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A fast fixed point learning method to implement associative memory on CNN's

IEEE TRANSACTIONS ON CIRCUITS AND systems I-FUNDAMENTAL THEORY AND APPLICATIONS 1997年第4期44卷 362-366页

作者： Szolgay, P Szatmari, I Laszlo, K Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Cellular neural Networks (CNN's) with space-varying interconnections are considered here to implement associative memories, A fast learning method is presented to compute the interconnection weights. The algorithm was carefully tested and compared to other methods. Storage capacity, noise immunity, and spurious state avoidance capability of the proposed system are discussed.

关键词： associative memory fixed point

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HIGH-SPEED CHARACTER-RECOGNITION USING A DUAL CELLULAR neural-NETWORK ARCHITECTURE (CNND)

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IEEE TRANSACTIONS ON CIRCUITS AND systems II-EXPRESS BRIEFS 1993年第3期40卷 223-231页

作者： SZIRANYI, T CSICSVARI, J Analogic and Neural Computing Systems Research Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

An effective new character recognition procedure implemented on a new type of hardware system is proposed. This procedure applied a new architecture, called CNND. This CNND contains one or more analog cellular neural Networks (CNN) and some digital logic, incorporating the advantages of the fast analog CNN signal processing and the fast and easy decision capability of digital logics. This paper shows that this CNND system can be used for recognition of multifont printed or handwritten characters. Implemented in hardware, the system could hit the 100 000 char/s recognition speed with a recognition rate of more than 95 %. We show that the CNN results of pictures (maximum 40 * 40 pixels) of printed characters can be coded into about n * 20 bits (n = 2 ... 6) , so the coded results can be used to address memories of about 1 MB. The codes of CNN results of possible character pictures are used to address the memories while the memory contents are filled by the character categories. Prior to the hardware implementation the decision memories are filled by the results of recognition simulation for the possible pictures of each character-class in a filling procedure. In the memory filling procedure the simulated recognition uses a new random-type nearest neighbor (NN) method, which is ideal for the recent proposal of hardware applications. Recognition of handwritten characters is demonstrated in the same system with good recognition accuracy.

关键词： Character recognition Cellular neural networks Image edge detection Handwriting recognition neural networks Detectors Logic Hardware Charge coupled devices Filling

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Short circuit detection on printed circuit boards during the manufacturing process by using an analogic CNN algorithm 14th

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14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert systems, IEA/AIE 2001

作者： Hidvégi, Timót Szolgay, Péter Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences P.O.B 63 BudapestH-1502 Hungary

ISBN: (纸本)3540422196

One of the most critical errors is the short circuit in the manufacturing of printed circuit boards. In this contribution we extend the already existing solution to more general cases where the errors can be detected by checking two production layers. The algorithm was tested with software simulator and 64*64 CNN-UM chip of ALADDIN System. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Printed circuit boards

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Programmable OASLM as a Novel Sensing Cellular Computer

Programmable OASLM as a Novel Sensing Cellular Computer

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： Szabolcs Tokes Laszlo Orzo Ahmed Ayoub Analogic and Neural Computing Systems Laboratory MTA-SZTAKI Budapest Hungary

We have developed a novel type of correlators named searching correlator (SC) (Tokes et al., 2003) using bacteriorhodopsin (BR) as a dynamic rewritable and multiply readable holographic material. Our POAC (programmable optical array computer) model can perform highly parallel optical correlations and feedforward CNN operations. However, to keep pace with digital technologies (Moore's law); newer, more effective materials are needed. The sensitivity, diffraction efficiency and cycle time are the most critical parameters of the dynamic holographic materials to be used in 2D optical processors. Only highly engineered, composite media, such as multiple quantum well optically addressable spatial light modulator (MQW-OASLM), liquid crystal embedded polymers or photonic crystal devices will be able to satisfy all of the requirements. However, these devices could not reach yet the stage of a commercial product. That is why we are working on a new approach for realizing competitive optical CNN processor architectures by integrating a high-end CMOS camera and an LCD micro-display with an embedded FPGA processor. Embedding this PRC-OASLM, our present feed-forward-only optical CNN processor architecture can be developed to a nonlinear feedback architecture

关键词： Holography Holographic optical components Optical feedback Nonlinear optics Optical sensors Optical modulation Optical polymers Optical materials Cellular neural networks Correlators

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Regular small-world cellular neural networks: key properties and experiments

Regular small-world cellular neural networks: key properties...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： G. Timar D. Balya AnaLogic and Neural Computing Systems Laboratory Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Small world networks have a peculiar property: even though almost all of their nodes are only locally connected, the average path length between two nodes is nearly as low as that of random networks. Networks of this type are ubiquitous in natural systems and frequently show interesting dynamics. We investigated the behavior of a new class of cellular nonlinear networks (CNNs) where all cells are locally connected to their nearest neighbors (as in a conventional CNN), but some of the nodes have long-range shortcuts as well. We give lower bounds on the number of extra links that must be added for a network to be considered as small-world and suggest an optimal topology for the added links. We also show some interesting behaviors of this network in diffusion and wave propagation phenomena.

关键词： Cellular neural networks Lattices Cellular networks Network topology Complex networks Laboratories Automation Nearest neighbor searches Diseases Image processing

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Pattern formation on the prototype complex-cell CNN-UM chip (CACE1K)

Pattern formation on the prototype complex-cell CNN-UM chip ...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： D. Balya I. Petras C. Rekeczky AnaLogic and Neural Computing Systems Laboratory Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

In this paper several pattern formation phenomena generated on a prototype complex cell (second order) CNN-UM chip (CACE1K) will be demonstrated. It is shown that the basic motifs (patches, checkers, and stripes) can be easily calculated and furthermore, the interaction of the two dynamic layers leads to new patterns that cannot be generated on a first order architecture. Throughout the paper a rich set of measured phenomena is given motivating further research toward a constructive use of pattern formation dynamics in various flow processing algorithms.

关键词： Pattern formation Prototypes Cellular neural networks Semiconductor device measurement Time measurement Differential equations CMOS technology Laboratories Automation Fluid flow measurement

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Solution of stereo correspondence in real scene: an analogic CNN algorithm

Solution of stereo correspondence in real scene: an analogic...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： A.G. Radvanyi Analogic & Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest Hungary

A multilayer analogic CNN (cellular neural network) algorithm is proposed to reconstruct surfaces from real scene stereo-pairs consisting of two images shot from one and the same camera position. The whole scene is illuminated by randomly structured light to provide texture on otherwise "empty" surfaces. One image is that of the illuminated background, the other one is the image of illuminated objects in front of the same background.< >

关键词： Layout Cellular neural networks Image reconstruction Surface reconstruction Stereo vision Surface texture Matched filters Laboratories Automation Nonhomogeneous media

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CASTLE: an emulated digital CNN architecture design issues, new results

CASTLE: an emulated digital CNN architecture design issues, ...

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IEEE International Conference on Electronics, Circuits and systems (ICECS)

作者： A. Zarandy P. Keresztes T. Roska P. Szolgay Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

A new emulated digital CNN universal machine chip architecture is introduced in this paper. The chip contains 24 CNN core processors using 70 mm/sup 2/ silicon area with a 0.35 /spl mu/m technology. Its speed is 1 ns/virtual cells/CNN iterations with 12 bit precision. This enables one to execute over five hundred 3/spl times/3 convolutions on each frame of a 240/spl times/320 sized 25 fps (standard NTSC) digital image flow. Another new feature of the design is the different precision capability. This allows the user to convert precision to speed.

关键词： Cellular neural networks Computer architecture Optical feedback Equations Turing machines Silicon Convolution Image processing Very large scale integration Biomedical optical imaging

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Distance preserving 1D Turing-wave models via CNN, implementation of complex-valued CNN and solving a simple inverse pattern problem (detection)

Distance preserving 1D Turing-wave models via CNN, implement...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： G. Toth P. Foldesy T. Roska Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

In this paper a cellular neural network (CNN) implementation of a reaction-diffusion system is described, which produces distance preserving periodic Turing patterns. The CNN with complex-valued templates are introduced, presenting an application for pattern generation. Finally a method for black-and-white pattern detection is described.

关键词： Cellular neural networks Equations Skin Marine animals Laboratories Automation Physics Chemistry Biological system modeling Inhibitors

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Adaptive histogram equalization with cellular neural networks

Adaptive histogram equalization with cellular neural network...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： M. Csapodi T. Roska Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Adaptive histogram equalization (AHE), a method of contrast enhancement which is sensitive to local spatial information in image, has demonstrated its effectiveness in many applications. However, this technique is computationally intensive. In this paper we present two computational methods designed to fit well onto the locally interconnected array computer architecture of cellular neural networks (CNNs). CNNs are well known for their image processing capabilities, specially for grey-scale medical images and images of a natural scene. In many applications it would be very useful if the operation of a template or a complex analogic algorithm were highly illumination independent. Our results suggest that we can achieve this goal by using the AHE method in a pre-processing step.

关键词： Histograms Adaptive equalizers Cellular neural networks Application software Adaptive systems Computer networks Design methodology Computer architecture Image processing Biomedical imaging

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