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Optical detection of breaks and short circuits on the layouts of printed circuit boards using CNN

Optical detection of breaks and short circuits on the layout...

引用

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

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

The printed circuit board layout inspection methods are mostly based on local geometric information, therefore it is well suited to the cellular neural network (CNN) paradigm. Two layout errors are detected here namely, the breaks in the wires and some kind of short circuits. The designed analogic algorithms to solve the problems above were tested on real life examples using an experimental system based on our CNN-HAC1M digital multiprocessor add-on-board, with 1 million cell space and 2.0 /spl mu/s/cell/iteration speed.

关键词： Optical detectors Cellular neural networks Geometrical optics Optical computing Printed circuits Inspection Wires Algorithm design and analysis Circuit testing Life testing

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Statistical subpixel pattern recognition by histograms

Statistical subpixel pattern recognition by histograms

引用

International Conference on Pattern Recognition

作者： T. Sziranyi Dual and Neural Computing Systems Research Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

A new statistical pattern recognition method has been developed for detection, recognition or measurement of patterns which are (much) smaller than the measure of the elementary pixel windows in the image screen. In this measurement the gray-level histogram of the objects examined is compared with the simulated histograms of different (in type or size) possible objects, and the recognition (of shape or measure) is taken on the basis of the comparison. This method does not need ultra-precise movement of the scanning sensors or any additional hardwares. Moreover, the examined pattern should be randomly distributed on the screen, or a random movement of camera (or target or both) is needed. Effect of noises are analyzed, and filtering processes are suggested in the histogram domain. Several examples of different shapes are presented through simulations and experiments.< >

关键词： Pattern recognition Histograms Shape measurement Size measurement Image recognition Pixel Hardware Cameras Noise shaping Filtering

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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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Elastic wave propagation modeling on emulated digital CNN-UM architecture

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

引用

9th IEEE International Workshop on Cellular neural Networks and their Applications, CNNA

作者： Kozma, Péter Sonkoly, Péter Szolgay, Péter Department of Image Processing and Neurocomputing University of Veszprém Egyetem u. 10 H-8200 Veszprém Hungary Department of Image Processing and Neurocomputing University of Veszprém Analogic and Neural Computing Laboratory Computer and Automation Institute HAS Kende u. 13-17 H-1111 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： Cellular neural networks

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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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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...

引用

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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Configurable multi-layer CNN-UM emulator on FPGA

Configurable multi-layer CNN-UM emulator on FPGA

引用

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

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

A new emulated digital multi-layer CNN-UM chip architecture called Falcon has been developed. In this paper the main steps of the FPGA implementation are introduced. Main results are as follows: CNN-UM architecture emulated on Xilinx Virtex series FPGA, 3D non-linear spatio-temporal dynamics can be implemented on this architecture. The critical parameters of the implementation in single layer configuration are 55 million cell update/second/processor core or equivalently 1 GOPS computing performance. In face of the high performance the power requirements of the architecture are relatively low only /spl sim/3 W per processor core. Using re-configurable devices to implement emulated digital architectures provides more flexibility compared to the custom VLSI designs because different Falcon architectures can be used on the same FPGA device.

关键词： Field programmable gate arrays Cellular neural networks Equations computer architecture Very large scale integration Signal processing Analog computers State feedback Laboratories Turing machines

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

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

引用

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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A new CNN template for POAC peak enhancement

A new CNN template for POAC peak enhancement

引用

European Conference on Circuit Theory and Design, ECCTD

作者： A. Ayoub S. Tokes Analogic and Neural Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Magyar Tudomanyos Akademia Szamitastechnikai es Automatizalasi Kutatointezet Budapest Budapest HU

We propose a CNN template to enhance the POAC output (correlogram peaks) for higher detection accuracy. Our recursive algorithm found the optimum template values under the one-third condition. The statistical, simulation and practical results confirmed that the template: enhances the correlogram peaks' detection; is fast for real time applications; is suitable to work under several illuminations; and avoids the complexity of adaptive thresholding.

关键词： Cellular neural networks Band pass filters Optical feedback Equations Lighting Optical computing Optical filters Computational modeling Adaptive optics Optical fiber networks

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