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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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A qualitative model-framework for spatio-temporal effects in vertebrate retinas

A qualitative model-framework for spatio-temporal effects in...

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

作者： D. Balya B. Roska E. Nemeth T. Roska F. Werblin Analogical & Neural Computing Laboratory Computer and Automation Research Institute Budapest Hungary Dision Research Laboratory Deparhnent of Molecular and Cell Biology University of California Berkeley Berkeley CA USA

Retinal models based on the cellular neural network (CNN) paradigm have been widely used. These neuromorphic models are based on retinal anatomy and physiology. In this paper a framework is proposed for qualitative spatio-temporal studies in vertebrate retinas, the underlying retinal anatomy is followed as closely as possible, the characteristics of the physiological models, however, are kept simple. The goal is to model the qualitative effects, since the developed models are simple, compared to a fully neuromorphic one, we have a good chance to implement them on CNN Universal Machine chips using multi-layer technology.

关键词： Retina Biological system modeling Cellular neural networks Prototypes Neuromorphics Biology computing computer networks Laboratories Biological cells Anatomy

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Elastic wave propagation modeling on emulated digital CNN-UM architecture

Elastic wave propagation modeling on emulated digital CNN-UM...

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

作者： P. Kozma P. Sonkoly P. Szolgay Department of Image Processing and Neurocomputing University of Veszprém Veszprem Hungary Department of Image Processing and Neurocomputing University of Veszprém Hungary Analogic and Neural Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences Budapest Hungary

The synthetic seismogram has seen many years of widespread and successful application in geophysical prospecting. It is used to simulate the normal incidence reflectivity of a heterogeneous medium and has been employed more recently to obtain the responses of subsurface structural and stratigraphic configurations. The propagation of seismic waves has to be modeled to create synthetic seismograms. The solution of the partial differential equations of motion describing the propagation of stress waves in an elastic medium requires enormous computation power. In this paper a solution of seismic wave propagation will be presented on CNN-UM architecture. Unfortunately the space-dependent equations and the low computational precision do not make it possible to utilize the huge computing power of the analog CNN-UM chips so the Falcon emulated digital CNN-UM architecture is used to implement our solution.

关键词： Partial differential equations computer architecture Cellular neural networks Geophysics computing Seismic waves Stress Analog computers Image processing Very large scale integration Numerical models

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CNN technology in action

CNN technology in action

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

作者： A. Zarandy S. Espejo P. Foldesy L. Kek G. Linan C. Rekeczky A. Rodriguez-Vazquez T. Roska I. Szatmari T. Sziranyi P. Szolgay Analogical and Neural Computing Laboratory of the Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Hungary Institute of Microelectronics of Seville Spanish National Microelectronics Center Spain

Two Cellular neural Net Universal Machine (CNN-UM) prototypes are demonstrated in action. The first one is the latest 4096 cell-processor, analog I/O, analogic CNN visual microprocessor, on which online video image processing will be performed. The second one, the 20/spl times/22 binary input-binary output CNN-UM chip is introduced as an ultra high-speed focal plane array processor. In the live demonstration it captures and classifies 10000 frames in a second.

关键词： Cellular neural networks Microelectronics Microprocessors Image processing Prototypes Laboratories Engines Condition monitoring Cellular networks Logic

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Fast and efficient multi-layer CNN-UM emulator using FPGA

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Periodica Polytechnica Electrical Engineering 2003年第1-2期47卷 57-70页

作者： Nagy, Zoltán Szolgay, Péter Department of Image Processing University of Veszprém Egyetem u. 10 H-8200 Veszprém Hungary Analogic/Neural Computing Laboratory Computer and Automation Institute HAS Kende u. 13-17 H-1111 Budapest Hungary

A new emulated digital multi-layer CNN-UM chip architecture called Falcon has been developed. Simulation running time can be hundred times shorter using the Falcon processor array compared to the software simulation. This huge computing power makes real time image processing possible. In this paper the main steps of the FPGA implementation and optimization are introduced. The Distributed Arithmetic technique is used to optimize the architecture on FPGAs. Using this technique, smaller and faster arithmetic units can be designed than using conventional approach where multiplier cores and adder trees are used to compute the state equation of the CNN array.

关键词： Program processors

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Hand gesture recognition in camera-projector system

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8th European Conference on computer Vision, ECCV 2004 and International Workshop on Human-computer Interaction, HCI 2004

作者： Licsár, Attila Szirányi, Tamás University of Veszprém Department of Image Processing and Neurocomputing Egyetem u. 10 VeszprémH-8200 Hungary Analogical and Neural Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences Kendeu. 13-17 BudapestH-1111 Hungary

ISBN: (纸本)3540220127

Our paper proposes a vision-based hand gesture recognition system. It is implemented in a camera-projector system to achieve an augmented reality tool. In this configuration the main problem is that the hand surface reflects the projected background, thus we apply a robust hand segmentation method. Hand localizing is based on a background subtraction method, which adapts to the changes of the projected background. Hand poses are described by a method based on modified Fourier descriptors, which involves distance metric for the nearest neighbor classification. The proposed classification method is compared to other feature extraction methods. We also conducted tests on several users. Finally, the recognition efficiency is improved by the recognition probabilities of the consecutive detected gestures by maximum likelihood approach. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Augmented reality

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Implementing a retinal visual language in CNN: a neuromorphic study

Implementing a retinal visual language in CNN: a neuromorphi...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： F. Werblin B. Roska D. Balya C. Rekeczky T. Roska Analogical & Neural Computing Laboratory Computer and Automation Research Institute Budapest Hungary Department of Molecular and Cell Biology University of California at Berkeley Berkeley CA USA

ISBN: (纸本)0780366859

The retina sends to the brain a parallel set of about a dozen different space-time representations of the visual world. Each of these representations is generated by a distinct set of "feature detecting" transformations. These features most likely contain all the information we need and use to analyze and interpret the visual world. They constitute a fundamental visual language that is elaborated upon at higher centers in the brain. A multi-layer CNN is presented for mimicking this new retinal model. The model is composed of several prototype 3-layer CNN units, called Complex R-units. Surfaces of activity are represented by CNN layers. Various parameter sets represent the different parts of the multi-layer retinal model. The whole model can be described by a visual language with elementary instructions of a CNN Universal Machine containing the programmable Complex R-units. Decomposition methods in time and space are discussed.

关键词： Retina Cellular neural networks Neuromorphics computer vision Information analysis Laboratories Biology computing Concurrent computing Turing machines Image analysis

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Deblurring of images by cellular neural networks with applications to microscopy

Deblurring of images by cellular neural networks with applic...

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

作者： J.P. Miller T. Roska T. Sziranyi K.R. Crounse L.O. Chua L. Nemes Division Neurobiology Department of Molecular & Cell Biology University of California Berkeley CA USA Analogic and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Electronics Research Laboratory Department of Electrical Engineering and Computer Science University of California Berkeley CA USA

In this paper it is shown how the Cellular neural Network (CNN) can be used to perform image and volume deblurring, with particular emphases on applications to microscopy. We discuss the basic linear theory of the CNN including issues of stability and template size. It is observed that a CNN with a small template can be used to implement an Infinite Impulse Response filter. It is then shown how general deblurring problems can be addressed with a CNN when the blurring operator is known. The proposed application is to solve the basic 3-D confocal image reconstruction task about the form of the blurring operator, confocal behavior in microscope images can be obtained with only 3-5 acquired image planes. In addition, the stored program capability of the CNN Universal Machine would provide integration of several image processing and detection tasks in the same architecture.< >

关键词： Cellular neural networks Microscopy Application software Biology computing computer networks Laboratories Stability Optical devices Optical distortion Image resolution

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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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Supervised training based hand gesture recognition system

Supervised training based hand gesture recognition system

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

We have developed a hand gesture recognition system, based on the shape analysis of static gestures, for Human computer Interaction purposes. Our appearance-based recognition uses modified Fourier descriptors for the classification, of hand shapes. As always found in literature, such recognition systems consist of two phases: training and recognition. In our new practical approach, following the chosen appearance-based model, training and recognition is done in an interactive supervised way: the adaptation for untrained gestures is also solved by hand signals. Our experimental results with three different users are reported. In this paper, besides describing the recognition itself, we demonstrate our interactive training method in a practical application. © 2002 IEEE.

关键词： Gesture recognition

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