Dense CMOS implementation of a binary-programmable cellular neural network

Jacek Flak; Mika Laiho; Ari Paasio; Kari Halonen

doi:10.1002/cta.365

Dense CMOS implementation of a binary-programmable cellular neural network

International Journal of Circuit Theory and Applications ◽

10.1002/cta.365 ◽

2006 ◽

Vol 34 (4) ◽

pp. 429-443 ◽

Cited By ~ 12

Author(s):

Jacek Flak ◽

Mika Laiho ◽

Ari Paasio ◽

Kari Halonen

Keyword(s):

Neural Network ◽

Cellular Neural Network ◽

Cmos Implementation

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Cmos implementation of a cellular neural network with dynamically alterable cloning templates

Lecture Notes in Computer Science - Artificial Neural Networks ◽

10.1007/bfb0035902 ◽

2006 ◽

pp. 260-267 ◽

Cited By ~ 1

Author(s):

M. Anguita ◽

A. Prieto ◽

F. J. Pelayo ◽

J. Ortega ◽

A. Diaz

Keyword(s):

Neural Network ◽

Cellular Neural Network ◽

Cmos Implementation

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CMOS implementation of associative memory using cellular neural network having adjustable template coefficients

Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94 ◽

10.1109/iscas.1994.409632 ◽

2002 ◽

Cited By ~ 6

Author(s):

A. Paasio ◽

K. Halonen ◽

V. Porra

Keyword(s):

Neural Network ◽

Associative Memory ◽

Cellular Neural Network ◽

Cmos Implementation

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

Proceedings of the Fourth IEEE International Caracas Conference on Devices, Circuits and Systems (Cat. No.02TH8611) ◽

10.1109/iccdcs.2002.1004011 ◽

2003 ◽

Author(s):

F. Lobato-Lopez ◽

J.L. Finol

Keyword(s):

Neural Network ◽

Image Processing ◽

Low Voltage ◽

Cellular Neural Network ◽

Cmos Implementation

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Compact CMOS implementation of a low-power, current-mode programmable cellular neural network

International Journal of Circuit Theory and Applications ◽

10.1002/cta.147 ◽

2001 ◽

Vol 29 (3) ◽

pp. 299-310 ◽

Cited By ~ 2

Author(s):

L. Ravezzi ◽

G.-F. Dalla Betta ◽

G. Setti

Keyword(s):

Neural Network ◽

Low Power ◽

Current Mode ◽

Cellular Neural Network ◽

Cmos Implementation

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CMOS implementation of an analogically programmable cellular neural network

IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing ◽

10.1109/82.222820 ◽

1993 ◽

Vol 40 (3) ◽

pp. 206-215 ◽

Cited By ~ 20

Author(s):

G.F.D. Betta ◽

S. Graffi ◽

Z.M. Kovacs ◽

G. Masetti

Keyword(s):

Neural Network ◽

Cellular Neural Network ◽

Cmos Implementation

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0.8 μm cmos implementation of weighted-order statistic image filter based on cellular neural network architecture

IEEE Transactions on Neural Networks ◽

10.1109/tnn.2003.816384 ◽

2003 ◽

Vol 14 (5) ◽

pp. 1366-1374 ◽

Cited By ~ 12

Author(s):

J. Kowalski

Keyword(s):

Neural Network ◽

Order Statistic ◽

Network Architecture ◽

Cellular Neural Network ◽

Neural Network Architecture ◽

Image Filter ◽

Cmos Implementation

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A CMOS Implementation of a 1-Bit Multi-Cell Encoded—Cellular Neural Network

Analog Integrated Circuits and Signal Processing ◽

10.1023/b:alog.0000016647.11624.10 ◽

2004 ◽

Vol 39 (1) ◽

pp. 95-108

Author(s):

S.S. Villareal ◽

J.P. De Gyvez ◽

M.H. Weichold

Keyword(s):

Neural Network ◽

Cellular Neural Network ◽

Cmos Implementation

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Optimizing Time-multiplexing Raster Cellular Neural Network simulator using genetic algorithms with RK8(6)

Indian Journal Of Applied Research ◽

10.15373/2249555x/june2013/30 ◽

2011 ◽

Vol 3 (6) ◽

pp. 87-90

Author(s):

O. H. Abdelwahed O. H. Abdelwahed ◽

◽

M. El-Sayed Wahed ◽

O. Mohamed Eldaken

Keyword(s):

Neural Network ◽

Genetic Algorithms ◽

Cellular Neural Network ◽

Network Simulator

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Optimizing Single-Layer Raster Cellular Neural Network Simulator Using Simulated Annealing Technique and RK4(2), RK4(3) and RK 6(4)

International Journal of Scientific Research ◽

10.15373/22778179/june2013/35 ◽

2012 ◽

Vol 2 (6) ◽

pp. 108-112

Author(s):

O. H. Abdelwahed O. H. Abdelwahed ◽

◽

M. El-Sayed Wahed

Keyword(s):

Neural Network ◽

Simulated Annealing ◽

Single Layer ◽

Cellular Neural Network ◽

Network Simulator

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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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