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

作者： T. Roska P. Szolgay A. Zarandy P.L. Venetianer A. Radvanyi T. Sziranyi Analogical and Neural Computing Laboratory Computer and Automation Inst. Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Comput. & Autom. Inst. Hungarian Acad. of Sci. Budapest Hungary

An analogic CNN chip prototyping and development system was designed and manufactured to test and measure different VLSI implementations of the analogic CNN Universal Machine. A high level language was developed to support the design of analogic algorithms and an image capture was designed for on-chip image sensing and through CCD camera.< >

关键词： Cellular neural networks Algorithm design and analysis Prototypes Manufacturing System testing Semiconductor device measurement Very large scale integration Turing machines High level languages Charge coupled devices

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Random parameter variation in analog VLSI neural networks for linear image filtering

Random parameter variation in analog VLSI neural networks fo...

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International Joint Conference on neural Networks (IJCNN)

作者： B.E. Shi T. Roska L.O. Chua Department of Electrical Engineering and Computer Sciences University of California Berkeley CA USA Dual and Neural and Neural Computing Systems Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

This paper introduces an analytic method to determine the sensitivity to random parameter variations of analog VLSI neural network architectures for linear image filtering. The authors compare the robustness of several different circuit architectures for low pass filtering. This method can also determine which components within a particular architecture should specified the most precisely.< >

关键词： Intelligent networks Very large scale integration neural networks Nonlinear filters Circuits Filtering Robustness Low pass filters computer architecture Cellular neural networks

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An accelerated digital CNN-UM (CASTLE) architecture by using the pipe-line technique

An accelerated digital CNN-UM (CASTLE) architecture by using...

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

作者： T. Hidvegi P. Keresztes P. Solgay Analogic and Neural Computing Systems Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Analogic and Neural Computing Systems Laboratory Hungarian Academy of Sciences Budapest Hungary István Széchenyi University Gyôr Hungary

Different CNN-UM architecture implementations, analog and emulated digital, were developed. The emulated digital architecture (CASTLE) is accurate but slower than the analog CNN-UMs. It is generally disadvantageous especially if transient computing is critical. The operation speed of the emulated digital implementations, namely CASTLE, can be increased significantly using the pipeline technique. This solution is analyzed with respect to area, time, etc. These arithmetic cores were tested and simulated using a VIRTEX FPGA development system.

关键词： Acceleration computer architecture Cellular neural networks Arithmetic Registers automation Image processing System testing Computational modeling Field programmable gate arrays

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Image inpainting methods by using cellular neural networks

Image inpainting methods by using cellular neural networks

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

作者： A. Gacsadi P. Szolgay Electronics Department University of Oradea Oradea Romania Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Image Processing and Neurocomputing Department Veszprem University Veszprem Hungary

Some CNN methods are presented that can be used for the reconstruction of damaged or partially known images. The proposed methods take the possibility of direct implementation on an existing CNN chip into account, in a single step, by using 3*3 dimensional linear reaction templates. Due to complete parallel processing, computational time reduction is achieved. Efficiency of these methods can be increased by combining them with nonlinear template that ensures the growth of the local properties spreading area along with regional ones.

关键词： Cellular neural networks Image processing Image restoration Partial differential equations Image reconstruction Parallel processing Concurrent computing Cost function Gray-scale Laboratories

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A fast, complex and efficient test implementation of the CNN Universal Machine

A fast, complex and efficient test implementation of the CNN...

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

作者： J.M. Cruz L.O. Chua T. Roska Electronics Research Laboratory and Department of Electrical Engineering and Computer Sciences University of California Berkeley CA USA Analogical and Neural Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Electronics Research Laboratory University of California Berkeley CA USA

In this paper we report on a fast, complex and efficient implementation of the Cellular neural Network Universal Machine as an IC chip. The chip has continuous time analog dynamics, and has been designed to process 50... 详细信息

关键词： Testing Cellular neural networks Turing machines Voltage Laboratories USA Councils Analog memory Logic circuits Programmable logic arrays Capacitors

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Multi-scale image analysis on the CNN Universal Machine

Multi-scale image analysis on the CNN Universal Machine

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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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Multigrid MRF based picture segmentation with cellular neural networks 7th

Multigrid MRF based picture segmentation with cellular neura...

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7th International Conference on computer Analysis of Images and Patterns, CAIP 1997

作者： Czúni, Lázló Szirányi, Tamás Zerubia, Josiane University of Veszprém Dep. of Image Processing and Neurocomputing Egyetem u. 10 VeszprémH-8200 Hungary Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences H-11 t 1 Budapest Kende u. 13-17 Hungary NRIA Sophia-Antipolis BP 93 2004 route des Lucioles Sophia-Antipolis Cedex06902 France

ISBN: (纸本)3540634606

Due to the large computation power needed in image processing methods based on Markovian Random Field (MRF) [6], new variations of basic MRF models are implemented. The Cellular neural Network [5,14,15] (CNN) architecture, implemented in real VLSI circuits, is of superior speed in image processing. This very fast CNN can implement the ideas of existing MRF models. which would result in real-time processing of images. On the other hand this VLSI solution gives new tasks since the CNN has a special local architecture [4], but it is already shown that a type of MRF image segmentation with Modified Metropolis Dynamics (MMD [9]) can be well implemented in the CNN architecture [18]. In this paper, we address the improvement of the existing CNN method [ 17]. We have tested different multigrid models and compared segmentation results. The main reason for this research is to find proper implementation of the CNN-MRF technique on CNNs taking into consideration the abilities of today's and future's VLSI CNN systems. © Springer-Verlag Berlin Heidelberg 1997.

关键词： Cellular neural networks

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A comparison of the different CNN implementations in solving the problem of spatiotemporal dynamics in mechanical systems

A comparison of the different CNN implementations in solving...

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

作者： P. Szolgay T. Hidvegi Z. Szolgay P. Kozma Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Department of Mechanics Technical University of Budapest Budapest Hungary Image processing and Neurocomputing University of Veszprém Veszprem Hungary

The large computing power of cellular neural networks (CNNs) is used to compute the transient response of a mechanical vibrating system. A basic question is how an inhomogeneous mechanical system can be computed by using different CNNUM (cellular neural net Universal Machine ) implementations. How complexity to time transformations were used in different CNNUM implementations, namely in software, in emulated digital CNN chips and in analog chips is discussed.

关键词： Cellular neural networks Spatiotemporal phenomena Mechanical systems computer networks Equations Damping Boundary conditions Laboratories automation Transient response

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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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Non-linear scale-selection for image compression improvement obtained by perceptual distortion criteria

Non-linear scale-selection for image compression improvement...

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International Conference on Image Analysis and Processing

作者： I. Kopilovic T. Sziranyi Department of Image Processing and Neurocomputing University of Veszprém Veszprem Hungary Analogical and Neural Computing Laboratory Computer & Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Department of Image Processing and Neurocomputing University of Veszprém Hungary

The effect of linear and nonlinear diffusions prior to the Joint Photographic Experts Group (JPEG) standard baseline compression is analysed and compared. We shall show that anisotropic diffusion acts as an adaptive filtering that extracts and preserves the important visual information, and it will be shown how it can be used as a preprocessing method for compression artefact reduction. The selection of the appropriate preprocessing parameters at a specified bit-rate greatly reduces the compression artefacts. The selection criteria were determined using a perceptual error metric based on a model of the human visual system.

关键词： Image coding Nonlinear distortion Adaptive filters Data mining Image processing Transform coding Smoothing methods Nonlinear equations Noise shaping Filtering

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