Pub Date : 2002-07-22DOI: 10.1109/CNNA.2002.1035041
A. Selikhov
A mL-CNN is presented in this paper as a generalization of CNN models of reaction-diffusion processes in nonlinear media with m components. Main properties of the model are considered in accordance with imaginations of the process "mechanisms". Two particular CNN models, an autonomous 2L-CNN and a 2L-CNN with external inputs, are presented as examples of special cases of the mL-CNN. Emergence of some complex phenomena in such particular models are also shown.
{"title":"mL-CNN: a CNN model for reaction-diffusion processes in m-component systems","authors":"A. Selikhov","doi":"10.1109/CNNA.2002.1035041","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035041","url":null,"abstract":"A mL-CNN is presented in this paper as a generalization of CNN models of reaction-diffusion processes in nonlinear media with m components. Main properties of the model are considered in accordance with imaginations of the process \"mechanisms\". Two particular CNN models, an autonomous 2L-CNN and a 2L-CNN with external inputs, are presented as examples of special cases of the mL-CNN. Emergence of some complex phenomena in such particular models are also shown.","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":"126103336","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.2002.1035097
Z. Zhang, E.M. Namba, S. Takatori, H. Kawabata
Setting the optimal values of the neighborhood is an important factor for improving a CNN's capability. In this paper, we propose a new design method for the neighborhood, which reduces the computation time while maintaining its capability. In order to examine its effectiveness, we use synthesized model patterns and confirm whether the efficiency is improved. In addition, we apply the CNN designed to diagnosing abnormal sounds and obtained very encouraging results.
{"title":"A new design method for the neighborhood on improving the CNN's efficiency","authors":"Z. Zhang, E.M. Namba, S. Takatori, H. Kawabata","doi":"10.1109/CNNA.2002.1035097","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035097","url":null,"abstract":"Setting the optimal values of the neighborhood is an important factor for improving a CNN's capability. In this paper, we propose a new design method for the neighborhood, which reduces the computation time while maintaining its capability. In order to examine its effectiveness, we use synthesized model patterns and confirm whether the efficiency is improved. In addition, we apply the CNN designed to diagnosing abnormal sounds and obtained very encouraging results.","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":"126057599","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.2002.1035059
R. Kunz, C. Niederhofer, R. Tetzlaff
In this contribution, a novel approach for the prediction of epileptic seizures is introduced using binary input-output patterns and Boolean CNN with linear weight functions. Two different algorithms are introduced and verified on invasive recordings of different patients.
{"title":"Prediction of epileptic seizures by CNN with linear weight functions","authors":"R. Kunz, C. Niederhofer, R. Tetzlaff","doi":"10.1109/CNNA.2002.1035059","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035059","url":null,"abstract":"In this contribution, a novel approach for the prediction of epileptic seizures is introduced using binary input-output patterns and Boolean CNN with linear weight functions. Two different algorithms are introduced and verified on invasive recordings of different patients.","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":"115102199","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.2002.1035079
M. Laiho, A. Paasio, A. Kananen, K. Halonen
In this paper realization of couplings between cells in a polynomial type mixed-mode cellular neural network (CNN) is analyzed. One quadrant operation is required from the analog multipliers and polynomial circuits because in a mixed-mode CNN extension to four quadrant operation can be done digitally. A one quadrant multiplier is analyzed and simulated with HSPICE. Furthermore, circuits for generating second and third order polynomial terms of cell output are analyzed and HSPICE simulations are shown.
{"title":"Realization of couplings in a polynomial type mixed-mode CNN","authors":"M. Laiho, A. Paasio, A. Kananen, K. Halonen","doi":"10.1109/CNNA.2002.1035079","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035079","url":null,"abstract":"In this paper realization of couplings between cells in a polynomial type mixed-mode cellular neural network (CNN) is analyzed. One quadrant operation is required from the analog multipliers and polynomial circuits because in a mixed-mode CNN extension to four quadrant operation can be done digitally. A one quadrant multiplier is analyzed and simulated with HSPICE. Furthermore, circuits for generating second and third order polynomial terms of cell output are analyzed and HSPICE simulations are shown.","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":"114763673","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.2002.1035105
M. Jakubowski, S. Jankowski
This paper presents the design of a digital integrated circuit implementation of fully programmable cellular neural network for binary images processing. It consists of 16/spl times/16 cells and the memory able to store the image. The circuit is design in the standard cell style CMOS 0.35 /spl mu/m technology. The advantages of the digital CNN are: high reliability and robustness to the manufacturing parameters disturbances in comparison with analogue implementation. The disadvantages of this approach are: higher power consumption and larger IC silicon area. The paper presents the architecture of the network, as well as its components, the estimated system parameters (calculation speed, power consumption and density of cells) in comparison to selected CNN designs.
{"title":"Design of IC implementation of 16/spl times/16 CNN with serial-parallel input","authors":"M. Jakubowski, S. Jankowski","doi":"10.1109/CNNA.2002.1035105","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035105","url":null,"abstract":"This paper presents the design of a digital integrated circuit implementation of fully programmable cellular neural network for binary images processing. It consists of 16/spl times/16 cells and the memory able to store the image. The circuit is design in the standard cell style CMOS 0.35 /spl mu/m technology. The advantages of the digital CNN are: high reliability and robustness to the manufacturing parameters disturbances in comparison with analogue implementation. The disadvantages of this approach are: higher power consumption and larger IC silicon area. The paper presents the architecture of the network, as well as its components, the estimated system parameters (calculation speed, power consumption and density of cells) in comparison to selected CNN designs.","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":"122466229","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.2002.1035071
M. Salerno, F. Sargeni, V. Bonaiuto
In this paper a hardware implementation of a PDE analogue simulator is presented. In particular, this circuit is able to manage reaction-diffusion partial differential equations by using a cellular nonlinear network (CNN).
{"title":"PDE-DPCNN: a CNN chip for analogue simulations of RD equations","authors":"M. Salerno, F. Sargeni, V. Bonaiuto","doi":"10.1109/CNNA.2002.1035071","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035071","url":null,"abstract":"In this paper a hardware implementation of a PDE analogue simulator is presented. In particular, this circuit is able to manage reaction-diffusion partial differential equations by using a cellular nonlinear network (CNN).","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":"124878504","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.2002.1035039
D. L. Vilariño, D. Cabello, V. Brea
This paper addresses the application of the pixel level snakes for the segmentation of moving objects. This kind of active contour techniques can handle multiple contours simultaneously without time-processing penalty as well as to manage appropriately the topologic transformations among them when this is required. The implementation into a CNNUM or a specific purpose CNN platform gives solution to the speed requirements of this kind of tasks. Particularly, we show an analogic CNN-algorithm which meets all the constrains imposed for the current CNNUM hardware implementations.
{"title":"An analogic CNN-algorithm of pixel level snakes for tracking and surveillance tasks","authors":"D. L. Vilariño, D. Cabello, V. Brea","doi":"10.1109/CNNA.2002.1035039","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035039","url":null,"abstract":"This paper addresses the application of the pixel level snakes for the segmentation of moving objects. This kind of active contour techniques can handle multiple contours simultaneously without time-processing penalty as well as to manage appropriately the topologic transformations among them when this is required. The implementation into a CNNUM or a specific purpose CNN platform gives solution to the speed requirements of this kind of tasks. Particularly, we show an analogic CNN-algorithm which meets all the constrains imposed for the current CNNUM hardware implementations.","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":"121990831","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.2002.1035051
G. Grassi, L.A. Grieco
For pt.I see ibid., p.172-9 (2002). In the context of image analysis for object-oriented coding schemes, this paper presents new analogic CNN algorithms for implementing the image synthesis and consistency observation stages. Along with the motion estimation algorithm illustrated in the companion paper, the proposed approach represents a framework for implementing CNN-based real-time image analysis. Simulation results, carried out for Miss America video sequence, confirm the validity of the algorithms developed herein.
{"title":"Object-oriented image analysis via analogic CNN algorithms. II. Image synthesis and consistency observation","authors":"G. Grassi, L.A. Grieco","doi":"10.1109/CNNA.2002.1035051","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035051","url":null,"abstract":"For pt.I see ibid., p.172-9 (2002). In the context of image analysis for object-oriented coding schemes, this paper presents new analogic CNN algorithms for implementing the image synthesis and consistency observation stages. Along with the motion estimation algorithm illustrated in the companion paper, the proposed approach represents a framework for implementing CNN-based real-time image analysis. Simulation results, carried out for Miss America video sequence, confirm the validity of the algorithms developed herein.","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":"128490751","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.2002.1035037
L. Koskinen, M. Laiho, A. Paasio, K. Halonen
The suitability of an existing cellular nonlinear network (CNN) chip for MPEG-4 core profile shape segmentation is investigated. The chip and the algorithm it is based on are found to be suitable for shape segmentation and additional templates are proposed to enhance the chip's MPEG-4 suitability. Additional uses for the CNN chip are found in MPEG-4 encoder computational power demand reduction.
{"title":"MPEG-4 based modifications for an CNN segmentation chip","authors":"L. Koskinen, M. Laiho, A. Paasio, K. Halonen","doi":"10.1109/CNNA.2002.1035037","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035037","url":null,"abstract":"The suitability of an existing cellular nonlinear network (CNN) chip for MPEG-4 core profile shape segmentation is investigated. The chip and the algorithm it is based on are found to be suitable for shape segmentation and additional templates are proposed to enhance the chip's MPEG-4 suitability. Additional uses for the CNN chip are found in MPEG-4 encoder computational power demand reduction.","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":"127396716","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.2002.1035075
E. Bilgili, O. Nucan, A. Muhittin Albora, I. Cem Goknar
In this paper, multi-level genetic cellular neural networks (ML-GCNN) are applied to the geophysical problem of potential anomaly separation and satisfactory results are obtained, compared to classical deterministic approaches. ML-GCNN is a stochastic image processing technique which is based on template optimisation using neighbourhood relationships of the pixels. The residual anomaly separation used in location decisions is one of the main problems in geophysics. The method proposed here is used in evaluating the Dumluca iron ore region of Turkey.
{"title":"Potential anomaly separation using genetically trained multi-level cellular neural networks","authors":"E. Bilgili, O. Nucan, A. Muhittin Albora, I. Cem Goknar","doi":"10.1109/CNNA.2002.1035075","DOIUrl":"https://doi.org/10.1109/CNNA.2002.1035075","url":null,"abstract":"In this paper, multi-level genetic cellular neural networks (ML-GCNN) are applied to the geophysical problem of potential anomaly separation and satisfactory results are obtained, compared to classical deterministic approaches. ML-GCNN is a stochastic image processing technique which is based on template optimisation using neighbourhood relationships of the pixels. The residual anomaly separation used in location decisions is one of the main problems in geophysics. The method proposed here is used in evaluating the Dumluca iron ore region of Turkey.","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":"127001808","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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