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

作者： L.O. Chua T. Roska P.L. Venetianer A. Zarandy Department of Electrical Engineering and Computer Sciences and the Electronics Research Laboratory University of California Berkeley Berkeley CA USA Dual and Neural Computing Systems Laboratory Computer & Automation Institute (MTA SzTAKI) Research Division Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

The authors show that the game of life algorithm, which is equivalent to a Turing machine, can be realized by cellular neural networks (CNNs). They also present a multipath CNN algorithm that demonstrates the capabilities of analog/logic (analogic) software. Complex image processing tasks can be realized by programmable dual computing CNNs. A specific example of blob-counting in a gray-scale image is presented.< >

关键词： Cellular neural networks Turing machines Laboratories Logic Analog computers Biological system modeling automation Games Fading neural networks

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On the chip implementation of analogic algorithms for optical detection of some layout errors of printed circuit boards

On the chip implementation of analogic algorithms for optica...

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

作者： K. Tomordi P. Foldesy P. Szolgay Analogic and Neural Computing Systems Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Image Processing and Neurocomputing Department Faculty of Engineering University of Veszprém Veszprem Hungary

Printed circuit board layout inspection methods are mostly based on local geometric information, therefore they are well suited to the cellular neural networks (CNN) paradigm. The wire break, the wire and isolation width violation and an "H" type short circuits detector analogic algorithms were tested on a 20*22 CNN Universal Machine (CNNUM) chip working in the CNN Chip Prototyping System (CCPS) and on the CNN Engine Board (CNNEB), and the results were compared to the commercially available inspection systems.

关键词： Cellular neural networks Inspection Wire Circuit testing Geometrical optics Optical computing Printed circuits Detectors System testing Turing machines

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Optical tracking system for automatic guided vehicles using cellular neural networks

Optical tracking system for automatic guided vehicles using ...

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

作者： G. Eross T. Boros A. Kiss A. Radvanyi T. Roska J. Bito J. Vass Centre of Robotics and Automation Tungsram TH Company Limited Budapest Hungary Dual and Neural Computing Systems Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences Budapest Hungary Department of information Technology Faculty of Engineering University of Veszprém Veszprem Hungary

A method for the path-control of automated guided vehicles (AGVs) in computer integrated manufacturing (CIM) systems that combines the flexibility and easy installation of optical methods with the simplicity and robustness of the inductive method is proposed. Using a new computing paradigm, the cellular neural network (CNN), and a related device, the VLSI CNN chip, a very high speed solution that is less expensive than the conventional methods can be achieved. This AGV control complies with the requirements of CIM systems. Further advantages of the proposed system are as follows: fault tolerance and the ability to give instructions along the path, and the use of a simple local control.< >

关键词： Vehicles computer integrated manufacturing Cellular neural networks High speed optical techniques Control systems Integrated optics Optical computing Robustness computer networks Very large scale integration

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A biosensing system employing nanowell microelectrode arrays to record the intracellular potential of a single cardiomyocyte

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Microsystems & Nanoengineering 2022年第3期8卷 185-198页

作者： Yuting Xiang Haitao Liu Wenjian Yang Zhongyuan xu Yue Wu Zhajian Tang Zhjing Zhu Zhiyong Zeng Depeng Wang Tianxing Wang Ning Hu Diming Zhang Department of Obstetrics and Gynecology Affiliated Dongguan People's HospitalSouthern Medical UniversityDongguan 523058China Research Center for Inelligent Sensing Systems Zhejiang LaboratoryHangzhou 311100China Research Center for Humanoid Sensing Zhejiang LaboratoryHangzhou 311100China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejiang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China School of Automation Nanjing University of Science and TechnologyNanjing 210094China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-Link Care Meditech Co. LtdHangzhou 310011China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemitryZhejiang UniversityHangzhou 310058China

Electrophysiological recording is a widely used method to investigate cardiovascular pathology,pharmacology and developmental *** arrays record the electrical potential of cells in a minimally invasive and highthroughput ***,commonly used microelectrode arays primarily employ planar microelectrodes and cannot work in applications that require a recording of the intracellular action potential of a single *** this study,we proposed a novel measuring method that is able to record the intracellular action potential of a single cardiomyocyte by using a nanowell patterned microelectrode array(NWMEA).The NWMEA consists of five nanoscale wells at the center of each circular planar *** pulse electroporation was applied to the NWMEA to penetrate the cardiomyocyte membrane,and the intracellular action potential was continuously *** intracellular potential recording of cardiomyocytes by the NWMEA measured a potential signal with a higher quality(213.76±25.85%),reduced noise root-mean-square(~33%),and higher signal-to-noise ratio(254.36±12.61%)when compared to those of the extracellular *** to previously reported nanopillar microelectrodes,the NWMEA could ensure single cell electroporation and acquire high-quality action potential of cardiomyocytes with reduced fabrication *** NWMEA-based biosensing system is a promising tool to record the intracellular action potential of a single cell to broaden the usage of microelectrode arrays in electrophysiological investigation.

关键词： arrays potential record

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Implementation and test of human-operated and human-like adaptive impedance controls on Baxter robot

Implementation and test of human-operated and human-like ada...

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15th Annual Conference on Advances in Autonomous Robotics systems, TAROS 2014

作者： Liang, Peidong Yang, Chenguang Wang, Ning Li, Zhijun Li, Ruifeng Burdet, Etienne Centre for Robotics and Neural Systems School of Computing and Mathematics Plymouth University United Kingdom MOE Key Lab. of Autonomous System and Network Control College of Automation Science and Engineering South China University of Technology China State Key Laboratory of Robotics and System Harbin Institute of Technology China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Bioengineering Imperial College of Science Technology and Medicine London United Kingdom

ISBN: (纸本)9783319104003

This paper presents an improved method to teleoperate impedance of a robot based on surface electromyography (EMG) and test it experimentally. Based on a linear mapping between EMG amplitude and stiffness, an incremental stiffness extraction method is developed, which uses instantaneous amplitude identified from EMG in a high frequency band, compensating for non-linear residual error in the linear mapping and preventing muscle fatigue from affecting the control. Experiments on one joint of the Baxter robot are carried out to test the approach in a disturbance attenuation task, and to compare it with automatic human-like impedance adaptation. The experimental results demonstrate that the new human operated impedance method is successful at attenuating disturbance, and results similarly to as automatic disturbance attenuation, thus demonstrating its efficiency. © 2014 Springer International Publishing.

关键词： Stiffness

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CNN Wave computing: Theory, Architectures, Implementations and Applicaions

CNN Wave Computing: Theory, Architectures, Implementations a...

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

作者： Marco Gilli Csaba Rekeczky Bertram Shi Department of Electronics Politecnico di Turino Torino Italy Analogic and Neural Computing Systems Laboratory of the Computer and Automation Information Technology Department Institute of the Hungarian Academy of Sciences Budapest Hungary Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong China

A cellular neural/nonlinear network (CNN) is any spatial arrangement of mainly locally coupled cells, where each cell is a dynamical system which has an input, an output and a state that evolves according to some prescribed dynamical laws. Since the CNN was first introduced in 1988, research in this field has developed rapidly. The goal of this tutorial is to provide participants with a snapshot of the current state of the art and research trends. It covers the broad multi-disciplinary areas of CNN research, from theoretical aspects to applications. The presenters have experience ranging from theoretical analysis of CNN dynamics, VLSI implementation and system level applications

关键词： Cellular neural networks computer architecture computer networks Very large scale integration Analog computers computer interfaces computer vision Biological system modeling Nonlinear dynamical systems computer industry

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CNN-based difference-controlled adaptive non-linear image filters

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International Journal of Circuit Theory and Applications 1999年第4期26卷

作者： Csaba Rekeczky Tamás Roska Akio Ushida Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest Hungary Department of Electrical and Electronic Engineering Faculty of Engineering University of Tokushima Tokushima Japan

In this paper, we develop a common cellular neural network framework for various adaptive non-linear filters based on robust statistic and geometry-driven diffusion paradigms. The base models of both approaches are defined as difference-controlled non-linear CNN templates, while the self-adjusting property is ensured by simple analogic (analog and logic) CNN algorithms. Two adaptive strategies are shown for the order statistic class. When applied to the images distorted by impulse noise both give more visually pleasing results with lower-frequency weighted mean square error than the median base model. Generalizing a variational approach we derive the constrained anisotropic diffusion, where the output of the geometry-driven diffusion model is forced to stay close to a pre-defined morphological constraint. We propose a coarse-grid CNN approach that is capable of calculating an acceptable noise-level estimate (proportional to the variance of the Gaussian noise) and controlling the fine-grid anisotropic diffusion models. A combined geometrical–statistical approach has also been developed for filtering both the impulse and additive Gaussian noise while preserving the image structure. We briefly discuss how these methods can be embedded into a more complex algorithm performing edge detection and image segmentation. The design strategies are analysed primarily from VLSI implementation point of view; therefore all non-linear cell interactions of the CNN architecture are reduced to two fundamental non-linearities, to a sigmoid type and a radial basis function. The proposed non-linear characteristics can be approximated with simple piecewise-linear functions of the voltage difference of neighbouring cells. The simplification makes it possible to convert all space-invariant non-linear templates of this study to a standard instruction set of the CNN Universal Machine, where each instruction is coded by at most a dozen analog numbers. Examples and simulation results are given t

关键词： non-linear cellular neural networks CNN Universal Machine adaptive non-linear filters robust statistic filters geometry-driven diffusion

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Invertible operations on a Cellular neural Network Universal Machine

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International Journal of Circuit Theory and Applications 1999年第6期26卷

作者： Márton Csapodi Joos Vandewalle Tamás Roska Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences P.O. Box 63 H-1502 Budapest Hungary ESAT Laboratory Department of Electrical Engineering Katholieke Universiteit Leuven K. Mercierlaan 94 B-3001 Leuven Belgium

This paper is concerned with the invertibility of parallel operations definable on two-dimensional binary arrays by means of a local Boolean function. In this way, it contributes to the theory of cellular automata and may open new potentialities to cellular neural network applications. We provide a theoretically new approach to the invertibility of two-dimensional additive maps which can be used to obtain detailed information on the behaviour of these operations. Other ways to make invertible operations are also investigated, including local maps which are additive in only one of the terms, and second-order cellular automata. The discussed techniques yield a rich variety of invertible operations on infinite and/or finite two-dimensional binary arrays. When implemented on CNN Universal Chips, all of these operations, performed by TeraOPS speed, become subroutines of a 2D analogic algorithm. These operations can be used in 2D cryptography. © 1998 John Wiley & Sons, Ltd.

关键词： CNN universal machine two-dimensional CA additive maps complementary landscapes

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