Pub Date : 2002-07-22DOI: 10.1109/CNNA.2002.1035088
A. Rdriguez-Vazquez
CNN-based analog visual microprocessors have similarities with the so-called Single Instruction Multiple Data systems, although they work directly on analog signal representations obtained through embedded optical sensors and hence do not need a frontend sensory plane or analog-to-digital converters. The architecture of these visual microprocessors is illustrated in the paper through two prototype chips, namely: ACE4K and ACE16K. In both cases, as in other related chips the architecture includes a core array of interconnected elementary processing units, surrounded by a global circuitry.
{"title":"CMOS design of cellular APAPs and FPAPAPs: an overview","authors":"A. Rdriguez-Vazquez","doi":"10.1109/CNNA.2002.1035088","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035088","url":null,"abstract":"CNN-based analog visual microprocessors have similarities with the so-called Single Instruction Multiple Data systems, although they work directly on analog signal representations obtained through embedded optical sensors and hence do not need a frontend sensory plane or analog-to-digital converters. The architecture of these visual microprocessors is illustrated in the paper through two prototype chips, namely: ACE4K and ACE16K. In both cases, as in other related chips the architecture includes a core array of interconnected elementary processing units, surrounded by a global circuitry.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130386372","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}
Pub Date : 2002-07-22DOI: 10.1109/CNNA.2002.1035062
M. Haenggi
Large-scale networks of integrated wireless sensors and actuators are becoming increasingly tractable. Advances in hardware technology and engineering design have led to dramatic reductions in size, power consumption, and cost for digital circuitry, wireless communications, and MEMS. This enables very compact, autonomous, and mobile nodes, each containing one or more sensors and actuators, computation and communication capabilities, and a power supply. Networking is a crucial ingredient to harness these capabilities into a complete system. While wireless sensor networks have been studied for about a decade, their extension with actuators is a more recent thrust of research that greatly enhances their capabilities and range of applications, at the cost of requiring closed control loops that can cause instability and are subject to delay constraints. This article provides an overview of existing and emerging technologies, pointing out the opportunities and challenges of mobile integrated sensor-actuator networks and their relation to CNNs.
{"title":"Mobile sensor-actuator networks: opportunities and challenges","authors":"M. Haenggi","doi":"10.1109/CNNA.2002.1035062","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035062","url":null,"abstract":"Large-scale networks of integrated wireless sensors and actuators are becoming increasingly tractable. Advances in hardware technology and engineering design have led to dramatic reductions in size, power consumption, and cost for digital circuitry, wireless communications, and MEMS. This enables very compact, autonomous, and mobile nodes, each containing one or more sensors and actuators, computation and communication capabilities, and a power supply. Networking is a crucial ingredient to harness these capabilities into a complete system. While wireless sensor networks have been studied for about a decade, their extension with actuators is a more recent thrust of research that greatly enhances their capabilities and range of applications, at the cost of requiring closed control loops that can cause instability and are subject to delay constraints. This article provides an overview of existing and emerging technologies, pointing out the opportunities and challenges of mobile integrated sensor-actuator networks and their relation to CNNs.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132962964","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}
Pub Date : 2002-07-22DOI: 10.1109/CNNA.2002.1035032
F. Corinto, M. Gilli, P. Civalleri
Cellular neural networks (CNNs) are analog dynamic processors that have found several applications for the solution of complex computational problems. The mathematical model of a CNN consists in a large set of coupled nonlinear differential equations that have been mainly studied through numerical simulations; the knowledge of the dynamic behavior is essential for developing rigorous design methods and for establishing new applications. In most applications (such as image processing tasks) it is required that the CNN be stable, i.e. that after a transient all the trajectories tend to a constant value (with at most the exception of a set of measure zero). So far, three main CNN models have been proposed: the original Chua-Yang model, the full range model, that was exploited for VLSI implementation and the polynomial type model, which presents polynomial interactions among the cells. This manuscript is devoted to the study of the stability properties of polynomial type CNNs and to the comparison of such properties with those of Chua-Yang and of full range models.
{"title":"On stability of full range and polynomial type CNNs","authors":"F. Corinto, M. Gilli, P. Civalleri","doi":"10.1109/CNNA.2002.1035032","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035032","url":null,"abstract":"Cellular neural networks (CNNs) are analog dynamic processors that have found several applications for the solution of complex computational problems. The mathematical model of a CNN consists in a large set of coupled nonlinear differential equations that have been mainly studied through numerical simulations; the knowledge of the dynamic behavior is essential for developing rigorous design methods and for establishing new applications. In most applications (such as image processing tasks) it is required that the CNN be stable, i.e. that after a transient all the trajectories tend to a constant value (with at most the exception of a set of measure zero). So far, three main CNN models have been proposed: the original Chua-Yang model, the full range model, that was exploited for VLSI implementation and the polynomial type model, which presents polynomial interactions among the cells. This manuscript is devoted to the study of the stability properties of polynomial type CNNs and to the comparison of such properties with those of Chua-Yang and of full range models.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124180212","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}
Pub Date : 2002-07-22DOI: 10.1109/CNNA.2002.1035053
M. Bucolo, L. Fortuna, M. Frasca, M. La Rosa
In this paper a cellular neural network (CNN) based system to perform a real-time, parallel processing of magetoencephalographic data is proposed. In particular, a nonlinear approach to blind sources separation, instead of the linear procedure performed by independent component analysis, is introduced. Moreover, the characteristic spatial distribution of the cells in the CNN system has been exploited to reproduce the topology of the acquisition channels over the scalp.
{"title":"A CNN based system to blind sources separation of MEG signals","authors":"M. Bucolo, L. Fortuna, M. Frasca, M. La Rosa","doi":"10.1109/CNNA.2002.1035053","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035053","url":null,"abstract":"In this paper a cellular neural network (CNN) based system to perform a real-time, parallel processing of magetoencephalographic data is proposed. In particular, a nonlinear approach to blind sources separation, instead of the linear procedure performed by independent component analysis, is introduced. Moreover, the characteristic spatial distribution of the cells in the CNN system has been exploited to reproduce the topology of the acquisition channels over the scalp.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122890707","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}
Pub Date : 2002-07-22DOI: 10.1109/CNNA.2002.1035035
R. Caponetto, L. Fortuna, L. Occhipinti, M.G. Xibilia
A CNN based circuit for chaotic signal applications in communication systems is proposed. An hyperchaotic Saito oscillator has been designed by using a configuration of cellular neural networks named state-controlled CNNs. A communication system, based on chaotic inverse system synchronization, is described and the results, relative to a prototype circuit realization, are given.
{"title":"SC-CNNs for chaotic signal generation","authors":"R. Caponetto, L. Fortuna, L. Occhipinti, M.G. Xibilia","doi":"10.1109/CNNA.2002.1035035","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035035","url":null,"abstract":"A CNN based circuit for chaotic signal applications in communication systems is proposed. An hyperchaotic Saito oscillator has been designed by using a configuration of cellular neural networks named state-controlled CNNs. A communication system, based on chaotic inverse system synchronization, is described and the results, relative to a prototype circuit realization, are given.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128556300","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}
Pub Date : 2002-07-22DOI: 10.1109/CNNA.2002.1035081
M. Laiho, A. Paasio, A. Kananen, K. Halonen
In this paper A/D and D/A converters in a mixed-mode cellular neural network (CNN) are analyzed. The choice of A/D converter type is discussed and design characteristics associated with A/D converter design for a mixed-mode CNN are overviewed. A current mode successive approximation type A/D converter suitable for use in a mixed-mode CNN cell is shown. A current mode D/A converter is also shown in block level.
{"title":"A/D and D/A converters in a mixed-mode CNN","authors":"M. Laiho, A. Paasio, A. Kananen, K. Halonen","doi":"10.1109/CNNA.2002.1035081","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035081","url":null,"abstract":"In this paper A/D and D/A converters in a mixed-mode cellular neural network (CNN) are analyzed. The choice of A/D converter type is discussed and design characteristics associated with A/D converter design for a mixed-mode CNN are overviewed. A current mode successive approximation type A/D converter suitable for use in a mixed-mode CNN cell is shown. A current mode D/A converter is also shown in block level.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125174546","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}
Pub Date : 2002-07-22DOI: 10.1109/CNNA.2002.1035034
S. Arik
This paper presents a sufficient condition for the uniqueness and global asymptotic stability of the equilibrium point for delayed cellular neural networks, which improves the previous stability results derived in the literature.
本文给出了时滞细胞神经网络平衡点唯一性和全局渐近稳定的一个充分条件，改进了已有文献的稳定性结果。
{"title":"An improved global stability result for cellular neural networks with time delay","authors":"S. Arik","doi":"10.1109/CNNA.2002.1035034","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035034","url":null,"abstract":"This paper presents a sufficient condition for the uniqueness and global asymptotic stability of the equilibrium point for delayed cellular neural networks, which improves the previous stability results derived in the literature.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126339769","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}
Pub Date : 2002-07-22DOI: 10.1109/CNNA.2002.1035057
V. Gál, S. Grun, R. Tetzlaff
In this paper we show that CNN-UM is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: the occurrences of different patterns are first counted, then the statistical significance of each occurrence frequency is calculated separately.
{"title":"Analyzing multidimensional neural activity via CNN-UM","authors":"V. Gál, S. Grun, R. Tetzlaff","doi":"10.1109/CNNA.2002.1035057","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035057","url":null,"abstract":"In this paper we show that CNN-UM is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: the occurrences of different patterns are first counted, then the statistical significance of each occurrence frequency is calculated separately.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121790719","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}
Pub Date : 2002-07-22DOI: 10.1109/CNNA.2002.1035082
R. Carmona-Galán, F. Jiménez-Garrido, R. Domínguez-Castro, S. Espejo-Meana, Á. Rodríguez-Vázquez
Some of the features of the biological retina can be modelled by a cellular neural network (CNN) composed of two dynamically coupled layers of locally connected elementary nonlinear processors. In order to explore the possibilities of these complex spatio-temporal dynamics in image processing, a prototype chip has been developed by implementing this CNN model with analog signal processing blocks. This chip has been designed in a 0.5/spl mu/m CMOS technology. Design challenges, trade-offs and the building blocks of such a high-complexity system (0.5 /spl times/ 10/sup 6/ transistors, most of them operating in analog mode) are presented in this paper.
生物视网膜的一些特征可以通过由两个局部连接的基本非线性处理器动态耦合层组成的细胞神经网络(CNN)来建模。为了探索这些复杂的时空动态在图像处理中的可能性，通过使用模拟信号处理模块实现该CNN模型，开发了一个原型芯片。该芯片采用0.5/spl μ m CMOS工艺设计。本文介绍了这种高复杂性系统(0.5 /spl次/ 10/sup 6/个晶体管，其中大多数工作在模拟模式)的设计挑战，权衡和构建模块。
{"title":"CMOS realization of a 2-layer CNN universal machine chip","authors":"R. Carmona-Galán, F. Jiménez-Garrido, R. Domínguez-Castro, S. Espejo-Meana, Á. Rodríguez-Vázquez","doi":"10.1109/CNNA.2002.1035082","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035082","url":null,"abstract":"Some of the features of the biological retina can be modelled by a cellular neural network (CNN) composed of two dynamically coupled layers of locally connected elementary nonlinear processors. In order to explore the possibilities of these complex spatio-temporal dynamics in image processing, a prototype chip has been developed by implementing this CNN model with analog signal processing blocks. This chip has been designed in a 0.5/spl mu/m CMOS technology. Design challenges, trade-offs and the building blocks of such a high-complexity system (0.5 /spl times/ 10/sup 6/ transistors, most of them operating in analog mode) are presented in this paper.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129461493","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}
Pub Date : 2002-07-22DOI: 10.1109/CNNA.2002.1035064
Csaba Rekeczky, G. Tímár, G. Cserey
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.
{"title":"Multi-target tracking with stored program adaptive CNN universal machines","authors":"Csaba Rekeczky, G. Tímár, G. Cserey","doi":"10.1109/CNNA.2002.1035064","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035064","url":null,"abstract":"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.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129821455","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}
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