| 1. Identificação | ||||
| Tipo de Referência | Artigo em Revista Científica (Journal Article) | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identificador | 8JMKD3MGP3W34T/4B8K9PE | |||
| Repositório | sid.inpe.br/mtc-m21d/2024/05.03.14.08 (acesso restrito) | |||
| Última Atualização | 2024:05.03.14.08.15 (UTC) simone | |||
| Repositório de Metadados | sid.inpe.br/mtc-m21d/2024/05.03.14.08.15 | |||
| Última Atualização dos Metadados | 2024:05.05.15.16.41 (UTC) administrator | |||
| DOI | 10.1016/j.jnlest.2024.100248 | |||
| Chave de Citação | BatistaÖbSaCaShSö:2024:MaLeAl | |||
| Título | Machine learning algorithm partially reconfigured on FPGA for an image edge detection system  | |||
| Ano | 2024 | |||
| Mês | June | |||
| Data de Acesso | 02 jun. 2024 | |||
| Tipo de Trabalho | journal article | |||
| Número de Arquivos | 1 | |||
| Tamanho | 3465 KiB | |||
| 2. Contextualização | ||||
| Autor | 1 Batista, Gracieth Cavalcanti 2 Öberg, Johnny 3 Saotome, Osamu 4 Campos Velho, Haroldo Fraga de 5 Shiguemori, Elcio Hideiti 6 Söderquist, Ingemar | |||
| Identificador de Curriculo | 1 2 3 4 8JMKD3MGP5W/3C9JHC3 | |||
| Grupo | 1 2 3 4 COPDT-CGIP-INPE-MCTI-GOV-BR | |||
| Afiliação | 1 KTH Royal Institute of Technology 2 KTH Royal Institute of Technology 3 Instituto Tecnológico de Aeronáutica (ITA) 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 KTH Royal Institute of Technology | |||
| Endereço de e-Mail do Autor | 1 gracieth@kth.se 2 johnnyob@kth.se 3 osaotome@ita.br 4 haroldo.camposvelho@inpe.br 5 elciohs@gmail.com 6 ingemar.soderquist@saabgroup.com | |||
| Revista | Journal of Electronic Science and Technology | |||
| Volume | 22 | |||
| Número | 2 | |||
| Páginas | e100248 | |||
| Histórico (UTC) | 2024-05-03 14:09:01 :: simone -> administrator :: 2024 2024-05-05 15:16:41 :: administrator -> simone :: 2024 | |||
| 3. Conteúdo e estrutura | ||||
| É a matriz ou uma cópia? | é a matriz | |||
| Estágio do Conteúdo | concluido | |||
| Transferível | 1 | |||
| Tipo do Conteúdo | External Contribution | |||
| Tipo de Versão | publisher | |||
| Palavras-Chave | Dynamic partial reconfiguration (DPR) Field programmable gate array (FPGA) implementation Image edge detection Support vector regression (SVR) Unmanned aerial vehicle (UAV) pose estimation | |||
| Resumo | Unmanned aerial vehicles (UAVs) have been widely used in military, medical, wireless communications, aerial surveillance, etc. One key topic involving UAVs is pose estimation in autonomous navigation. A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system (GNSS) signal. However, some factors can interfere with the GNSS signal, such as ionospheric scintillation, jamming, or spoofing. One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images. But a high effort is required for image edge extraction. In this paper, a support vector regression (SVR) model is proposed to reduce this computational load and processing time. The dynamic partial reconfiguration (DPR) of part of the SVR datapath is implementated to accelerate the process, reduce the area, and analyze its granularity by increasing the grain size of the reconfigurable region. Results show that the implementation in hardware is 68 times faster than that in software. This architecure with DPR also facilitates the low power consumption of 4 mW, leading to a reduction of 57% than that without DPR. This is also the lowest power consumption in current machine learning hardware implementations. Besides, the circuitry area is 41 times smaller. SVR with Gaussian kernel shows a success rate of 99.18% and minimum square error of 0.0146 for testing with the planning trajectory. This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application, thus contributing to lower power consumption, smaller hardware area, and shorter execution time. | |||
| Área | COMP | |||
| Arranjo | Projeto Memória 60... > CGIP > Machine learning algorithm... | |||
| Conteúdo da Pasta doc | acessar | |||
| Conteúdo da Pasta source | não têm arquivos | |||
| Conteúdo da Pasta agreement |
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| 4. Condições de acesso e uso | ||||
| Idioma | en | |||
| Arquivo Alvo | 1-s2.0-S1674862X24000168-main.pdf | |||
| Grupo de Usuários | simone | |||
| Grupo de Leitores | administrator simone | |||
| Visibilidade | shown | |||
| Permissão de Leitura | deny from all and allow from 150.163 | |||
| Permissão de Atualização | não transferida | |||
| 5. Fontes relacionadas | ||||
| Repositório Espelho | urlib.net/www/2021/06.04.03.40.25 | |||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/46KUES5 | |||
| Lista de Itens Citando | sid.inpe.br/mtc-m21/2012/07.13.14.49.40 1 | |||
| Acervo Hospedeiro | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notas | ||||
| Campos Vazios | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination e-mailaddress format isbn issn label lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | simone | |||
| atualizar | ||||