1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)最新文献_第5页

Image segmentation based on active contours using discrete time cellular neural networks 基于活动轮廓的离散时间细胞神经网络图像分割

1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359) Pub Date : 1998-04-14 DOI: 10.1109/CNNA.1998.685396

D. L. Vilariño, D. Cabello, M. Balsi, V. Brea

引用次数: 21

Precise edge detection: representation by Boolean functions, implementation on the CNN 精确边缘检测:用布尔函数表示，在CNN上实现

1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359) Pub Date : 1998-04-14 DOI: 10.1109/CNNA.1998.685391

I. Aizenberg, N. Aizenberg, J. Vandewalle

引用次数: 15

Estimation of the basin of attractions of stable equilibrium points in CNNs cnn中稳定平衡点吸引盆地的估计

1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359) Pub Date : 1998-04-14 DOI: 10.1109/CNNA.1998.685331

V. Mladenov, D. Leenaerts

引用次数: 1

Local Activity: The Origin of Complexity 局部活动:复杂性的起源

1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359) Pub Date : 1998-04-14 DOI: 10.1109/CNNA.1998.685319

L. Chua

{"title":"Local Activity: The Origin of Complexity","authors":"L. Chua","doi":"10.1109/CNNA.1998.685319","DOIUrl":"https://doi.org/10.1109/CNNA.1998.685319","url":null,"abstract":"Many scientists have struggled to uncover the elusive origin of “complexity”, and its many equivalent jargons, such as emergence, self-organization, synergetics, collective behaviors, nonequilibrium phenomena, etc. They have provided some qualitative, but not quantitative, characterizations of numerous fascinating examples from many disciplines. For example, Schrodinger had identified “the exchange of energy” from open systems as a necessary condition for complexity. Prigogine has argued for the need to introduce a new principle of nature which he dubbed “the instability of the homogeneous”. Turing had proposed “symmetry breaking” as an origin of morphogenesis. Smale had asked what “axiomatic” properties must a reaction–diffusion system possess to make the Turing interacting system oscillate. The purpose of this paper is to show that all the jargons and issues cited above are mere manifestations of a new fundamental principle called local activity, which is mathematically precise and testable. The local activity theorem provides the quantitative characterization of Prigogine’s “instability of the homogeneous” and Smale’s quest for an axiomatic principle on Turing instability. Among other things, a mathematical proof is given which shows none of the complexityrelated jargons cited above is possible without local activity. Explicit mathematical criteria are given to identify a relatively small subset of the locally-active parameter region, called the edge of chaos, where most complex phenomena emerge.","PeriodicalId":171485,"journal":{"name":"1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125919597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Threshold class CNNs with input-dependent initial state 具有输入依赖初始状态的阈值类cnn

1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359) Pub Date : 1998-04-14 DOI: 10.1109/CNNA.1998.685349

I. Genç, C. Guzelis

{"title":"Threshold class CNNs with input-dependent initial state","authors":"I. Genç, C. Guzelis","doi":"10.1109/CNNA.1998.685349","DOIUrl":"https://doi.org/10.1109/CNNA.1998.685349","url":null,"abstract":"This paper introduces a special class of cellular neural networks (CNNs) where cells are uncoupled and they are initialized depending on their weighted input level. An uncoupled CNN cell operating in the bipolar output mode defines a discrete-valued perceptron whose threshold is determined by the initial condition. CNNs of uncoupled cells, so called linear threshold class CNNs, can be trained by perceptron learning rule for searching optimum template values in linearly separable input cases. However, just like perceptron, conventional linear threshold class CNNs can not perform the classification of linearly nonseparable input cases. However, just like perceptron, conventional linear threshold class CNN cannot perform the classification of linearly nonseparable input sets. To overcome this problem, we choose the initial states of the considered CNNs as piecewise constant functions of the external inputs so that a cell defines a modified perceptron having an input-dependent threshold. We show that such linear threshold class CNNs can perform some linearly nonseparable threshold functions. The results obtained by the experiments done on edge detection problem justify our design method.","PeriodicalId":171485,"journal":{"name":"1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128044397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A programmable g/sub m/-C CNN implementation 一种可编程的g/sub / c CNN实现

1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359) Pub Date : 1998-04-14 DOI: 10.1109/CNNA.1998.685390

D. Lim, G. Moschytz

引用次数: 4

A novel compact architecture for a programmable full-range CNN in 0.5 /spl mu/m CMOS technology 采用0.5 /spl mu/m CMOS技术的可编程全范围CNN的新颖紧凑架构

1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359) Pub Date : 1998-04-14 DOI: 10.1109/CNNA.1998.685389

J. A. Hegt, D. Leenaerts, R.T. Wilmans

引用次数: 7

A robustness study of a CNN based stereo vision algorithm 基于CNN的立体视觉算法鲁棒性研究

1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359) Pub Date : 1998-04-14 DOI: 10.1109/CNNA.1998.685395

A. Zanela, S. Taraglio

引用次数: 3

High speed calculation of cryptographic hash functions by CNN chips CNN芯片高速计算密码哈希函数

1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359) Pub Date : 1998-04-14 DOI: 10.1109/CNNA.1998.685361

M. Csapodi, J. Vandewalle, T. Roska

引用次数: 8

On the chip implementation of analogic algorithms for optical detection of some layout errors of printed circuit boards 在芯片上实现了模拟算法对印刷电路板的一些布局误差进行光学检测

1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359) Pub Date : 1998-04-14 DOI: 10.1109/CNNA.1998.685356

K. Tomordi, P. Foldesy, P. Szolgay

引用次数: 3