Design of neuron-bipolar junction transistor (νBJT) cellular neural network (CNN) structure with multi-neighborhood-layer templates

Wen Cheng Yen; Chung-Yu Wu

doi:10.1109/CNNA.2000.876844

Design of neuron-bipolar junction transistor (νBJT) cellular neural network (CNN) structure with multi-neighborhood-layer templates

Wen Cheng Yen^*, Chung-Yu Wu

^*此作品的通信作者

研究成果 › 同行評審

6 引文斯高帕斯（Scopus）

摘要

A compact and efficient neuron-bipolar junction transistor (νBJT) cellular neural network (CNN) structure with multi-neighborhood-layer templates is proposed and analyzed. Using the proposed structure, the coefficients of the templates with two neighborhood layers are fully realizable. But those with more than two neighborhood layers are constrained. As the demonstrative examples on the applications of the proposed vBJT CNNs, the functions of both de-blurring and muller-lyer arrowhead illusion functions have been successfully realized and verified by HSPICE simulation.

原文	English
頁面	195-200
頁數	6
DOIs	https://doi.org/10.1109/CNNA.2000.876844
出版狀態	Published - 5月 2000
事件	Proceedings of the 2000 6th IEEE International Workshop on Cellular Neural Network and their Applications (CNNA 2000) - Catania, Italy 持續時間: 23 5月 2000 → 25 5月 2000

Conference

Conference	Proceedings of the 2000 6th IEEE International Workshop on Cellular Neural Network and their Applications (CNNA 2000)
城市	Catania, Italy
期間	23/05/00 → 25/05/00

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10.1109/CNNA.2000.876844

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title = "Design of neuron-bipolar junction transistor (νBJT) cellular neural network (CNN) structure with multi-neighborhood-layer templates",

abstract = "A compact and efficient neuron-bipolar junction transistor (νBJT) cellular neural network (CNN) structure with multi-neighborhood-layer templates is proposed and analyzed. Using the proposed structure, the coefficients of the templates with two neighborhood layers are fully realizable. But those with more than two neighborhood layers are constrained. As the demonstrative examples on the applications of the proposed vBJT CNNs, the functions of both de-blurring and muller-lyer arrowhead illusion functions have been successfully realized and verified by HSPICE simulation.",

author = "Yen, {Wen Cheng} and Chung-Yu Wu",

year = "2000",

month = may,

doi = "10.1109/CNNA.2000.876844",

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pages = "195--200",

note = "Proceedings of the 2000 6th IEEE International Workshop on Cellular Neural Network and their Applications (CNNA 2000) ; Conference date: 23-05-2000 Through 25-05-2000",

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Yen, WC & Wu, C-Y 2000, 'Design of neuron-bipolar junction transistor (νBJT) cellular neural network (CNN) structure with multi-neighborhood-layer templates', 論文發表於 Proceedings of the 2000 6th IEEE International Workshop on Cellular Neural Network and their Applications (CNNA 2000), Catania, Italy, 23/05/00 - 25/05/00 頁 195-200. https://doi.org/10.1109/CNNA.2000.876844

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AU - Wu, Chung-Yu

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N2 - A compact and efficient neuron-bipolar junction transistor (νBJT) cellular neural network (CNN) structure with multi-neighborhood-layer templates is proposed and analyzed. Using the proposed structure, the coefficients of the templates with two neighborhood layers are fully realizable. But those with more than two neighborhood layers are constrained. As the demonstrative examples on the applications of the proposed vBJT CNNs, the functions of both de-blurring and muller-lyer arrowhead illusion functions have been successfully realized and verified by HSPICE simulation.

AB - A compact and efficient neuron-bipolar junction transistor (νBJT) cellular neural network (CNN) structure with multi-neighborhood-layer templates is proposed and analyzed. Using the proposed structure, the coefficients of the templates with two neighborhood layers are fully realizable. But those with more than two neighborhood layers are constrained. As the demonstrative examples on the applications of the proposed vBJT CNNs, the functions of both de-blurring and muller-lyer arrowhead illusion functions have been successfully realized and verified by HSPICE simulation.

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Design of neuron-bipolar junction transistor (νBJT) cellular neural network (CNN) structure with multi-neighborhood-layer templates

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