A low voltage CMOS implementation of a linear cellular neural network for image processing applications

2003 ◽  
Author(s):  
F. Lobato-Lopez ◽  
J.L. Finol
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Low Voltage ◽  
Cellular Neural Network ◽  
Cmos Implementation

Basic Cellular Neural Networks Image Processing

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).

Dense CMOS implementation of a binary-programmable cellular neural network

2006 ◽  
Vol 34 (4) ◽  
pp. 429-443 ◽  
Author(s):  
Jacek Flak ◽  
Mika Laiho ◽  
Ari Paasio ◽  
Kari Halonen
Keyword(s):  
Neural Network ◽  
Cellular Neural Network ◽  
Cmos Implementation

scholarly journals Modular Cellular Neural Network Structure for Wave-Computing-Based Image Processing

ETRI Journal ◽  
2013 ◽  
Vol 35 (2) ◽  
pp. 207-217 ◽  
Author(s):  
Mojtaba Karami ◽  
Reza Safabakhsh ◽  
Mohammad Rahmati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Network Structure ◽  
Cellular Neural Network ◽  
Neural Network Structure

Advanced Cellular Neural Networks Image Processing

2011 ◽  
pp. 45-50
Author(s):  
J. Álvaro Fernández
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Cellular Neural Network ◽  
Research Community ◽  
Vlsi Circuits ◽  
Anatomy And Physiology ◽  
The Core ◽  
Scientific Papers ◽  
Artificial Neural Network Ann ◽  
Cellular Computer

Since its introduction to the research community in 1988, the Cellular Neural Network (CNN) (Chua & Yang, 1988) paradigm has become a fruitful soil for engineers and physicists, producing over 1,000 published scientific papers and books in less than 20 years (Chua & Roska, 2002), mostly related to Digital Image Processing (DIP). This Artificial Neural Network (ANN) offers a remarkable ability of integrating complex computing processes into compact, real-time programmable analogic VLSI circuits as the ACE16k (Rodríguez et al., 2004) and, more recently, into FPGA devices (Perko et al., 2000). CNN is the core of the revolutionary Analogic Cellular Computer (Roska et al., 1999), a programmable system based on the so-called CNN Universal Machine (CNN-UM) (Roska & Chua, 1993). Analogic CNN computers mimic the anatomy and physiology of many sensory and processing biological organs (Chua & Roska, 2002). This article continues the review started in this Encyclopaedia under the title Basic Cellular Neural Network Image Processing.

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