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Fernando Auat Cheein
ResearcherPublications, citations & collaboration network
Fernando Auat Cheein is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 246 works, 4,868 citations, an h-index of 34 and an i10-index of 109.
246
Works
4,868
Citations
34
h-index
109
i10-index
How has Fernando Auat Cheein's publication output changed over time?
ScholarIQpublication output · 2008–2024
Output grew0% over the shown period — from 1 works in 2008 to 1 in 2024.
1
1
1
1
1
2
3
1
20082010201120132017201920202024
What are the most-cited papers on Fernando Auat Cheein?
ScholarIQmost cited works
Human–robot interaction in agriculture: A survey and current challenges
Juan Pablo Vásconez, George Kantor, Fernando Auat Cheein
S65230043. 2019308 Citations
Agricultural Robotics: Unmanned Robotic Service Units in Agricultural Tasks
Fernando Auat Cheein, Ricardo Carelli
S32918732. 2013230 CitationsOPEN ACCESS
The Role of 5G Technologies: Challenges in Smart Cities and Intelligent Transportation Systems
Leonardo Guevara, Fernando Auat Cheein
Sustainability. 2020211 CitationsOPEN ACCESS
Comparison of convolutional neural networks in fruit detection and counting: A comprehensive evaluation
Juan Pablo Vásconez, José Delpiano, Stavros Vougioukas, Fernando Auat Cheein
Computers and Electronics in Agriculture. 2020193 Citations
Human-machine interfaces based on EMG and EEG applied to robotic systems
André Ferreira, Wanderley Cardoso Celeste, Fernando Auat Cheein, Teodiano Bastos-Filho, Mário Sarcinelli-Filho, Ricardo Carelli
Journal of NeuroEngineering and Rehabilitation. 2008152 CitationsOPEN ACCESS
Related on ScholarIQ
Heriot-Watt University
Institution
Human–robot interaction in agriculture: A survey and current challenges
Paper
Agricultural Robotics: Unmanned Robotic Service Units in Agricultural Tasks
Paper
The Role of 5G Technologies: Challenges in Smart Cities and Intelligent Transportation Systems
Paper
Comparison of convolutional neural networks in fruit detection and counting: A comprehensive evaluation
Paper
Human-machine interfaces based on EMG and EEG applied to robotic systems
Paper