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1
Towards providing effective data-driven responses to predict the Covid-19 in São Paulo and Brazil
Towards providing effective data-driven responses to predict the Covid-19 in São Paulo and Brazil
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Towards providing effective data-driven responses to predict the Covid-19 in São Paulo and Brazil

Fabio Vinicius Góes Amaral Wallace Correa de Oliveira Casaca; Cassio Machiaveli Oishi; José Alberto Cuminato

Sensors Basel : MDPI AG v. 21, n. 2, p. 1-25, 2021

Basel 2021

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3018972 )(Acessar)

2
Counting Cattle in UAV Images Dealing with Clustered Animals and Animal/Background Contrast Changes
Counting Cattle in UAV Images Dealing with Clustered Animals and Animal/Background Contrast Changes
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Counting Cattle in UAV Images Dealing with Clustered Animals and Animal/Background Contrast Changes

Jayme Garcia Arnal Barbedo Luciano Vieira Koenigkan; Patrícia Menezes Santos; Andrea Roberto Bueno Ribeiro

Sensors Basel v. 20, art. 2126, p. 1-14, 2020

Basel 2020

Item não circula. Consulte sua biblioteca.(Acessar)

3
Handlebar robotic system for bimanual motor control and learning research
Handlebar robotic system for bimanual motor control and learning research
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Handlebar robotic system for bimanual motor control and learning research

Lucas Ribeiro Lopes Cardoso Leonardo Marquez Pedro; Arturo Forner Cordero 1969-

Sensors Basel v. 21, n. 18, p. 1-20, 2021

Basel 2021

Item não circula. Consulte sua biblioteca.(Acessar)

4
Machine learning for the detection and diagnosis of anomalies in applications driven by electric motors
Machine learning for the detection and diagnosis of anomalies in applications driven by electric motors
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Machine learning for the detection and diagnosis of anomalies in applications driven by electric motors

Fábio Ferraz Júnior Roseli Aparecida Francelin Romero; Sheng-Jen Hsieh

Sensors Basel v. 23, n. 24, p. 1-25, 2023

Basel 2023

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3172974 )(Acessar)

5
Towards interpretable deep learning a feature selection framework for prognostics and health management using deep neural networks
Towards interpretable deep learning a feature selection framework for prognostics and health management using deep neural networks
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Towards interpretable deep learning a feature selection framework for prognostics and health management using deep neural networks

Joaquín Eduardo Figueroa Barraza Enrique López Droguett; Marcelo Ramos Martins 1971-

Sensors Basel v. 21, n. 17, p. 1-30, 2021

Basel 2021

Item não circula. Consulte sua biblioteca.(Acessar)

6
How to improve fault tolerance in disaster predictions a case study about flash floods using IoT, ML and real data
How to improve fault tolerance in disaster predictions a case study about flash floods using IoT, ML and real data
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How to improve fault tolerance in disaster predictions a case study about flash floods using IoT, ML and real data

Gustavo Furquim (Gustavo Antonio Furquim) Geraldo P. R Filho (Geraldo Pereira Rocha Filho); Roozbeh Jalali; Gustavo Pessin; Richard W Pazzi (Richard Werner Nelem Pazzi); Jó Ueyama

Sensors Basel, Switzerland : MDPI AG v.18, n. 3, artigo 907, p. 1-20, Mar. 2018

Basel, Switzerland 2018

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD 2883084 )(Acessar)

7
IoT off-grid, data collection from a machine learning classification using UAV
IoT off-grid, data collection from a machine learning classification using UAV
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IoT off-grid, data collection from a machine learning classification using UAV

Ademir Goulart Alex Sandro Roschildt Pinto; Adão Boava; Kalinka Regina Lucas Jaquie Castelo Branco

Sensors Basel : MDPI v. 22, p. 1-16, 2022

Basel 2022

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3109238 )(Acessar)

8
A TDD framework for automated monitoring in internet of things with machine learning
A TDD framework for automated monitoring in internet of things with machine learning
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A TDD framework for automated monitoring in internet of things with machine learning

Victor Takashi Hayashi Wilson Vicente Ruggiero; Júlio Cezar Estrella; Artino Quintino Filho; Matheus Ancelmo Bonfim Pita; Reginaldo Arakaki; Cairo Mateus Neves Ribeiro; Bruno Manias Trazzi; Romeo Bulla Junior

Sensors Basel : MDPI v. 22, n. 23, p. 1-25, 2022

Basel 2022

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3113943 )(Acessar)

9
A method for chlorophyll-a and suspended solids prediction through remote sensing and machine learning †
A method for chlorophyll-a and suspended solids prediction through remote sensing and machine learning †
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A method for chlorophyll-a and suspended solids prediction through remote sensing and machine learning †

Lucas Silveira Kupssinskü Tainá Thomassim Guimarães; Eniuce Menezes de Souza; Daniel C Zanotta; Maurício Roberto Veronez; Luiz Gonzaga Junior; Frederico Fábio Mauad

Sensors Basel : M D P I AG, 2020 v. 20, n. 7, 2125, Apr. 2020.

Basel 2020

Localização: EESC - Esc. Engenharia de São Carlos    (PROD-022315 )(Acessar)

10
Machine Learning for Seed Quality Classification An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging
Machine Learning for Seed Quality Classification An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging
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Machine Learning for Seed Quality Classification An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging

André Dantas de Medeiros Laércio Junio da Silva; João Paulo Oliveira Ribeiro; Kamylla Calzolari Ferreira; Jorge Tadeu Fim Rosas; Abraão Almeida Santos; Clíssia Barboza da Silva

Sensors v. 20, n. 15, p. 1-13, 2020

Basel 2020

Item não circula. Consulte sua biblioteca.(Acessar)

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Deste Autor:

  1. Romero, R
  2. Branco, K
  3. Silva, L
  4. Estrella, J
  5. Boava, A

Neste Assunto:

  1. Aprendizado Computacional
  2. Aeronaves Não Tripuladas
  3. Internet Das Coisas
  4. Redes Neurais
  5. Reconhecimento De Imagem

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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