Pattern recognition on FPGA for aerospace applications
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/19181 |
Resumo: | This paper presents a low power near real-time pattern recognition technique based on Mathematical Morphology-MM implemented on FPGA (Field Programmable Gate Array). The key to the success of this approach concerns the advantages of machine learning paradigm applied to the translation invariant template-matching operators from MM. The paper shows that compositions of simple elementary operators from Mathematical Morphology based on ELUTs (Elementary Look-Up Tables) are very suitable to embed in FPGA hardware. The paper also shows the development techniques regarding all mathematical modeling for computer simulation and system generating models applied for hardware implementation using FPGA chip. In general, image processing on FPGAs requires low-level description of desired operations through Hardware Description Language-HDL, which uses high complexity to describe image operations at pixel level. However, this work presents a reconfiguring pattern recognition device implemented directly in FPGA from mathematical modeling simulation under Matlab/Simulink/System Generator environment. This strategy has reduced the hardware development complexity. The device will be useful mainly when applied on remote sensing tasks for aerospace missions using passive or active sensors. |
id |
UNIFEI_46fc4c68ca2465cfd38e5689a3494cc3 |
---|---|
oai_identifier_str |
oai:ojs.pkp.sfu.ca:article/19181 |
network_acronym_str |
UNIFEI |
network_name_str |
Research, Society and Development |
repository_id_str |
|
spelling |
Pattern recognition on FPGA for aerospace applicationsReconocimiento de patrones en FPGA para aplicaciones aeroespacialesReconhecimento de padrões em FPGA para aplicações aeroespaciaisSatélites inteligentesInteligência artificial em hardwareVisão computacionalInteligência artificial em tempo realAprendizagem de máquinaNanosatélitesMorfologia matemáticaAplicações aeroespaciaisReconhecimento de padrõesSensoriamento remoto.Intelligent satellitesNanosatellitesArtificial intelligence in hardwareComputer visionMachine learningMathematical morphologyPattern recognitionReal time systemsAerospace applicationsRemote sensing.Satélites inteligentesNanosatélitesInteligencia artificial en hardwareVisión artificialAprendizaje automáticaMorfología matemáticaReconocimiento de patronesInteligencia artificial en tiempo realAplicaciones aeroespacialesThis paper presents a low power near real-time pattern recognition technique based on Mathematical Morphology-MM implemented on FPGA (Field Programmable Gate Array). The key to the success of this approach concerns the advantages of machine learning paradigm applied to the translation invariant template-matching operators from MM. The paper shows that compositions of simple elementary operators from Mathematical Morphology based on ELUTs (Elementary Look-Up Tables) are very suitable to embed in FPGA hardware. The paper also shows the development techniques regarding all mathematical modeling for computer simulation and system generating models applied for hardware implementation using FPGA chip. In general, image processing on FPGAs requires low-level description of desired operations through Hardware Description Language-HDL, which uses high complexity to describe image operations at pixel level. However, this work presents a reconfiguring pattern recognition device implemented directly in FPGA from mathematical modeling simulation under Matlab/Simulink/System Generator environment. This strategy has reduced the hardware development complexity. The device will be useful mainly when applied on remote sensing tasks for aerospace missions using passive or active sensors.El presente trabajo presenta una técnica de reconocimiento de patrones en tiempo real basada en Morfología Matemática-MM implementada en FPGA (Field Programmable Gate Array). La estrategia para la efectividad de este enfoque tiene que ver con las ventajas del paradigma de aprendizaje automática aplicada al modelo de correspondencia con la invariancia traslacional de operadores elementales da MM. El artículo muestra que las composiciones de operadores elementales simples de morfología matemática basadas en ELUT (tablas de consulta elementales) son adecuadas para integrarse en dispositivos FPGA. Este artículo también muestra técnicas de desarrollo de sistemas de reconocimiento de patrones, desde el modelado matemático de operadores morfológicos hasta la implementación del dispositivo electrónico utilizando el software System Generator. En general, las operaciones para el procesamiento de imágenes en FPGAs se implementan a un bajo nivel de abstracción de los lenguajes de descripción del hardware-HDL. Esto crea una gran complejidad en la implementación de operaciones en imágenes a nivel de píxeles. Sin embargo, este trabajo presenta un dispositivo reconfigurable de reconocimiento de patrones implementado directamente en FPGA a partir de simulación de modelado matemático en el software Matlab/Simulink/System Generator. Esta estrategia ha reducido la complejidad del desarrollo de hardware. El dispositivo será útil principalmente cuando se aplique en tareas de teledetección para misiones aeroespaciales utilizando sensores pasivos o activos.Esse trabalho apresenta uma técnica de reconhecimento de padrões baseada em Morfologia Matemática-MM, implementada em FPGA (Field Programmable Gate Array). A estratégia para o êxito dessa abordagem consiste na utilização das vantagens do paradigma de aprendizagem de máquina aplicado em operadores morfológicos de casamento de padrões invariantes à translação. Esse artigo mostra que a composição de simples operadores elementares da MM baseados em ELUTS (Elementary Look-Up Tables) são adequados para aplicações embarcadas em FPGA. Esse artigo também mostra as técnicas de desenvolvimento do sistema de reconhecimento de padrões, desde a modelagem matemática dos operadores morfológicos até a implementação do dispositivo eletrônico usando o software System Generator. Em geral, as operações para o processamento de imagens em FPGAs são implementadas em baixo nível de abstração das linguagens de descrição de hardware-HDL. Isto gera alta complexidade na implementação de operações em imagens ao nível de pixel. No entanto, esse trabalho apresenta um dispositivo reconfigurável aplicado ao reconhecimento de padrões implementado em FPGA, a partir da simulação da modelagem matemática usando o ambiente de software Matlab/Simulink/System Generator. Essa estratégia reduz a complexidade do desenvolvimento em hardware. O dispositivo apresentado deverá ser útil principalmente quando aplicado em tarefas de sensoriamento remoto para missões aeroespaciais através de sensores passivos ou ativos.Research, Society and Development2021-09-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1918110.33448/rsd-v10i12.19181Research, Society and Development; Vol. 10 No. 12; e83101219181Research, Society and Development; Vol. 10 Núm. 12; e83101219181Research, Society and Development; v. 10 n. 12; e831012191812525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIenghttps://rsdjournal.org/index.php/rsd/article/view/19181/18001Copyright (c) 2021 Francisco de Assis Tavares Ferreira da Silva; Magno Prudêncio de Almeida Filho; Antonio Macilio Pereira de Lucena; Alexandre Guirland Nowosadhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSilva, Francisco de Assis Tavares Ferreira da Almeida Filho, Magno Prudêncio de Lucena, Antonio Macilio Pereira deNowosad, Alexandre Guirland 2021-11-14T20:26:51Zoai:ojs.pkp.sfu.ca:article/19181Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:39:08.703603Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Pattern recognition on FPGA for aerospace applications Reconocimiento de patrones en FPGA para aplicaciones aeroespaciales Reconhecimento de padrões em FPGA para aplicações aeroespaciais |
title |
Pattern recognition on FPGA for aerospace applications |
spellingShingle |
Pattern recognition on FPGA for aerospace applications Silva, Francisco de Assis Tavares Ferreira da Satélites inteligentes Inteligência artificial em hardware Visão computacional Inteligência artificial em tempo real Aprendizagem de máquina Nanosatélites Morfologia matemática Aplicações aeroespaciais Reconhecimento de padrões Sensoriamento remoto. Intelligent satellites Nanosatellites Artificial intelligence in hardware Computer vision Machine learning Mathematical morphology Pattern recognition Real time systems Aerospace applications Remote sensing. Satélites inteligentes Nanosatélites Inteligencia artificial en hardware Visión artificial Aprendizaje automática Morfología matemática Reconocimiento de patrones Inteligencia artificial en tiempo real Aplicaciones aeroespaciales |
title_short |
Pattern recognition on FPGA for aerospace applications |
title_full |
Pattern recognition on FPGA for aerospace applications |
title_fullStr |
Pattern recognition on FPGA for aerospace applications |
title_full_unstemmed |
Pattern recognition on FPGA for aerospace applications |
title_sort |
Pattern recognition on FPGA for aerospace applications |
author |
Silva, Francisco de Assis Tavares Ferreira da |
author_facet |
Silva, Francisco de Assis Tavares Ferreira da Almeida Filho, Magno Prudêncio de Lucena, Antonio Macilio Pereira de Nowosad, Alexandre Guirland |
author_role |
author |
author2 |
Almeida Filho, Magno Prudêncio de Lucena, Antonio Macilio Pereira de Nowosad, Alexandre Guirland |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Silva, Francisco de Assis Tavares Ferreira da Almeida Filho, Magno Prudêncio de Lucena, Antonio Macilio Pereira de Nowosad, Alexandre Guirland |
dc.subject.por.fl_str_mv |
Satélites inteligentes Inteligência artificial em hardware Visão computacional Inteligência artificial em tempo real Aprendizagem de máquina Nanosatélites Morfologia matemática Aplicações aeroespaciais Reconhecimento de padrões Sensoriamento remoto. Intelligent satellites Nanosatellites Artificial intelligence in hardware Computer vision Machine learning Mathematical morphology Pattern recognition Real time systems Aerospace applications Remote sensing. Satélites inteligentes Nanosatélites Inteligencia artificial en hardware Visión artificial Aprendizaje automática Morfología matemática Reconocimiento de patrones Inteligencia artificial en tiempo real Aplicaciones aeroespaciales |
topic |
Satélites inteligentes Inteligência artificial em hardware Visão computacional Inteligência artificial em tempo real Aprendizagem de máquina Nanosatélites Morfologia matemática Aplicações aeroespaciais Reconhecimento de padrões Sensoriamento remoto. Intelligent satellites Nanosatellites Artificial intelligence in hardware Computer vision Machine learning Mathematical morphology Pattern recognition Real time systems Aerospace applications Remote sensing. Satélites inteligentes Nanosatélites Inteligencia artificial en hardware Visión artificial Aprendizaje automática Morfología matemática Reconocimiento de patrones Inteligencia artificial en tiempo real Aplicaciones aeroespaciales |
description |
This paper presents a low power near real-time pattern recognition technique based on Mathematical Morphology-MM implemented on FPGA (Field Programmable Gate Array). The key to the success of this approach concerns the advantages of machine learning paradigm applied to the translation invariant template-matching operators from MM. The paper shows that compositions of simple elementary operators from Mathematical Morphology based on ELUTs (Elementary Look-Up Tables) are very suitable to embed in FPGA hardware. The paper also shows the development techniques regarding all mathematical modeling for computer simulation and system generating models applied for hardware implementation using FPGA chip. In general, image processing on FPGAs requires low-level description of desired operations through Hardware Description Language-HDL, which uses high complexity to describe image operations at pixel level. However, this work presents a reconfiguring pattern recognition device implemented directly in FPGA from mathematical modeling simulation under Matlab/Simulink/System Generator environment. This strategy has reduced the hardware development complexity. The device will be useful mainly when applied on remote sensing tasks for aerospace missions using passive or active sensors. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09-14 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/19181 10.33448/rsd-v10i12.19181 |
url |
https://rsdjournal.org/index.php/rsd/article/view/19181 |
identifier_str_mv |
10.33448/rsd-v10i12.19181 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/19181/18001 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 10 No. 12; e83101219181 Research, Society and Development; Vol. 10 Núm. 12; e83101219181 Research, Society and Development; v. 10 n. 12; e83101219181 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
_version_ |
1797052825396576256 |