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Artigo de periodico
Identification and quantification of common adulterants in extra virgin olive oil using microwave dielectric spectroscopy aided by feedforward neural networks (2024)
Alarcon, Julio C. P.; Souza, Mateus Isaac de Oliveira; Pepino, Vinicius Marrara ; Borges, Ben-Hur Viana
Fonte: IEEE Sensors Journal. Unidade: EESC
Assuntos: REDES NEURAIS, ESPECTROSCOPIA, APRENDIZADO COMPUTACIONAL, AZEITE, ENGENHARIA ELÉTRICA
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ABNT
ALARCON, Julio C. P. et al. Identification and quantification of common adulterants in extra virgin olive oil using microwave dielectric spectroscopy aided by feedforward neural networks. IEEE Sensors Journal, p. 1-11, 2024Tradução . . Disponível em: http://dx.doi.org/10.1109/JSEN.2024.3448221. Acesso em: 10 out. 2024.
APA
Alarcon, J. C. P., Souza, M. I. de O., Pepino, V. M., & Borges, B. -H. V. (2024). Identification and quantification of common adulterants in extra virgin olive oil using microwave dielectric spectroscopy aided by feedforward neural networks. IEEE Sensors Journal, 1-11. doi:10.1109/JSEN.2024.3448221
NLM
Alarcon JCP, Souza MI de O, Pepino VM, Borges B-HV. Identification and quantification of common adulterants in extra virgin olive oil using microwave dielectric spectroscopy aided by feedforward neural networks [Internet]. IEEE Sensors Journal. 2024 ; 1-11.[citado 2024 out. 10 ] Available from: http://dx.doi.org/10.1109/JSEN.2024.3448221
Vancouver
Alarcon JCP, Souza MI de O, Pepino VM, Borges B-HV. Identification and quantification of common adulterants in extra virgin olive oil using microwave dielectric spectroscopy aided by feedforward neural networks [Internet]. IEEE Sensors Journal. 2024 ; 1-11.[citado 2024 out. 10 ] Available from: http://dx.doi.org/10.1109/JSEN.2024.3448221
Artigo de periodico
Adaptive algorithm for gait segmentation using a single IMU in the thigh pocket (2022)
Garcia, Francisco A; Pérez Ibarra, Juan Carlos ; Terra, Marco Henrique ; Siqueira, Adriano Almeida Gonçalves
Fonte: IEEE Sensors Journal. Unidade: EESC
Assuntos: ALGORITMOS, APRENDIZADO COMPUTACIONAL, PROCESSOS DE MARKOV, ENGENHARIA MECÂNICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GARCIA, Francisco A et al. Adaptive algorithm for gait segmentation using a single IMU in the thigh pocket. IEEE Sensors Journal, v. 22, n. 3, p. 13251-13261, 2022Tradução . . Disponível em: https://doi.org/10.1109/JSEN.2022.3177951. Acesso em: 10 out. 2024.
APA
Garcia, F. A., Pérez Ibarra, J. C., Terra, M. H., & Siqueira, A. A. G. (2022). Adaptive algorithm for gait segmentation using a single IMU in the thigh pocket. IEEE Sensors Journal, 22( 3), 13251-13261. doi:10.1109/JSEN.2022.3177951
NLM
Garcia FA, Pérez Ibarra JC, Terra MH, Siqueira AAG. Adaptive algorithm for gait segmentation using a single IMU in the thigh pocket [Internet]. IEEE Sensors Journal. 2022 ; 22( 3): 13251-13261.[citado 2024 out. 10 ] Available from: https://doi.org/10.1109/JSEN.2022.3177951
Vancouver
Garcia FA, Pérez Ibarra JC, Terra MH, Siqueira AAG. Adaptive algorithm for gait segmentation using a single IMU in the thigh pocket [Internet]. IEEE Sensors Journal. 2022 ; 22( 3): 13251-13261.[citado 2024 out. 10 ] Available from: https://doi.org/10.1109/JSEN.2022.3177951
Artigo de periodico
An open-source tool for classification models in resource-constrained hardware (2022)
Silva, Lucas Tsutsui da ; Souza, Vinícius Mourão Alves de ; Batista, Gustavo Enrique de Almeida Prado Alves
Fonte: IEEE Sensors Journal. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, SISTEMAS EMBUTIDOS, RECONHECIMENTO DE PADRÕES, SENSOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Lucas Tsutsui da e SOUZA, Vinícius Mourão Alves de e BATISTA, Gustavo Enrique de Almeida Prado Alves. An open-source tool for classification models in resource-constrained hardware. IEEE Sensors Journal, v. 22, n. Ja 2022, p. 544-554, 2022Tradução . . Disponível em: https://doi.org/10.1109/JSEN.2021.3128130. Acesso em: 10 out. 2024.
APA
Silva, L. T. da, Souza, V. M. A. de, & Batista, G. E. de A. P. A. (2022). An open-source tool for classification models in resource-constrained hardware. IEEE Sensors Journal, 22( Ja 2022), 544-554. doi:10.1109/JSEN.2021.3128130
NLM
Silva LT da, Souza VMA de, Batista GE de APA. An open-source tool for classification models in resource-constrained hardware [Internet]. IEEE Sensors Journal. 2022 ; 22( Ja 2022): 544-554.[citado 2024 out. 10 ] Available from: https://doi.org/10.1109/JSEN.2021.3128130
Vancouver
Silva LT da, Souza VMA de, Batista GE de APA. An open-source tool for classification models in resource-constrained hardware [Internet]. IEEE Sensors Journal. 2022 ; 22( Ja 2022): 544-554.[citado 2024 out. 10 ] Available from: https://doi.org/10.1109/JSEN.2021.3128130
Artigo de periodico
Roadmap for electrical impedance spectroscopy for sensing: a tutorial (2021)
Buscaglia, Lorenzo Antonio ; Oliveira Junior, Osvaldo Novais de ; Carmo, João Paulo Pereira do
Fonte: IEEE Sensors Journal. Unidades: IFSC, EESC
Assuntos: IMPEDÂNCIA ELÉTRICA, SENSORES BIOMÉDICOS, MATERIAIS NANOESTRUTURADOS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BUSCAGLIA, Lorenzo Antonio e OLIVEIRA JUNIOR, Osvaldo Novais de e CARMO, João Paulo Pereira do. Roadmap for electrical impedance spectroscopy for sensing: a tutorial. IEEE Sensors Journal, v. 21, n. 20, p. 22246-22257, 2021Tradução . . Disponível em: https://doi.org/10.1109/JSEN.2021.3085237. Acesso em: 10 out. 2024.
APA
Buscaglia, L. A., Oliveira Junior, O. N. de, & Carmo, J. P. P. do. (2021). Roadmap for electrical impedance spectroscopy for sensing: a tutorial. IEEE Sensors Journal, 21( 20), 22246-22257. doi:10.1109/JSEN.2021.3085237
NLM
Buscaglia LA, Oliveira Junior ON de, Carmo JPP do. Roadmap for electrical impedance spectroscopy for sensing: a tutorial [Internet]. IEEE Sensors Journal. 2021 ; 21( 20): 22246-22257.[citado 2024 out. 10 ] Available from: https://doi.org/10.1109/JSEN.2021.3085237
Vancouver
Buscaglia LA, Oliveira Junior ON de, Carmo JPP do. Roadmap for electrical impedance spectroscopy for sensing: a tutorial [Internet]. IEEE Sensors Journal. 2021 ; 21( 20): 22246-22257.[citado 2024 out. 10 ] Available from: https://doi.org/10.1109/JSEN.2021.3085237
Artigo de periodico
Identification of gait events in healthy and parkinson’s disease subjects using inertial sensors: a supervised learning approach (2020)
Pérez Ibarra, Juan Carlos ; Siqueira, Adriano Almeida Gonçalves ; Krebs, Hermano Igo
Fonte: IEEE Sensors Journal. Unidade: EESC
Assuntos: HEURÍSTICA, SENSOR, APRENDIZADO COMPUTACIONAL, MARCHA, ENGENHARIA MECÂNICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PÉREZ IBARRA, Juan Carlos e SIQUEIRA, Adriano Almeida Gonçalves e KREBS, Hermano Igo. Identification of gait events in healthy and parkinson’s disease subjects using inertial sensors: a supervised learning approach. IEEE Sensors Journal, v. 20, n. 24, p. 14984-14993, 2020Tradução . . Disponível em: https://doi.org/10.1109/JSEN.2020.3011627. Acesso em: 10 out. 2024.
APA
Pérez Ibarra, J. C., Siqueira, A. A. G., & Krebs, H. I. (2020). Identification of gait events in healthy and parkinson’s disease subjects using inertial sensors: a supervised learning approach. IEEE Sensors Journal, 20( 24), 14984-14993. doi:10.1109/JSEN.2020.3011627
NLM
Pérez Ibarra JC, Siqueira AAG, Krebs HI. Identification of gait events in healthy and parkinson’s disease subjects using inertial sensors: a supervised learning approach [Internet]. IEEE Sensors Journal. 2020 ; 20( 24): 14984-14993.[citado 2024 out. 10 ] Available from: https://doi.org/10.1109/JSEN.2020.3011627
Vancouver
Pérez Ibarra JC, Siqueira AAG, Krebs HI. Identification of gait events in healthy and parkinson’s disease subjects using inertial sensors: a supervised learning approach [Internet]. IEEE Sensors Journal. 2020 ; 20( 24): 14984-14993.[citado 2024 out. 10 ] Available from: https://doi.org/10.1109/JSEN.2020.3011627

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