0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/103496 https://doi.org/10.3390/s22103657 |
Resumo: | Persistent Homology (PH) analysis is a powerful tool for understanding many relevant topological features from a given dataset. PH allows finding clusters, noise, and relevant connections in the dataset. Therefore, it can provide a better view of the problem and a way of perceiving if a given dataset is equal to another, if a given sample is relevant, and how the samples occupy the feature space. However, PH involves reducing the problem to its simplicial complex space, which is computationally expensive and implementing PH in such Resource-Scarce Embedded Systems (RSES) is an essential add-on for them. However, due to its complexity, implementing PH in such tiny devices is considerably complicated due to the lack of memory and processing power. The following paper shows the implementation of 0-Dimensional Persistent Homology Analysis in a set of well-known RSES, using a technique that reduces the memory footprint and processing power needs of the 0-Dimensional PH algorithm. The results are positive and show that RSES can be equipped with this real-time data analysis tool. |
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0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systemspersistent homologytopological data analysisembedded intelligenceintelligent resource-scarce embedded systemsTinyMLAlgorithmsPersistent Homology (PH) analysis is a powerful tool for understanding many relevant topological features from a given dataset. PH allows finding clusters, noise, and relevant connections in the dataset. Therefore, it can provide a better view of the problem and a way of perceiving if a given dataset is equal to another, if a given sample is relevant, and how the samples occupy the feature space. However, PH involves reducing the problem to its simplicial complex space, which is computationally expensive and implementing PH in such Resource-Scarce Embedded Systems (RSES) is an essential add-on for them. However, due to its complexity, implementing PH in such tiny devices is considerably complicated due to the lack of memory and processing power. The following paper shows the implementation of 0-Dimensional Persistent Homology Analysis in a set of well-known RSES, using a technique that reduces the memory footprint and processing power needs of the 0-Dimensional PH algorithm. The results are positive and show that RSES can be equipped with this real-time data analysis tool.MDPI2022-05-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/103496http://hdl.handle.net/10316/103496https://doi.org/10.3390/s22103657eng1424-8220Branco, SérgioCarvalho, João G.Reis, Marco S.Lopes, Nuno V.Cabral, Jorgeinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-02-24T17:27:07Zoai:estudogeral.uc.pt:10316/103496Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:20:19.311709Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems |
title |
0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems |
spellingShingle |
0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems Branco, Sérgio persistent homology topological data analysis embedded intelligence intelligent resource-scarce embedded systems TinyML Algorithms |
title_short |
0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems |
title_full |
0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems |
title_fullStr |
0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems |
title_full_unstemmed |
0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems |
title_sort |
0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems |
author |
Branco, Sérgio |
author_facet |
Branco, Sérgio Carvalho, João G. Reis, Marco S. Lopes, Nuno V. Cabral, Jorge |
author_role |
author |
author2 |
Carvalho, João G. Reis, Marco S. Lopes, Nuno V. Cabral, Jorge |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Branco, Sérgio Carvalho, João G. Reis, Marco S. Lopes, Nuno V. Cabral, Jorge |
dc.subject.por.fl_str_mv |
persistent homology topological data analysis embedded intelligence intelligent resource-scarce embedded systems TinyML Algorithms |
topic |
persistent homology topological data analysis embedded intelligence intelligent resource-scarce embedded systems TinyML Algorithms |
description |
Persistent Homology (PH) analysis is a powerful tool for understanding many relevant topological features from a given dataset. PH allows finding clusters, noise, and relevant connections in the dataset. Therefore, it can provide a better view of the problem and a way of perceiving if a given dataset is equal to another, if a given sample is relevant, and how the samples occupy the feature space. However, PH involves reducing the problem to its simplicial complex space, which is computationally expensive and implementing PH in such Resource-Scarce Embedded Systems (RSES) is an essential add-on for them. However, due to its complexity, implementing PH in such tiny devices is considerably complicated due to the lack of memory and processing power. The following paper shows the implementation of 0-Dimensional Persistent Homology Analysis in a set of well-known RSES, using a technique that reduces the memory footprint and processing power needs of the 0-Dimensional PH algorithm. The results are positive and show that RSES can be equipped with this real-time data analysis tool. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-05-11 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/103496 http://hdl.handle.net/10316/103496 https://doi.org/10.3390/s22103657 |
url |
http://hdl.handle.net/10316/103496 https://doi.org/10.3390/s22103657 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1424-8220 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134095911944192 |