Abstract
This paper addresses the problem of finding the best Brain Emotional Learning (BEL) controller parameters in order to improve the response of a single degree-of-freedom (SDOF) structural system under an earthquake excitation. The control paradigm considered is based on a semi-active system to control the dynamics of a lumped mass-damper-spring model, being carried out by changing the damping force of a magneto-rheological (MR) damper. A typical BEL based controller requires the definition of several parameters which can be proved difficult and non-intuitive to obtain. For this reason, an evolutionary based search technique has been added to the current problem framework in order to automate the controller design. In particular, the particle swarm optimization (PSO) method was chosen as the evolutionary based technique to be integrated within the current control paradigm. The obtained results suggest that, indeed, it is possible to parametrize a BEL controller using an evolutionary based algorithm. Moreover, simulation shows that the obtained results can outperform the ones obtained by manual tuning each controller parameter individually.

Abstract
In this paper it is described a hardware experimental setup applied to study the response of structures due to an earthquake excitation. The described hardware experimental setup was applied in the performance and effectiveness evaluation of passive and semi-active vibration control strategies, in order to reduce the structural response due to an earthquake excitation. This study is also meant to validate the accurateness of the developed numerical models that simulate the experimental results. The hardware implementation is described, being the structural response real data and its numerical models compared.

Abstract
Improving the safety of public roads and industrial factories requires more reliable and robust computer vision-based approaches for monitoring the eye state (open or closed) of human operators. Getting this information in real time when humans are driving cars or using hazardous machinery will help to prevent accidents and deaths. This paper proposes a new framework called EyeLSD to localize the eyes and detect their states without face detection step. For EyeLSD aims, two novel descriptors are proposed: enhanced Pyramidal Local Binary Pattern Histogram (ePLBPH) and Multi-Three-Patch LBP histogram (Multi-TPLBP). The performance of EyeLSD with ePLBPH and Multi-TPLBP is evaluated and compared against other approaches. For this evaluation three independent and public datasets were used: BioID, CAS-PEAL-R1 and ZJU datasets. The set EyeLSD, ePLBPH and Multi-TPLBP have a greater performance when compared against the state-of-the-art algorithms. The proposed approach is very stable under large range of eye appearances caused by expression, rotation, lighting, head pose, and occlusion.

Abstract
The intensive use of agricultural machinery is promoting the soil compaction. The use of agricultural robots or autonomous machinery can intensify this problem, due its capacity to replicate the same trajectories. One of the possible strategies to minimize the effects of soil compaction is to control agricultural traffic instead of common random traffic. Since geo-referencing systems are present in almost all field operations it is possible to optimize trajectories to avoid to damage the crop and intensify the soil compaction. The controlled agricultural traffic on farms will not only increase production capacity, the incomes as well as the quality of the soil. In this work a novel approach based on A-star algorithm is proposed to reduce soil compaction in steep slope vineyards. © 2018 IEEE.

Abstract
The BSc in Systems Engineering, taught at the School of Engineering of the Porto Polytechnic (ISEP–PPorto), includes the development of team projects at the end of each semester. These projects are intended to be multidisciplinary, to allow students getting a systems perspective of the problems, and as a way to integrate knowledge from different scientific areas. To integrate the knowledge in electronics, microcontrollers and programming, common in several distinct systems nowadays, there is a course named Systems Labs I (LSIS1). This paper discusses the experience of teaching this course using a robotics based approach. The analysis is based on a survey conducted to the students enrolled in the course in the last three academic years, allowing to conclude that the overall students experience with the attendance of LSIS1 course is good. © 2018 by World Scientific Publishing Co. Pte. Ltd.

Abstract
This paper reports the collaborative design and development of Helios, a wearable UltraViolet (UV) meter. Helios is intended to help preventing the negative effects of over-exposure to UV radiation, e.g., sun burning, photo ageing, eye damage and skin cancer, as well as of under-exposure to solar radiation, e.g., the risk of developing vitamin D shortage. This project-based learning experience involved five Erasmus students who participated in EPS@ISEP - the European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP) - in the spring of 2017. The Team, motivated by the desire to find a solution to this problem, conducted multiple studies, including scientific, technical, sustainability, marketing, ethics and deontology analyses, and discussions to derive the requirements, design structure, functional system and list of materials and components. The result is Helios, a prototype Wearable UV Meter that can be worn as both a bracelet and a clip-on. The tangible result was the Helios prototype, but more importantly was the learning experience of the Team, as concluded from their closing statements.