Cellular Neural Network


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Cellular Neural Network

(architecture)
(CNN) The CNN Universal Machine is a low cost, low power, extremely high speed supercomputer on a chip. It is at least 1000 times faster than equivalent DSP solutions of many complex image processing tasks. It is a stored program supercomputer where a complex sequence of image processing algorithms is programmed and downloaded into the chip, just like any digital computer. Because the entire computer is integrated into a chip, no signal leaves the chip until the image processing task is completed.

Although the CNN universal chip is based on analogue and logic operating principles, it has an on-chip analog-to-digital input-output interface so that at the system design and application perspective, it can be used as a digital component, just like a DSP. In particular, a development system is available for rapid design and prototyping. Moreover, a compiler, an operating system, and a user-friendly CNN high-level language, like the C language, have been developed which makes it easy to implement any image processing algorithm.

[Professor Leon Chua, University of California at Berkeley].
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References in periodicals archive ?
The cellular neural network is a large-scale nonlinear analog circuit with the high speed and parallel signal processing.
5-7] The above content introduces the mathematical model of cellular neural network detailed.
2 Image encryption based on cellular neural network
A new algorithm of image encryption based on hyper chaos of Cellular Neural Network.
A new algorithm of image encryption technology based on Cellular Neural Network.
In this paper we investigate the possibility of using cellular neural network along with adaptive kernel strategy for improving the feature matching as well as resampling accuracies of automatic image registration.
These object representations are modeled and interpreted using Cellular Neural Network (CNN) for effective feature matching (details in sec 1.
A cellular system for pattern recognition using associative neural networks, in Proceedings of IEEE International Workshop on Cellular Neural Networks and their Application, 14-17 April, 1997, 2(4): 665-669.
On the global asymptotic stability of delayed cellular neural networks, IEEE Trans.
Global Exponential Stability and Periodic Solutions of Delayed Cellular Neural Networks, Journal of Computer and Systems Sciences, vol.

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