Carmona ◽

A. Rodriguez-Vazquez

Keyword(s):

Neural Network ◽

Continuous Time ◽

Image Acquisition ◽

Cellular Neural Network ◽

Optical Image ◽

Connected Component

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Basic Cellular Neural Networks Image Processing

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch034 ◽

2011 ◽

pp. 218-222

Author(s):

J. Álvaro Fernández

Keyword(s):

Neural Network ◽

Image Processing ◽

Continuous Time ◽

Engineering Application ◽

Cellular Neural Network ◽

Vlsi Circuits ◽

Ann Model ◽

Anatomy And Physiology ◽

Artificial Neural Network Ann ◽

Cellular Computer

Since its seminal publication in 1988, the Cellular Neural Network (CNN) (Chua & Yang, 1988) paradigm have attracted research community’s attention, mainly because of its ability for integrating complex computing processes into compact, real-time programmable analogic VLSI circuits (Rodríguez et al., 2004). Unlike cellular automata, the CNN model hosts nonlinear processors which, from analogic array inputs, in continuous time, generate analogic array outputs using a simple, repetitive scheme controlled by just a few real-valued parameters. CNN is the core of the revolutionary Analogic Cellular Computer, a programmable system whose structure is the so-called CNN Universal Machine (CNN-UM) (Roska & Chua, 1993). Analogic CNN computers mimic the anatomy and physiology of many sensory and processing organs with the additional capability of data and program storing (Chua & Roska, 2002). This article reviews the main features of this Artificial Neural Network (ANN) model and focuses on its outstanding and more exploited engineering application: Digital Image Processing (DIP).

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