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Sobre

Sobre

António Paulo Gomes Mendes Moreira é licenciado em Engenharia Eletrotécnica e de Computadores - FEUP (1986), opção Instrumentação Eletrónica, Mestre em Engenharia Eletrotécnica e de Computadores - Especialização em Sistemas pela FEUP (1991), Doutor em Engenharia Eletrotécnica e de Computadores (1998) e Agregado - FEUP (2017). Atualmente é Professor Catedrático no Departamento de Engenharia Eletrotécnica e de Computadores da Faculdade de Engenharia da Universidade do Porto. É também Investigador e Coordenador do CRIIS - Centro de Robótica Industrial e Sistemas Inteligentes e Diretor do iiLab - Laboratório de Indústria e Inovação do INESC TEC. Desenvolve investigação essencialmente em Robótica, Automação e Controlo, com ênfase na sua aplicação em projectos industriais e transferência de tecnologia. Participou ou participa ainda em 25 projetos científicos, sendo coordenador ou investigador responsável por 7 deles. O trabalho realizado nestes projectos gerou 40 projectos com empresas ou contratos de desenvolvimento e transferência de tecnologia, sendo o investigador principal em 18 destes projectos. Participou também no desenvolvimento de 18 protótipos e 2 patentes, das quais é coproprietário. Contribuiu para a criação de duas empresas spin-off. Mais pormenores em: https://www.cienciavitae.pt/portal/EB15-85A7-4A0D

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    António Paulo Moreira
  • Cargo

    Coordenador de Centro
  • Desde

    01 junho 2009
039
Publicações

2023

Special Issue on Advances in Industrial Robotics and Intelligent Systems

Autores
Moreira, AP; Neto, P; Vidal, F;

Publicação
APPLIED SCIENCES-BASEL

Abstract
Robotics and intelligent systems are key technologies to promote efficient and innovative applications in the most diverse domains (industry, healthcare, agriculture, construction, mobility, etc [...]

2023

Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models

Autores
Magalhaes, SC; Castro, L; Rodrigues, L; Padilha, TC; de Carvalho, F; dos Santos, FN; Pinho, T; Moreira, G; Cunha, J; Cunha, M; Silva, P; Moreira, AP;

Publicação
IEEE SENSORS JOURNAL

Abstract
Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialized labor is not available for proper classification or identification of grapevines, making the value of commercial vines uncertain. Traditional methods, such as genetic analysis or ampelometry, are time-consuming, expensive, and often require expert skills that are even rarer. New vision-based systems benefit from advanced and innovative technology and can be used by nonexperts in ampelometry. To this end, deep learning (DL) and machine learning (ML) approaches have been successfully applied for classification purposes. This work extends the state of the art by applying digital ampelometry techniques to larger grapevine varieties. We benchmarked MobileNet v2, ResNet-34, and VGG-11-BN DL classifiers to assess their ability for digital ampelography. In our experiment, all the models could identify the vines' varieties through the leaf with a weighted F1 score higher than 92%.

2023

Benchmarking edge computing devices for grape bunches and trunks detection using accelerated object detection single shot multibox deep learning models

Autores
Magalhaes, SC; dos Santos, FN; Machado, P; Moreira, AP; Dias, J;

Publicação
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Abstract
Purpose: Visual perception enables robots to perceive the environment. Visual data is processed using computer vision algorithms that are usually time-expensive and require powerful devices to process the visual data in real-time, which is unfeasible for open-field robots with limited energy. This work benchmarks the performance of different heterogeneous platforms for object detection in real-time. This research benchmarks three architectures: embedded GPU-Graphical Processing Units (such as NVIDIA Jetson Nano 2 GB and 4 GB, and NVIDIA Jetson TX2), TPU-Tensor Processing Unit (such as Coral Dev Board TPU), and DPU-Deep Learning Processor Unit (such as in AMD-Xilinx ZCU104 Development Board, and AMD-Xilinx Kria KV260 Starter Kit). Methods: The authors used the RetinaNet ResNet-50 fine-tuned using the natural VineSet dataset. After the trained model was converted and compiled for target-specific hardware formats to improve the execution efficiency.Conclusions and Results: The platforms were assessed in terms of performance of the evaluation metrics and efficiency (time of inference). Graphical Processing Units (GPUs) were the slowest devices, running at 3 FPS to 5 FPS, and Field Programmable Gate Arrays (FPGAs) were the fastest devices, running at 14 FPS to 25 FPS. The efficiency of the Tensor Processing Unit (TPU) is irrelevant and similar to NVIDIA Jetson TX2. TPU and GPU are the most power-efficient, consuming about 5 W. The performance differences, in the evaluation metrics, across devices are irrelevant and have an F1 of about 70 % and mean Average Precision (mAP) of about 60 %.

2023

2D LiDAR-Based System for Canopy Sensing in Smart Spraying Applications

Autores
Baltazar, AR; Dos Santos, FN; De Sousa, ML; Moreira, AP; Cunha, JB;

Publicação
IEEE ACCESS

Abstract
The efficient application of phytochemical products in agriculture is a complex issue that demands optimised sprayers and variable rate technologies, which rely on advanced sensing systems to address challenges such as overdosage and product losses. This work developed a system capable of processing different tree canopy parameters to support precision fruit farming and environmental protection using intelligent spraying methodologies. This system is based on a 2D light detection and ranging (LiDAR) sensor and a Global Navigation Satellite System (GNSS) receiver integrated into a sprayer driven by a tractor. The algorithm detects the canopy boundaries, allowing spray only in the presence of vegetation. The spray volume spared evaluates the system's performance compared to a Tree Row Volume (TRV) methodology. The results showed a 28% reduction in the overdosage of spraying product. The second step in this work was calculating and adjusting the amount of liquid to apply based on the tree volume. Considering this parameter, the saving obtained had an average value for the right and left rows of 78%. The volume of the trees was also monitored in a georeferenced manner with the creation of a occupation grid map. This map recorded the trajectory of the sprayer and the detected trees according to their volume.

2023

Special Issue on Advances in Industrial Robotics and Intelligent Systems

Autores
Moreira, AP; Neto, P; Vidal, F;

Publicação
Robotics

Abstract
Robotics and intelligent systems are intricately connected, each exploring their respective capabilities and moving towards a common goal [...]

Teses
supervisionadas

2022

Mobile Target Detection and Tracking using Multiple Cooperative Aerial Robots

Autor
Fábio André Costa Azevedo

Instituição
UP-FEUP

2022

Analysis and Optimisation of Computational Delays in Reinforcement Learning-based Wi-Fi Rate Adaptation

Autor
Ricardo Jorge Espirito Santo Trancoso

Instituição
UP-FEUP

2022

Development of strategies for energy storage in distribution grid with RES

Autor
Piedy Del Mar Agamez Arias

Instituição
UP-FEUP

2022

Harvesting with active perception for open-field agricultural robotics

Autor
Sandro Augusto Costa Magalhães

Instituição
UP-FEUP

2022

Machine Learning and Movement Analysis for Cognitive Screening in Older Adults

Autor
Vânia Margarida Cardoso Guimarães

Instituição
UP-FEUP