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

作者： T. Kozek K.R. Crounse T. Roska L.O. Chua Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Nonlinear Electronics Laboratory University of California Berkeley CA USA

Algorithms for generating multi-scale representations of gray-scale images are presented. A number of possible approaches are described to produce low-pass and band-pass decompositions using simple analogic algorithms. It is also shown how the wavelet transform can be approximated with a similar technique and be used to obtain multi-level descriptions of the input data. This paper presents some methods how the cellular neural network (CNN) Universal Machine can be used effectively for generating multi-scale representations of gray-scale imagery.

关键词： Image analysis Cellular neural networks Turing machines Convolution Laplace equations Image processing Kernel Data mining Laboratories Wavelet transforms

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Collision prediction via the CNN Universal Machine

Collision prediction via the CNN Universal Machine

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

作者： V. Gal T. Roska Analogical & Neural Computing Systems Laboratory at the computer Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Hungary

We present an analogic CNN algorithm that estimates the time to an impending collision between an approaching object and the observer. Calculation is based on a context insensitive method, which is well known in neuro... 详细信息

关键词： Cellular neural networks Turing machines Animals Equations Geometrical optics Object detection Image edge detection Detectors Pixel Optical arrays

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An Emulated Digital CNN Implementation

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Journal of VLSI signal processing systems for signal, image and video technology 1999年第2期23卷 291-303页

作者： Keresztes, Péter Zarándy, Ákos Roska, Tamás Szolgay, Péter Bezák, Tamás Hidvégi, Timót Jónás, Péter Katona, Attila István Széchenyi Polytechnic Győr Hungary Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest Hungary University of Veszprém Veszprém Hungary

A new emulated digital CNN Universal Machine chip architecture is introduced and the main steps of the design process are shown in this paper. One core processor can be implemented on 2 × 2 mm2 silicon area with a 0.35 μm CMOS technology. Assuming an array of 24 processors on a chip, its speed is 1ns/virtual cell/CNN iteration with 12 bit precision. This enables the execution of over five hundred 3 × 3 convolution operations on each frame of a 240 × 320-pixel 25 fps digital image flow. Another new feature of the design is its variable precision capability. This allows the user to trade off precision for speed. The architecture supports some non-linear filter implementation as well.

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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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A new CNN template for POAC peak enhancement

A new CNN template for POAC peak enhancement

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European Conference on Circuit Theory and Design, ECCTD

作者： A. Ayoub S. Tokes Analogic and Neural Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Magyar Tudomanyos Akademia Szamitastechnikai es Automatizalasi Kutatointezet Budapest Budapest HU

We propose a CNN template to enhance the POAC output (correlogram peaks) for higher detection accuracy. Our recursive algorithm found the optimum template values under the one-third condition. The statistical, simulation and practical results confirmed that the template: enhances the correlogram peaks' detection; is fast for real time applications; is suitable to work under several illuminations; and avoids the complexity of adaptive thresholding.

关键词： Cellular neural networks Band pass filters Optical feedback Equations Lighting Optical computing Optical filters Computational modeling Adaptive optics Optical fiber networks

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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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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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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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Statistical subpixel pattern recognition by histograms

Statistical subpixel pattern recognition by histograms

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International Conference on Pattern Recognition

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

A new statistical pattern recognition method has been developed for detection, recognition or measurement of patterns which are (much) smaller than the measure of the elementary pixel windows in the image screen. In this measurement the gray-level histogram of the objects examined is compared with the simulated histograms of different (in type or size) possible objects, and the recognition (of shape or measure) is taken on the basis of the comparison. This method does not need ultra-precise movement of the scanning sensors or any additional hardwares. Moreover, the examined pattern should be randomly distributed on the screen, or a random movement of camera (or target or both) is needed. Effect of noises are analyzed, and filtering processes are suggested in the histogram domain. Several examples of different shapes are presented through simulations and experiments.< >

关键词： Pattern recognition Histograms Shape measurement Size measurement Image recognition Pixel Hardware Cameras Noise shaping Filtering

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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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