Exploring decoding using Machine Learning

Detalhes bibliográficos
Autor(a) principal: Tafoyem, Willy Kevin
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10773/37345
Resumo: In recent years, machine learning has become one of the most rapidly expanding technologies in a variety of technological fields. In general, it allows a computer to learn from data without being expressly designed for a particular purpose. This thesis investigates the application of decoding methods inspired by machine learning to linear block codes, such as Reed-Muller (RM) codes.
id RCAP_62ac9ce12eeef3fae1b640ee24af2b3e
oai_identifier_str oai:ria.ua.pt:10773/37345
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Exploring decoding using Machine LearningCoding theoryMachine learningReed-MullerDecodingNeural networksReinforcement learningIn recent years, machine learning has become one of the most rapidly expanding technologies in a variety of technological fields. In general, it allows a computer to learn from data without being expressly designed for a particular purpose. This thesis investigates the application of decoding methods inspired by machine learning to linear block codes, such as Reed-Muller (RM) codes.Recentemente, o Machine Learning tornou-se uma das tecnologias em mais rápida expansão numa variedade de campos tecnológicos. Em geral, permite que um computador aprenda com os dados sem ser expressamente concebido para um fim específico. Esta dissertação investiga a aplicação de métodos de descodificação inspirados no Machine Learning a códigos de blocos lineares, tais como os códigos de Reed-Muller.2023-04-26T13:25:10Z2022-11-21T00:00:00Z2022-11-21info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/37345engTafoyem, Willy Kevininfo: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:RCAAP2024-05-06T04:44:45Zoai:ria.ua.pt:10773/37345Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T14:18:54.208187Repositórios 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 Exploring decoding using Machine Learning
title Exploring decoding using Machine Learning
spellingShingle Exploring decoding using Machine Learning
Tafoyem, Willy Kevin
Coding theory
Machine learning
Reed-Muller
Decoding
Neural networks
Reinforcement learning
title_short Exploring decoding using Machine Learning
title_full Exploring decoding using Machine Learning
title_fullStr Exploring decoding using Machine Learning
title_full_unstemmed Exploring decoding using Machine Learning
title_sort Exploring decoding using Machine Learning
author Tafoyem, Willy Kevin
author_facet Tafoyem, Willy Kevin
author_role author
dc.contributor.author.fl_str_mv Tafoyem, Willy Kevin
dc.subject.por.fl_str_mv Coding theory
Machine learning
Reed-Muller
Decoding
Neural networks
Reinforcement learning
topic Coding theory
Machine learning
Reed-Muller
Decoding
Neural networks
Reinforcement learning
description In recent years, machine learning has become one of the most rapidly expanding technologies in a variety of technological fields. In general, it allows a computer to learn from data without being expressly designed for a particular purpose. This thesis investigates the application of decoding methods inspired by machine learning to linear block codes, such as Reed-Muller (RM) codes.
publishDate 2022
dc.date.none.fl_str_mv 2022-11-21T00:00:00Z
2022-11-21
2023-04-26T13:25:10Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/37345
url http://hdl.handle.net/10773/37345
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833594490262126592

Registros relacionados