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检索条件"机构=Analogical and Neural Computing Laboratory. Computer and Automation institute"

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Detecting global spatial-temporal events: saccadic suppression

Detecting global spatial-temporal events: saccadic suppressi...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： D. Balya Analogical & Neural Computing Laboratory Computer and Automation Research Institute Budapest Hungary

You may think that while are watching a schema, your eyes move smoothly and continuously to investigate the circuitry, but that is not the case. Your eyes fixate on a part of the circuit hold for a moment then suddenly jump to a new position where a particular visual detail attracts your eyes. During this jump or saccade the eyes move rapidly and we hardly notice that, relative to our eye position, the entire visual world globally shifts. Vision is in some sense turned off during saccades, which is called saccadic suppression. This paper presents a neuromorphic algorithm to detect these spatial-temporal events from the input flow. The algorithm of the saccadic suppression event detection is implemented on the standard cellular nonlinear network universal machine environment. The processing time is roughly one millisecond on the ACE4k CNN-UM chip in the Aladdin environment, and therefore it can serve as a common function in any real-time spatial-temporal algorithm. The saccadic suppression event detector can be used in different areas such as video shot detection or to define the instant when the time-dependent part of the algorithm, e.g. homotopy, has to be initialized.

关键词： Event detection Eyes Change detection algorithms Lighting Retina Circuits Gunshot detection systems Layout Cameras Switches

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A realistic mammalian retinal model implemented on complex cell CNN universal machine

A realistic mammalian retinal model implemented on complex c...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： D. Balya C. Rekeczky T. Roska Analogical & Neural Computing Laboratory Computer and Automation Research Institute Budapest Hungary

The visual system is probably the most important sensory modality for humans as well as for mammals. Its first and best-known part is the retina, which is not a mere photoreceptor or static camera but a sophisticated feature preprocessor with a continuous input and several parallel output channels. These channels build up a "visual language" and any realistic mammalian retina model should generate the elements of this visual language. The framework of mammalian retinal modeling via multi-layer CNN has been recently published. In the present paper we show the transformation of this model into a CNN-UM algorithm and the design steps of the implementation of this complex visual language. The analogic algorithm consists of a series of different complex-cell CNN dynamics. The algorithm is feasible on a recently fabricated complex cell CNN-UM chip. The decomposition method of the multilayer mammalian retina model will be discussed in detail.

关键词： Retina Cellular neural networks Turing machines Visual system Humans Photoreceptors Cameras Data preprocessing Algorithm design and analysis Heuristic algorithms

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Supervised and unsupervised art-like classifications of binary vectors on the CNN universal machine

Supervised and unsupervised art-like classifications of bina...

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

作者： D. Balya T. Roska Analogical & Neural Computing Laboratory Computer and Automation Research Institute Budapest Hungary

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be applied very efficiently as a feature detector and also for post-processing the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can also be mapped to the CNN-UM. The designed analogic CNN algorithm is capable of classifying the extracted binary feature vectors keeping the advantages of the ART networks. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. Another CNN-UM algorithm is suggested for supervised classification. In addition to the two algorithms, a new "repair" function is proposed to reduce the number of the created classes. The presented binary feature vector classification is feasible on the existing standard CNN-UM chips.

关键词： Cellular neural networks Turing machines Robustness Subspace constraints Real time systems Cellular networks computer vision Object detection Detectors Object recognition

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Basic mammalian retinal effects on the prototype complex cell CNN universal machine

Basic mammalian retinal effects on the prototype complex cel...

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

作者： D. Balya Cs. Rekeczky T. Roska Analogical & Neural Computing Laboratory Computer and Automation Research Institute Budapest Hungary

ISBN: (纸本)981238121X

The unique possibility for reconstructing the first stage of the visual system on a programmable silicon chip is described. The developed mammalian retinal model can be implemented as an analogic algorithm running on a prototype complex cell cellular neural network processor. It enables the neuro-biological and vision research communities to study the wisdom of biological visual transformations design in real-time. The operating prototype complex-cell CNN-UM processor opens a new world for the engineering as well as the computational neuroscience communities. This paper demonstrates the first steps in this direction. Here we present the decomposition and scaling of one retinal channel as a hardware-level CNN-UM algorithm. The analogic algorithm consists of a series of different complex-cell CNN spatial-temporal dynamics, feasible on the recently fabricated prototype complex cell CNN-UM chip.

关键词： Retina Prototypes Cellular neural networks Turing machines Neurons Target tracking Transfer functions

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Bio-inspired flight control and visual search with CNN technology

Bio-inspired flight control and visual search with CNN techn...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： C. Rekeczky D. Balya G. Timar I. Szatmari AnaLogical and Neural Computing Systems Laboratory Computer and Automation Institute of the Hungarian Academy of Sciences Budapest Hungary Analogical and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

In this paper it is shown that by building on parallel topographic CNN preprocessing of image flows, efficient terrain exploration and visual navigation algorithms can be developed. The approach combines several channels of nonlinear spatio-temporal feature detectors within an analogic CNN algorithm and produces unique binary maps of salient feature locations. This preprocessing scheme is embedded into a multi-target tracking (MTT) framework where these features are statistically described and assigned to numbered tracks. The MTT output has two distinct roles. First, its feature descriptors drive a classifier based on the adaptive resonance theory (ART), which is also implemented on CNN architecture. Second, it provides an optical flow ("target displacement") estimate to the navigation system, which in turn calculates the flight control parameters (Yaw-Pitch-Roll). An upper level visual attention and selection mechanism uses both the feature descriptors and the optical flow estimates to automatically adjust the focus and scale (zoom) during navigation. The paper describes the architecture and the algorithmic frameworks and provides the first experimental results on aerial video-flows.

关键词： Aerospace control Cellular neural networks Navigation Target tracking Biomedical optical imaging Nonlinear optics Image motion analysis computer vision Detectors Resonance

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Transient response computation of a mechanical vibrating system using cellular neural networks

Transient response computation of a mechanical vibrating sys...

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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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Implementing a retinal visual language in CNN: A neuromorphic study

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

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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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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...

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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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On the rectangular grid representation of general CNN networks

On the rectangular grid representation of general CNN networ...

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

作者： A.G. Radvanyi Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Although the cellular neural net (CNN) paradigm in its original form provides a suitable framework for investigating problems defined on arbitrary regular grids, the neural chips available or under design and the available simulators are all restricted to a rectangular structure. It is not at all self-evident, however, that the rectangular structure is the most suitable to represent every practical problems. In this paper we demonstrate that several CNNs of various regular grids can be mapped onto the typical eight-neighbour rectangular one, by applying weight matrices of periodic space-variance. By adopting this option, the applicability of cellular neural chips and simulators can be extended to investigate problems of essentially arbitrary grid structures.

关键词： Cellular neural networks Transmission line matrix methods Laboratories Design automation computer simulation neural network hardware computer languages Counting circuits

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Collision prediction via the CNN Universal Machine

Collision prediction via the CNN Universal Machine

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

作者： V. Gal T. Roska Analogical & Neural Computing Systems Laboratory at the computer Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Hungary

We present an analogic CNN algorithm that estimates the time to an impending collision between an approaching object and the observer. Calculation is based on a context insensitive method, which is well known in neuro... 详细信息

关键词： Cellular neural networks Turing machines Animals Equations Geometrical optics Object detection Image edge detection Detectors Pixel Optical arrays

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