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Real-life application case studies using CMOS 0.8 /spl mu/m CNN universal chip: analogic algorithm for motion detection and texture segmentation

Real-life application case studies using CMOS 0.8 /spl mu/m ...

引用

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

作者： P. Foldesy A. Zarandy P. Szolgay T. Sziranyi Analogical and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Using a 20/spl times/22 CNN Universal Machine chip two application case studies are presented. A new analogic CNN algorithm is shown to detect objects having larger size than a given value on black-and-white image sequences moving in a given range of direction and speed (17 /spl mu/s processing speed could be achieved). An extremely fast texture classification analogic algorithm is given next with approximately 2 /spl mu/s processing speed and with less than 5% misclassification error rate for 4 natural textures in a real-life testing environment.

关键词： computer aided software engineering Cellular neural networks Switches Logic Motion detection Turing machines Testing Prototypes Registers Thyristors

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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...

引用

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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Optimal CNN templates for deconvolution

Optimal CNN templates for deconvolution

引用

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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An adaptive resonance theory-based neural network capable of learning via representational redescription

An adaptive resonance theory-based neural network capable of...

引用

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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Multi-target tracking with stored program adaptive CNN universal machines

Multi-target tracking with stored program adaptive CNN unive...

引用

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

作者： C. Rekeczky G. Timar G. Cserey Analogical and Neural Computing Systems Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

This paper shows that the performance of multi-target tracking (MTT) systems can be significantly increased with stored program adaptive cellular array sensors. The primary motivation of the present work is to define a topographic microprocessor architecture for MTT with embedded sensors capable of operating in a process real-time manner. In the ongoing experiments it is assumed that the input data flow is acquired by a single array sensor and the data is processed on an adaptive CNN-UM architecture consisting of both a cellular nonlinear network (CNN) and digital signal processing (DSP) microprocessors. The algorithms designed for this combined hardware platform use adaptive multi-channel CNN solutions for instantaneous position estimation and morphological characterization of all visible targets and the DSP environment for distance calculation, gating, data association, track maintenance and dynamic target motion prediction. A special feature of the architecture is that it allows interactive communication between the sensor and the digital environment. The configuration of functional modules for various real-time applications is discussed. Using real-life video-flows, successful tracking of several maneuvering targets is demonstrated within the proposed adaptive multi-channel framework.

关键词： Cellular neural networks Turing machines Sensor arrays Digital signal processing Adaptive arrays Microprocessors Sensor phenomena and characterization Target tracking Sensor systems Cellular networks

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An optical CNN implementation with stored programmability

An optical CNN implementation with stored programmability

引用

IEEE International Symposium on Circuits and systems (ISCAS)

作者： S. Tokes L. Orzo C. Rekeczky T. Roska A. Zarandy Analogical & Neural Computing Systems Laboratory at the Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

The objective of this paper is to provide a framework for the implementation of programmable Opto-Electronic Analogic CNN (POAC) computers embedding CNN Universal Chips. Specifically, a new method for optical CNN implementation is provided and some details are experimentally studied. The POAC architecture includes the integration of an optical processing system, such as a joint transform correlator, with the fast spatio-temporal processing capabilities of a CNN-UM chip. We have built and tested an optical sub-unit of this experimental optoelectronic architecture to examine their processing capabilities for complex target recognition tasks. Preliminary result of these measurements will also be presented. The main idea is to introduce stored programmability into optical computing.

关键词： Cellular neural networks Nonlinear optics Optical filters Optical network units Correlators computer architecture Embedded computing Optical computing Image processing Target recognition

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Implementing a retinal visual language in CNN: A neuromorphic study

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

引用

2001 IEEE International Symposium on Circuits and systems, ISCAS 2001

作者： Werblin, F. Roska, B. Balya, D. Rekeczky, C. Roska, T. Vision Research Laboratory Department of Molecular and Cell Biology University of California at Berkeley Berkeley CA-94720 United States Analogical and Neural Computing Laboratory Computer and Automation Research Institute Lagymanyosi U. 11 Budapest H-1111 Hungary

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. © 2001 IEEE.

关键词： Visual languages

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Short circuit detection on printed circuit boards during the manufacturing process by using an analogic CNN algorithm 14th

引用

14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert systems, IEA/AIE 2001

作者： Hidvégi, Timót Szolgay, Péter Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences P.O.B 63 BudapestH-1502 Hungary

ISBN: (纸本)3540422196

One of the most critical errors is the short circuit in the manufacturing of printed circuit boards. In this contribution we extend the already existing solution to more general cases where the errors can be detected by checking two production layers. The algorithm was tested with software simulator and 64*64 CNN-UM chip of ALADDIN System. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Printed circuit boards

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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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2001 IEEE International Symposium on Circuits and systems, ISCAS 2001

作者： Rekeczky, Csaba Szatmari, Istvan Foldesy, Peter Analogical and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences Kende U. 13-17 Budapest 1111 Hungary Jedlik Laboratories Department of Information Technology Peter Pazmany Catholic University Szentkirdyi U. 28 Budapest 1088 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. © 2001 IEEE.

关键词： Silicon

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An analogic CNN engine board with the 64/spl times/64 analog I/O CNN-UM chip

An analogic CNN engine board with the 64/spl times/64 analog...

引用

IEEE International Symposium on Circuits and systems (ISCAS)

作者： I. Szatmari A. Zarandy P. Foldesy L. Kek Analogical and Neural Computing Laboratory of the Computer Automation Research Institute of the Hungarian Academy of Science Budapest Hungary

A computational framework was built around the new 64/spl times/64 CNN Universal Chip designed in Seville. It makes possible the systematic functional test of the new complex chip, analogic CNN algorithm development and testing. Using this complex software-hardware system as a vehicle, the full algorithmic capabilities of the CNN technology can be demonstrated, which opens the gate to industrial and commercial applications.

关键词： Cellular neural networks Engines System testing Microprocessors Analog computers CMOS process Image processing Prototypes Laboratories Design automation

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