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

作者： P. Szolgay G. Voros Analogical and Neural Computing Laboratory. Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Department of Technical Mechanics Budapest Hungary

Cellular neural networks (CNNs) paradigm is applied in the paper to compute the transient response of mechanical vibrating systems. Based on previous theoretical results on this field we would like to show (i) how the CNN templates can be generated automatically by a subroutine from the COSMOS/M finite element analysis system; (ii) how we assign to each degree of freedom two coupled CNN layers and how the templates are derived. Some interesting examples are shown and analyzed.< >

关键词： Transient response computer networks Cellular neural networks Finite element methods Symmetric matrices Differential equations Partial differential equations Algorithms Elasticity Damping

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Analogic Implementation of the Genetic Algorithm

Analogic Implementation of the Genetic Algorithm

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

作者： David Balya Viktor Gal Analogic and Neural Computing Systems Lab Computer and Automation Research Institute Budapest Hungary Function of Neural Circuits Laboratory Friedrich Miescher Institute for Biomedical Research Basel Switzerland

The CNN literature contains several papers about how to use the genetic algorithms for template tuning. This paper shows a possible CNN-UM implementation of the control algorithm of the genetic algorithm in a special fine-grained parallel version. The interesting of the analogic mapping is the different set of operators, which can be evaluated fast and efficient on the CNN-UM platform. The rational behind the spatial implementation is the possibility of focal-plane optimization

关键词： Genetic algorithms Biological cells Cellular neural networks Biomedical computing computer networks Evolution (biology) Algorithm design and analysis Genetic mutations Application software Application specific integrated circuits

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Anisotropic diffusion as a preprocessing step for efficient image compression

Anisotropic diffusion as a preprocessing step for efficient ...

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International Conference on Pattern Recognition

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

Anisotropic diffusion is an image enhancement method. It is a nonlinear process which removes noise and irrelevant details while preserving the edges, i.e. it "extracts" the essential visual information. The paper proposes a useful application of anisotropic diffusion in image data compression. We argue that for high compression an anisotropic diffusion preprocessing results in better quality of the decoded image.

关键词： Anisotropic magnetoresistance Image coding Image processing Transform coding Space heating Equations Laboratories automation Time of arrival estimation Data mining

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Computational Experiments for Complex Social systems:Experiment Design and Generative Explanation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1022-1038页

作者： Xiao Xue Deyu Zhou Xiangning Yu Gang Wang Juanjuan Li Xia Xie Lizhen Cui Fei-Yue Wang the School of Computer Software College of Intelligence and ComputingTianjin UniversityTianjin 300350China the School of Software and the Joint SDUNTU Centre for Artificial Intelligence Research(C-FAIR) Shandong UniversityJinan 250101China the Information School Xi’an University of Financial and EconomicsXi’an 710100China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Hainan University Haikou 570228China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 1001901China

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system *** address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating ***,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

关键词： Agent-based modeling computational experiments cyber-physical-social systems(CPSS) generative deduction generative experiments meta model

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Supervised training based hand gesture recognition system

Supervised training based hand gesture recognition system

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International Conference on Pattern Recognition

作者： A. Licsar T. Sziranyi Department of Image Processing and Neurocomputing University of Veszprém Veszprem Hungary Analogical & Neural Computing Laboratory Computer & Automation Research Institute Hungarian Academy of Sciences (ATOMKI) 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. Besides describing the recognition itself we demonstrate our interactive training method in a practical application.

关键词： Shape Character recognition Image processing Laboratories automation Human computer interaction Application software Control systems Virtual environment Cameras

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Dynamic training of hand gesture recognition system

Dynamic training of hand gesture recognition system

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International Conference on Pattern Recognition

We developed an augmented reality tool for vision-based hand gesture recognition in a camera-projector system. Our recognition method uses modified Fourier descriptors for the classification of static hand gestures. Hand segmentation is based on a background subtraction method, which is improved to handle background changes. Most of the recognition methods are trained and tested by the same service-person, and training phase occurs only preceding the interaction. However, there are numerous situations when several untrained users would like to use gestures for the interaction. In our new practical approach the correction of faulty detected gestures is done during the recognition itself. Our main result is the quick on-line adaptation to the gestures of a new user to achieve user-independent gesture recognition.

关键词： computer displays Cameras Fault detection Mice Hardware Phase detection System testing Feedback Auditory displays Image processing

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computing on silicon with trigger waves: experiments on CNN-UM chips

Computing on silicon with trigger waves: experiments on CNN-...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： C. Rekeczky I. Szatmari P. Foldesy Analogical and Neural Computing Systems Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Jedlik Laboratories Department of Information Technology Péter Pazmány Catholic University Budapest Hungary

ISBN: (纸本)0780366859

In this paper, experimental results on Cellular neural Network Universal Machine (CNN-UM) chips will be presented. These analogic spatio-temporal visual microprocessors make it possible that one can use nonlinear waves as the basic kernels of algorithms solving filtering-reconstruction and/or detection-classification problems. Showing output results from series of experiments it will be demonstrated how trigger waves, the simplest nonlinear waves, can constructively be used in a number of important application areas.

关键词： Silicon Cellular neural networks Shape Active contours Laboratories Microprocessors Object detection Image reconstruction Echocardiography automation

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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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International Conference on Pattern Recognition

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

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.

关键词： Anisotropic magnetoresistance Cellular neural networks Image segmentation Image processing computer networks Pixel Labeling Smoothing methods Laboratories automation

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Motion segmentation and tracking optimization with edge relaxation in the cellular nonlinear network architecture

Motion segmentation and tracking optimization with edge rela...

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

作者： L. Czuni T. Sziranyi Department of Image Processing and Neurocomputing University of Veszpém Veszprem Hungary Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest Hungary

We have developed a high-level set of cellular neural net (CNN) functions for finding the segment-borders of moving objects through spatio-temporal relaxation optimization. They are analogic algorithms based on simple CNN instructions considering their implementability in analogic VLSI chips. Motion information extraction from video series is very power consuming. Most computing effort is devoted to motion vector field estimation, object definition and boundary determination. Finding interrelations among small segments, obtained by oversegmentation, involves optimization through merging or separation. In our algorithm the process starts from an oversegmented image, then the segments are merged using information from spatial and temporal auxiliary data: motion fields and motion history. This grouping process is based on neighboring segment similarity in color, speed and time-depth. There is also a feedback to accept or refuse the cancellation of a segment-border. Our parallel approach is independent of the number of segments or objects. We use simple VLSI functions. We develop grouping by stochastic optimization. This relaxation-based motion segmentation can be a basic step of the effective coding of image-series and other automatic motion tracking systems. The proposed system is planned to implement in a cellular nonlinear network chip-set architecture.

关键词： Motion segmentation computer vision Tracking Image segmentation Cellular neural networks Very large scale integration neural networks Data mining Motion estimation Merging

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Diminishment and enlargement of binary pictures using slightly space variant cellular neural network architecture

Diminishment and enlargement of binary pictures using slight...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： C. Rekeczky I. Ushida T. Roska Department of Electrical and Electronics Engineering Faculty of Engineering University of Tokushima Tokushima Japan Analogical and Neural Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Size modification of binary pictures can be mapped onto the CNN array using space variant linear templates. However, if all the parameters have to be set for each cell individually, then one of the CNN's main advantages will be lost in practice, the simple and quick parallel reprogrammability. In this paper, a general methodology is presented to derive the space variant templates of the complete weighting matrix from control pictures applying a simple nonlinear space invariant template. The straightforward design method presumes a modified CNN architecture (multiple input and specific nonlinear voltage-controlled current sources in every cell) and can be extended for a large class of sparse weighting matrices. Following this strategy the diminishment and enlargement process has been investigated using constant cell current and various bias maps in the transformations.

关键词： Cellular neural networks Optical sensors Process control Output feedback Control systems

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