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

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

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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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FORMING AN INFORMATION TECHNOLOGY DEPARTMENT IN TURBULENT TIMES

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IEEE COMMUNICATIONS MAGAZINE 1992年第11期30卷 52-56页

作者： ROSKA, T Dual and Neural Computing Systems Research Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Hungary

The goals, objectives, characteristics, prerequisites, and courses of an information technology curriculum developed at the University of Veszprem in Hungary are discussed. Basic, introductory, and core courses as well as special subjects are listed, and the program schedule of the five years is presented. The place and role of information engineering in a changing world is discussed, including some thoughts on the role of information technology in the general university curricula.< >

关键词： Information technology Laboratories Education Educational technology Automation Europe Mechanical engineering Electrical engineering computing Informatics Solids

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Robustness of cellular neural networks in image deblurring and texture segmentation

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 1996年第3期24卷 381-396页

作者： Sziranyi, T Analogical and Neural Computing Systems Research Laboratory Computer and Automation Institute Hungarian Academy of Sciences (MTA SZTAKI) pf. 63 H-1518 Budapest Hungary

Template parameters of cellular neural networks (CNNs) should be robust enough to random variability of VLSI tolerances and noise. Using the CNN for image processing, one of the main problems is the robustness of a given task in a real VLSI chip. It will be shown that very different tasks such as 2D or 3D deconvolution and texture segmentation can be solved in a real VLSI CNN environment without significant loss of efficiency and accuracy under low precision (about 6-8 bits) and random variability of the VLSI parameters. The CNN turns out to be very robust against template noise, image noise, imperfect estimation of templates and parameter accuracy. The parameters of a template are tuned using genetic learning. These optimized parameters depend on the precision of the architecture. It was found that about 6-8 bits of precision is enough for a complicated multilayer deconvolution, while only 4 bits of precision is enough for difficult texture segmentation in the presence of noise and parameter variances, The tolerance sensitivity of template parameters is considered for VLSI implementation. Theory and examples are demonstrated by many results using real-life microscopic images and natural textures.

关键词： Cellular neural networks

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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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SUBPIXEL PATTERN-RECOGNITION BY IMAGE HISTOGRAMS

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PATTERN RECOGNITION 1994年第8期27卷 1079-1092页

作者： SZIRANYI, T Analogical and Neural Computing Systems Research Laboratory Computer and Automation Institute Hungarian Academy of Sciences (MTA SZTAKI) P.O. Box 63 H-1518 Budapest Hungary

Recognition of small patterns covering only a few pixels in an image cannot be done by conventional recognition methods. A theoretically new pattern recognition method has been developed for undersampled objects which are (much) smaller than the window-size of a picture element (pixel), i.e. these objects are of subpixel size. The proposed statistical technique compares the gray-level histogram of the patterns of a set of scanned objects to be examined with the (calculated) gray-level densities of different (in shape or size) possible objects, and the recognition is based on this comparison. This method does not need high-precision movement of scanning sensors or any additional hardware. Moreover, the examined patterns should be randomly distributed on the screen, or a random movement of camera is (or target or both are) needed. Effects of noise are analysed, and filtering processes are suggested in the histogram domain. Several examples of different object shapes (triangle, rectangle, square, circle, curving lines, etc.) are presented through simulations and experiments. A number of possible application areas are suggested, including astronomy, line-drawing analysis and industrial laser measurements.

关键词： SUBPIXEL-RECOGNITION HISTOGRAM NOISE FILTERING UNDERSAMPLING SUPERRESOLUTION IMAGE ANALYSIS PATTERN CLASSIFICATION CONVOLUTION STATISTICAL PATTERN RECOGNITION LIGHT-SENSOR ARRAYS DENSITY ESTIMATION

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3D integrated scalable focal-plane processor array

3D integrated scalable focal-plane processor array

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European Conference on Circuit Theory and Design 2007, ECCTD 2007

作者： Földesy, Péter Zarándy, Ákos Rekeczky, Csaba Roska, Tamás Computer and Automation Research Institute of the Hungarian Academy of Sciences Analogic and Neural Computing Laboratory Budapest Hungary Eutecus Inc. Berkeley CA United States

ISBN: (纸本)1424413427

The ASIC implementation of a 6464 sized mixedsignal Cellular Visual Microprocessor architecture with digital processors is described. Measurement results are shown. The architecture is composed of a regular photosensor readout circuit array, prepared for 3D sensor integration, an array of identical SIMD processing elements, and central program scheduler. The processing architecture supports cluster formation of differently parameterized arrays. © 2007 IEEE.

关键词： Application specific integrated circuits

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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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