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

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

Using a 20/spl times/22 CNN Universal Machine chip two application case studies are presented. A new analogic CNN algorithm is shown to detect objects having larger size than a given value on black-and-white image sequences moving in a given range of direction and speed (17 /spl mu/s processing speed could be achieved). An extremely fast texture classification analogic algorithm is given next with approximately 2 /spl mu/s processing speed and with less than 5% misclassification error rate for 4 natural textures in a real-life testing environment.

关键词： computer aided software engineering Cellular neural networks Switches Logic Motion detection Turing machines Testing Prototypes Registers Thyristors

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High speed stand-alone visual decision maker device based on focal plane analogic processor array chip

High speed stand-alone visual decision maker device based on...

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IEEE International Workshop on Virtual and Intelligent Measurement Systems

作者： L. Torok A. Zarandy T. Roska Analogical & Neural Computing Systems Laboratory at Computer and Automation Research Institute of the Hungarian Academy of Sciences Budapest Hungary

ISBN: (纸本)0780365682

Newly emerging problems require high speed decision making based on visual perception of the environment. A project was set up to construct an intelligent agent like self-contained device that is capable to act in real-time and show collaborative behavior. Giving the hardware basis for decisions to be made a cellular nonlinear network CNN (Chua and Yang, 1988) chip implementation's optical input is used in combination with the cooperative devices' information that is received via binary ports and serial ports. The proposed design is a self-contained compact device that is prepared to operate stand-alone for up to 10 hours running on medium sized batteries while doing measurements, logging and collaborating with its environment via parallel port (for image transfer), RS-232 port (using modbus, profibus, PPP protocols) and binary I/O-s. Intelligent power module, optical isolation, watch dog capability is also considered.

关键词： Collaboration High speed optical techniques Nonlinear optics Decision making Visual perception Intelligent agent Hardware Cellular networks Cellular neural networks Nonlinear optical devices

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Optimal CNN templates for deconvolution

Optimal CNN templates for deconvolution

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

作者： L. Orzo Analogical & Neural Computing Systems Laboratory at the Computer and Automation Research Institute of the Hungarian Academy of Sciences Budapest Hungary

A simplified version of the gradient descent method is introduced as a straightforward way to find optimal 3/spl times/3 CNN templates for the inversion of known point spread functions (PSF). In practical applications the determination of this inverse is necessary to fulfil deconvolution tasks. The proposed method is much faster than the previously applied algorithms (like genetic algorithm) and still, in almost all practically important cases, it is convergent. Moreover, unlike a closed form method, it leads to 3/spl times/3 templates instead of 5/spl times/5 or bigger ones. In several important practical cases the PSF, which can be caused by motion, out of focus or the aberration of the imaging system, can be computed from object positions and from the optical system's parameters. Iterative deconvolution algorithms, which are necessary for volume reconstruction from microscopic image sequences, require considerable computation time. Using CNN-UM chips for these deconvolution tasks, a much higher speed, even real time processing seems to be achievable.

关键词： Cellular neural networks Deconvolution Genetic algorithms Focusing Optical imaging Optical computing Iterative algorithms Image reconstruction Microscopy Image sequences

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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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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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An adaptive resonance theory-based neural network capable of learning via representational redescription

An adaptive resonance theory-based neural network capable of...

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International Joint Conference on neural Networks (IJCNN)

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

This paper introduces a neural network architecture called R2MAP, which is based on the representational redescription hypothesis in cognitive science and adaptive resonance theory (ART) neural networks. The R2MAP network learns to classify arbitrary sequences of input patterns using a re-iterative process whereby knowledge that gets embedded in the network via ARTMAP-style error-driven learning is redescribed and becomes available to it for further learning. The knowledge redescription phase is triggered when the perceived level of difficulty of the given task exceeds a certain threshold, and is achieved through the dynamic creation of new features that better distinguish between output classes. This way the R2MAP network is capable of learning complex, relational input-output dependencies that cannot be represented efficiently using solely the features extracted through ordinary learning of statistical relationships. A simple proof-of-concept example is presented to illustrate the main ideas. Some related work is also discussed.

关键词： Resonance neural networks Biological neural networks Subspace constraints Cognitive science Artificial neural networks Humans Laboratories automation computer science

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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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Multi-target tracking with stored program adaptive CNN universal machines

Multi-target tracking with stored program adaptive CNN unive...

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

作者： C. Rekeczky G. Timar G. Cserey Analogical and Neural Computing Systems Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

This paper shows that the performance of multi-target tracking (MTT) systems can be significantly increased with stored program adaptive cellular array sensors. The primary motivation of the present work is to define a topographic microprocessor architecture for MTT with embedded sensors capable of operating in a process real-time manner. In the ongoing experiments it is assumed that the input data flow is acquired by a single array sensor and the data is processed on an adaptive CNN-UM architecture consisting of both a cellular nonlinear network (CNN) and digital signal processing (DSP) microprocessors. The algorithms designed for this combined hardware platform use adaptive multi-channel CNN solutions for instantaneous position estimation and morphological characterization of all visible targets and the DSP environment for distance calculation, gating, data association, track maintenance and dynamic target motion prediction. A special feature of the architecture is that it allows interactive communication between the sensor and the digital environment. The configuration of functional modules for various real-time applications is discussed. Using real-life video-flows, successful tracking of several maneuvering targets is demonstrated within the proposed adaptive multi-channel framework.

关键词： Cellular neural networks Turing machines Sensor arrays Digital signal processing Adaptive arrays Microprocessors Sensor phenomena and characterization Target tracking Sensor systems Cellular networks

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An optical CNN implementation with stored programmability

An optical CNN implementation with stored programmability

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： S. Tokes L. Orzo C. Rekeczky T. Roska A. Zarandy Analogical & Neural Computing Systems Laboratory at the Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

The objective of this paper is to provide a framework for the implementation of programmable Opto-Electronic Analogic CNN (POAC) computers embedding CNN Universal Chips. Specifically, a new method for optical CNN implementation is provided and some details are experimentally studied. The POAC architecture includes the integration of an optical processing system, such as a joint transform correlator, with the fast spatio-temporal processing capabilities of a CNN-UM chip. We have built and tested an optical sub-unit of this experimental optoelectronic architecture to examine their processing capabilities for complex target recognition tasks. Preliminary result of these measurements will also be presented. The main idea is to introduce stored programmability into optical computing.

关键词： Cellular neural networks Nonlinear optics Optical filters Optical network units Correlators computer architecture Embedded computing Optical computing Image processing Target recognition

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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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