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About

About

Paulo Moura Oliveira received the Electrical Engineering degree in 1991, from the UTAD University, Portugal, MSc in Industrial Control Systems in 1994 and PhD in Control Engineering in 1998, both from Salford University, Manchester, UK. He is a Tenured Associated Professor at the Engineering Department of UTAD University and a researcher at the INESC TEC institute. Currently, he is the director of the PhD Course in Electrical and Computers Engineering in UTAD. His research interests are focused on the fields of control engineering, intelligent control, PID control, control engineering education, evolutionary and natural inspired metaheuristics for single and multiple objective optimization problem solving. He is author in more than 100 peer-reviewed scientific publications.

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Details

Details

Publications

2022

Forecasting Student s Dropout: A UTAD University Study

Authors
Da Silva, DEM; Pires, EJS; Reis, A; Oliveira, PBD; Barroso, J;

Publication
FUTURE INTERNET

Abstract
In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades. Four different machine learning techniques are presented and analyzed. The dataset consists of 331 students who were previously enrolled in the Computer Engineering degree at the Universidade de Tras-os-Montes e Alto Douro (UTAD). The study aims to detect students who may prematurely drop out using existing methods. The most relevant data features were identified using the Permutation Feature Importance technique. In the second phase, several methods to predict the dropouts were applied. Then, each machine learning technique's results were displayed and compared to select the best approach to predict academic dropout. The methods used achieved good results, reaching an Fl-Score of 81% in the final test set, concluding that students' marks somehow incorporate their living conditions.

2022

Pocket-Sized Portable Labs: Control Engineering Practice Made Easy in Covid-19 Pandemic Times

Authors
Oliveira, PBD; Soares, F; Cardoso, A;

Publication
IFAC PAPERSONLINE

Abstract

2022

Teaching Control during the COVID-19 Pandemic

Authors
Guzman, JL; Zakova, K; Craig, IK; Hagglund, T; Rivera, DE; Normey-Rico, JE; Moura-Oliveira, P; Wang, L; Serbezov, A; Sato, T; Beschi, M;

Publication
IFAC PAPERSONLINE

Abstract

2022

Edge AI-Based Tree Trunk Detection for Forestry Monitoring Robotics

Authors
da Silva, DQ; dos Santos, FN; Filipe, V; Sousa, AJ; Oliveira, PM;

Publication
ROBOTICS

Abstract
Object identification, such as tree trunk detection, is fundamental for forest robotics. Intelligent vision systems are of paramount importance in order to improve robotic perception, thus enhancing the autonomy of forest robots. To that purpose, this paper presents three contributions: an open dataset of 5325 annotated forest images; a tree trunk detection Edge AI benchmark between 13 deep learning models evaluated on four edge-devices (CPU, TPU, GPU and VPU); and a tree trunk mapping experiment using an OAK-D as a sensing device. The results showed that YOLOR was the most reliable trunk detector, achieving a maximum F1 score around 90% while maintaining high scores for different confidence levels; in terms of inference time, YOLOv4 Tiny was the fastest model, attaining 1.93 ms on the GPU. YOLOv7 Tiny presented the best trade-off between detection accuracy and speed, with average inference times under 4 ms on the GPU considering different input resolutions and at the same time achieving an F1 score similar to YOLOR. This work will enable the development of advanced artificial vision systems for robotics in forestry monitoring operations.

2022

Control Engineering and Industrial Automation Education using Out of the Box Approaches

Authors
Oliveira, PM; Vrancic, D; Huba, M;

Publication
20th Anniversary of IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2022 - Proceedings

Abstract

Supervised
thesis

2021

An Explainable Approach for Lung Cancer Classification and Integrative Survival Analysis using Omics Data

Author
Bernardo Manuel Faria Ramos

Institution
UP-FEUP

2019

Chat Bot -o diagmóstico de bolso

Author
Duarte Rui Afonso Gomes Tavares do Amaral

Institution
UTAD

2018

Remuneration and Tariffs in the Context of Virtual Power Players

Author
Ana Catarina Ribeiro

Institution
UTAD

2018

Sistemas Baseados em casos: Aplicação à Saúde

Author
Stéfanie Maria da Costa Alves

Institution
UTAD

2018

Análise da variabilidade da frequência cardíaca em indivíduos saudáveis e doentes

Author
Cristina Monteiro Pinto

Institution
UTAD