scholarly journals Assembly of a 3D Cellular Computer Using Folded E-Blocks

Micromachines ◽  
2016 ◽  
Vol 7 (5) ◽  
pp. 78 ◽  
Author(s):  
Shivendra Pandey ◽  
Nicholas Macias ◽  
Carmen Ciobanu ◽  
ChangKyu Yoon ◽  
Christof Teuscher ◽  
...  
Keyword(s):  
Cellular Computer

The cellular computer DNA: Program or data

1990 ◽  
Vol 52 (3) ◽  
pp. 335-348 ◽  
Author(s):  
H ATLAN ◽  
M KOPPEL
Keyword(s):  
Cellular Computer

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).

scholarly journals RECEPTIVE FIELD ATLAS AND RELATED CNN MODELS

2004 ◽  
Vol 14 (02) ◽  
pp. 551-584 ◽  
Author(s):  
V. GÁL ◽  
J. HÁMORI ◽  
T. ROSKA ◽  
D. BÁLYA ◽  
ZS. BOROSTYÁNKŐI ◽  
...  
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Nervous System ◽  
Receptive Field ◽  
Computer Architecture ◽  
Processing Mode ◽  
Universal Machine ◽  
Logic Signal ◽  
Cellular Computer ◽  
Different Parts

In this paper we demonstrate the potential of the cellular nonlinear/neural network paradigm (CNN) that of the analogic cellular computer architecture (called CNN Universal Machine — CNN-UM) in modeling different parts and aspects of the nervous system. The structure of the living sensory systems and the CNN share a lot of features in common: local interconnections ("receptive field architecture"), nonlinear and delayed synapses for the processing tasks, the potentiality of feedback and using the advantages of both the analog and logic signal-processing mode. The results of more than ten years of cooperative work of many engineers and neurobiologists have been collected in an atlas: what we present here is a kind of selection from these studies emphasizing the flexibility of the CNN computing: visual, tactile and auditory modalities are concerned.

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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