Abstract
The dropout of university students has been a factor of concern for educational institutions, affecting various aspects such as the institution’s reputation and funding and rankings. For this reason, it is essential to identify which students are at risk. In this study, algorithms based on decision trees and random forests are proposed to solve these problems using real data from 331 students from the University of Trásos-Montes and Alto Douro. In this work with these learning algorithms together with the training strategies, we managed to obtain an 89% forecast of students who may abandon their studies based on the evaluations of both semesters related to the first year and personal data. © 2021, Springer Nature Switzerland AG.

Abstract
Many undergraduate engineering students have just a single introductory feedback control course in their study list. Often the curricula found in such courses is totally based on continuous time-domain classic control techniques. However, currently most control design techniques are implemented in digital machines. Thus, digital control concepts should be covered in introductory control courses. In this paper, the issue of how to implement and test digital industrial controllers is addressed. Teaching experiments based on the APMonitor temperature control lab (TCLab) are proposed. It will be shown that TCLab as an Arduino based portable kit, provides an excellent means to test digital controllers, as it is a compact and portable lab to be used by lecturers and students. While there are many low-cost and portable hardware options for teaching dynamics and control, a novel aspect of this paper is the digital control education methods that are validated with classroom experience, particularly with Biomedical and Bioengineering students. Preliminary results are presented. Copyright (C) 2020 The Authors.

Abstract
O estudo analisou diversos documentos do curso de mestrado-integrado em Engenharia Elétrica e de Computadores de uma Universidade Pública Portuguesa. Avaliou-se quais são os drivers institucionais potencialmente responsáveis pela adoção de novas metodologias no curso. Realizou-se um estudo de caso com abordagem qualitativa baseada em métodos mistos: análise estatística aplicada a corpus textuais e complementada por análise de conteúdo. Como resultados emergiram duas classes de análise de conteúdo: competências e conhecimentos esperados dos estudantes; e aspectos da formação em Engenharia Elétrica e do ciclo de estudos. Identificaram-se sete drivers da adoção de novas metodologias no curso, com base nas teorias da difusão da inovação e teoria institucional: formação, desenvolvimento, competência, ciclo de estudos, novo, tecnologia e UC. Cada um destes drivers possui os seus próprios outcomes (resultados), 16 no total, que apresentam os efeitos percebidos pelos docentes, avaliadores de curso e coordenadores.

Abstract
The objective of this work is study, implementation and evaluation of compression techniques used in bioelectrical signals, applied to electroencephalography. For that, the fundamental concepts of Fast Walsh Hadamard Transform (FWHT), the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT), in essence, the mathematical models were studied. In these systems, the applicability and principles of operation were considered the Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR), Mean Absolute Error (MAE) and mean squared error. Later, it is proposed the implementation of the compression algorithms. For the implementation of the techniques, computational tools of tests were developed, and for the purposes of validation and comparison of the results were used, with the appropriate adaptations, and described in the work, being these among the most recognised in terms of evaluation of signal quality. Finally, we present the results and the conclusions, where we sought a compromise of the implementations between the estimated percentage of DCT and the level of degradation of the signal provided by the compression application. In this sense, it was verified that they presented satisfactory results.

Abstract
This paper presents a method capable of detecting and segmenting pulmonary nodules in clinical computed tomography images, using UNet convolutional neural network powered by The Lung Image Database Consortium image collection - LIDC-IDRI, that in the training process was submitted to different training tests, where for each of them, their hyper-parameters were modified so that the results could be collected from different media, getting quite satisfactory results in the segmentation task, highlighting the areas of interest almost perfectly, resulting in 91.61% on the IoU (Intersection over Union) metric.

Abstract
This paper presents the construction of an autonomous robot to participating in the autonomous driving competition of the National Festival of Robotics in Portugal, which relies on an open platform requiring basic knowledge of robotics, like mechanics, control, computer vision and energy management. The projet is an excellent way for teaching robotics concepts to engineering students, once the platform endows students with an intuitive learning for current technologies, development and testing of new algorithms in the area of mobile robotics and also in generating good team-building.