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

作者： A.G. Radvanyi Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Although the cellular neural net (CNN) paradigm in its original form provides a suitable framework for investigating problems defined on arbitrary regular grids, the neural chips available or under design and the available simulators are all restricted to a rectangular structure. It is not at all self-evident, however, that the rectangular structure is the most suitable to represent every practical problems. In this paper we demonstrate that several CNNs of various regular grids can be mapped onto the typical eight-neighbour rectangular one, by applying weight matrices of periodic space-variance. By adopting this option, the applicability of cellular neural chips and simulators can be extended to investigate problems of essentially arbitrary grid structures.

关键词： Cellular neural networks Transmission line matrix methods Laboratories Design automation computer simulation neural network hardware computer languages Counting circuits

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20 /spl mu/sec focal plane image processing

20 /spl mu/sec focal plane image processing

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

作者： A. Zarandy M. Csapodi T. Roska Analogical and Neural Computing Laboratory of the Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Ultra high frame rate image processing was achieved by applying CNN-UM (cellular neural net-Universal Machine) chips as focal plane array processors. By applying parallel optical input, and reading out binary decision from the chip only the computational overhead is negligible. This makes possible even 50,000 fps image capturing and complex processing. Experiments were done and are described in the paper.

关键词： Image processing Cellular neural networks Optical computing Optical sensors Hardware System testing Semiconductor device measurement Cameras Laboratories Turing machines

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The implementation of a nonlinear wave metric for image analysis and classification on the 64/spl times/64 I/O CNN-UM chip

The implementation of a nonlinear wave metric for image anal...

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

作者： I. Szatmari Analogical and Neural Computing Laboratory. Computer and Automation institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

In this paper the implementation of a nonlinear wave metric on the 64/spl times/64 I/O CNN-UM chip and its experimental results are presented. The nonlinear wave metric was designed and introduced as a generalized theorem for object analysis and classification. This proposed metric includes the well-known distance measures such as Hamming, Hausdorff metrics as special cases. The defined computational method is well-suited for cellular neural network (CNN) architecture and the experimental results shows good correlation with theoretic considerations.

关键词： Image analysis Hamming distance Semiconductor device measurement Cellular neural networks computer networks computer architecture Image recognition Laboratories automation Cellular networks

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High-speed label inspection system for textile industry

High-speed label inspection system for textile industry

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10th IMEKO TC10 International Conference on Technical Diagnostics

作者： Szatmári, István Zarándy, Ákos Analogical and Neural Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences 13-17 Kende Street Budapest H-1111 Hungary AnaLogic Computers Ltd. 13-17 Kende Street Budapest H-1111 Hungary

ISBN: (纸本)9789638658647

In this paper, the current development of a high-speed, visual inspection unit for use in the quality assurance of narrow fabric and label weaving will be presented. The aim of the project is to provide full visual defect recognition for the textile industry. Dirt spots and patches, weaving faults, missing or torn fibers, and certain errors of the label patterns are detected. A special experimental setup was prepared for evaluating the algorithm performance. Current phase achieves a very high quality level with low false alarm rate at inspection speed of 4 m/s.

关键词： Textile industry

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A Nonlinear Wave Metric and its CNN Implementation for Object Classification

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Journal of VLSI signal processing systems for signal, image and video technology 1999年第2期23卷 437-447页

作者： Szatmári, István Rekeczky, Csaba Roska, Tamás Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest Hungary Electronics Research Laboratory College of Engineering University of California at Berkeley Berkeley USA

In this paper a nonlinear wave metric is introduced for object classification. It is shown that the choice of a metric is a nontrivial problem since it is easy to give examples when well-known distance measures, such as Hamming, Hausdorff, and Nonlinear Hausdorff metrics are completely inadequate for this classification. As an alternative a generalized theorem is proposed that includes the previous metrics as special cases. It is based on nonlinear wave propagation and defines a computational framework that is well-suited for parallel array processors. In this study we investigate different Cellular neural Network (CNN) architectures and solutions for the proposed metric and analyze its VLSI implementation complexity.

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An experimental system for path tracking of a robot using a 16*16 connected component detector CNN chip with direct optical input

An experimental system for path tracking of a robot using a ...

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

作者： P. Szolgay A. Katona G. Eross A. Kiss Analogical and Neural Computing Laboratory Computer and Automation Hungarian Academy of Sciences Budapest Hungary Department of Information & Control Engineering TU Visual and Neural Computing Laboratories Veszprem Hungary

An experimental system was built up and tested for optical path tracking of a robot, where a 16*16 connected component detector chip with direct optical input was used. The speed of computation in the experimental arc... 详细信息

关键词： Detectors Cellular neural networks Robot sensing systems Optical sensors computer architecture Optical devices Fault tolerance Charge coupled devices

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Early segmentation in video compression using CNN processors

Early segmentation in video compression using CNN processors

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

作者： K. Laszlo F. Ziliani T. Roska M. Kunt Analogical and Neural Computing Laboratory Computer and Automation Institute Budapest Hungary Signal Processing Laboratory Swiss Federal Institute of Technology Lausanne Switzerland

Two analogic (analog and logic) CNN algorithms are presented which segment a video sequence into objects. The algorithms are mainly based on 3 by 3, linear templates. This allows the CNN Universal Machine to execute the task achieving enormous computation speed (10/sup 12/ equivalent operation per second). The proposed segmentation algorithms rely on texture and contour information only. They differ in the use or not of the color information. The estimated execution time proves that the proposed segmentation method may be implemented in real time. This result and the quality of the obtained frame description are very appealing in the context of the new video coding standard MPEG-4.

关键词： Video compression Cellular neural networks Analog computers Signal processing algorithms Image segmentation Laboratories Turing machines MPEG 4 Standard Layout Image color analysis

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Picture segmentation with introducing an anisotropic preliminary step to an MRF model with cellular neural networks

Picture segmentation with introducing an anisotropic prelimi...

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13th International Conference on Pattern Recognition, ICPR 1996

作者： Szirányi, Tamás Czúni, László Department of Image Processing and Neurocomputing University of Veszprém 8200 Veszprém Egyetem út 10. Hungary Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences Kende u. 13-17 H- 1111 Budapest Hungary

ISBN: (纸本)081867282X

Due to the large computation power needed for Markovian random field (MRF) based image processing, new variations of the basic MRF model are implemented. The transportation of the model to the very fast cellular neural networks (CNN) gave new tasks and opportunities to improve the technique, since the CNN has a special local architecture. This CNN architecture can be implemented in real VLSI circuits of superior speed in image processing. A type of MRF image segmentation with modified metropolis dynamics (MMD) can be well implemented in the CNN architecture. In this paper we address the improvement of this existing CNN method by introducing anisotropic diffusion as the smoothing process in the model. We suggest that this new feature with the MRF representation will give a new approach to solving early vision problems in the future. © 1996 IEEE.

关键词： Cellular neural networks

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CNN chip set architectures and the visual mouse

CNN chip set architectures and the visual mouse

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

作者： T. Roska Analogical and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Electronics Research Laboratory University of California Berkeley Berkeley CA USA

The cellular neural network (CNN) Universal Machine architecture when implemented in VLSI chips needs special interfaces to provide for efficient system performance both in time (speed) and space (image size). The CNN chip set architectures described solve this problem, including interfacing the possibly analog input sensors and digital output and control. Various forms of CNN Engines are presented embedding CNN chip sets. A new device, the Visual Mouse, a hand held visual supercomputer, is also presented which exploits the genuine features of CNN Engines.

关键词： Cellular neural networks Mice computer architecture Microprocessors Engines computer interfaces Spatiotemporal phenomena Buffer storage Laboratories Turing machines

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Analysis of Multidimensional neural Activity Via CNN-UM

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International Journal of neural systems 2003年第6期13卷 479-487页

作者： Gal, Viktor Grun, Sonja Tetzlaff, Ronald Analogical and Neural Computing Laboratory Hungarian Academy of Sciences Computer Automation Research Institute Ldgymdnyosi u. 13 BudapestH-1111 Hungary Dept. Neurophysiology Max Planck Inst for Brain Research Deutschordnerstr. 46 Frankfurt/MainD-60528 Germany Institute for Applied Physics University of Frankfurt Robert-Mayer-Str. 2-4 Frankfurt/MainD-60325 Germany

In this paper we show that the Cellular Nonlinear Network Universal Machine (CNN-UM) is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: first, the occurrences of different patterns are counted, then the statistical significance of each occurrence frequency is calculated separately. © 2003 World Scientific Publishing Company.

关键词： Electrophysiology

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