Cellular Neural Networks design for sensor networks.

Bibliographic Details
Main Author: João Luís Fernandes Ferreira
Publication Date: 2021
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://hdl.handle.net/10216/135338
Summary: The concept of Cellular Neural Networks was proposed by Leon O. Chua and Lin Yang in 1988, and it has ever since seen only relatively modest but promising improvements. The goal of this work is to delve further into this subject, which we consider to be largely unexplored, and to try to make a positive contribution to it. We also consider that these advancements fill an important gap in the context of the recent breakthrough we have been witnessing in the field of artificial intelligence and data fusion.
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spelling Cellular Neural Networks design for sensor networks.Engenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringThe concept of Cellular Neural Networks was proposed by Leon O. Chua and Lin Yang in 1988, and it has ever since seen only relatively modest but promising improvements. The goal of this work is to delve further into this subject, which we consider to be largely unexplored, and to try to make a positive contribution to it. We also consider that these advancements fill an important gap in the context of the recent breakthrough we have been witnessing in the field of artificial intelligence and data fusion.2021-07-162021-07-16T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/135338TID:202821226engJoão Luís Fernandes Ferreirainfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-27T17:37:02Zoai:repositorio-aberto.up.pt:10216/135338Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T22:20:47.397532Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Cellular Neural Networks design for sensor networks.
title Cellular Neural Networks design for sensor networks.
spellingShingle Cellular Neural Networks design for sensor networks.
João Luís Fernandes Ferreira
Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
title_short Cellular Neural Networks design for sensor networks.
title_full Cellular Neural Networks design for sensor networks.
title_fullStr Cellular Neural Networks design for sensor networks.
title_full_unstemmed Cellular Neural Networks design for sensor networks.
title_sort Cellular Neural Networks design for sensor networks.
author João Luís Fernandes Ferreira
author_facet João Luís Fernandes Ferreira
author_role author
dc.contributor.author.fl_str_mv João Luís Fernandes Ferreira
dc.subject.por.fl_str_mv Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
topic Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
description The concept of Cellular Neural Networks was proposed by Leon O. Chua and Lin Yang in 1988, and it has ever since seen only relatively modest but promising improvements. The goal of this work is to delve further into this subject, which we consider to be largely unexplored, and to try to make a positive contribution to it. We also consider that these advancements fill an important gap in the context of the recent breakthrough we have been witnessing in the field of artificial intelligence and data fusion.
publishDate 2021
dc.date.none.fl_str_mv 2021-07-16
2021-07-16T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
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dc.identifier.uri.fl_str_mv https://hdl.handle.net/10216/135338
TID:202821226
url https://hdl.handle.net/10216/135338
identifier_str_mv TID:202821226
dc.language.iso.fl_str_mv eng
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