Abstract
In this paper it is described a new Servo Motor Optimised for Educational Robotic Applications (SMORA), which comprises several sensors and higher level addressing/communication capabilities. Even though servos have excellent qualities when used in the field of robotics, several properties can still be enhanced. These devices are known to have nonlinear properties and dynamic factors such as dead-zones, backlash and friction that might hinder their precision and accuracy. They usually do not provide position, velocity or current feedback to the device controlling them. Factors such as change in the load attached to the system increase the complexity of the analysis even further. Controlling these servos using a PWM signal might also become complicated if the number of servo motors to control increases. The new proposed servo consists of custom hardware and firmware that includes a microcontroller and a series of sensors, allowing for the motor current, temperature and voltage to be measured in real-time as well as precise position feedback thanks to the integrated hall-effect magnetic position encoder. It also incorporates an accelerometer and a gyroscope to measure the servo body relative position and rotation.

Abstract

Abstract
Due to amount of today's electricity consumption, one of the most important tasks of the energy operators is to be able to predict the consumption and be ready to control the energy generation based on the estimated consumption for the future. In this way, having a trustable forecast of the electricity consumption is essential to control the consumption and maintain the balance in energy distribution networks. This study presents a day ahead forecasting approach based on a genetic fuzzy system for fuzzy rule learning based on the MOGUL methodology (GFS.FR.MOGUL). The proposed approach is used to forecast the electricity consumption of an office building in the following 24 hours. The goal of this work is to present a more reliable profile of the electricity consumption comparing to previous works. Therefore, this paper also includes the comparison of the results of day ahead forecasting using GFS.FR.MOGUL method against other fuzzy rule based methods, as well as a set of Artificial Neural Network (ANN) approaches. This comparison shows that using the GFS.FR.MOGUL forecasting method for day-ahead electricity consumption forecasting is able to estimate a more trustable value than the other approaches.

Abstract
Atmospheric turbulence and precise measurement of the astrometric baseline vector between any two telescopes are two major challenges in implementing phase-referenced interferometric astrometry and imaging. They limit the performance of a fibre-fed interferometer by degrading the instrument sensitivity and the precision of astrometric measurements and by introducing image reconstruction errors due to inaccurate phases. A multiple-beam acquisition and guiding camera was built to meet these challenges for a recently commissioned four-beam combiner instrument, GRAVITY, at the European Southern Observatory Very Large Telescope Interferometer. For each telescope beam, it measures (a) field tip-tilts by imaging stars in the sky, (b) telescope pupil shifts by imaging pupil reference laser beacons installed on each telescope using a 2x2 lenslet and (c) higher-order aberrations using a 9x9 Shack-Hartmann. The telescope pupils are imaged to provide visual monitoring while observing. These measurements enable active field and pupil guiding by actuating a train of tip-tilt mirrors placed in the pupil and field planes, respectively. The Shack-Hartmann measured quasi-static aberrations are used to focus the auxiliary telescopes and allow the possibility of correcting the non-common path errors between the adaptive optics systems of the unit telescopes and GRAVITY. The guiding stabilizes the light injection into single-mode fibres, increasing sensitivity and reducing the astrometric and image reconstruction errors. The beam guiding enables us to achieve an astrometric error of less than 50 mu as. Here, we report on the data reduction methods and laboratory tests of the multiple-beam acquisition and guiding camera and its performance on-sky.

Abstract
The purpose of this work is to explore the design principles for a Real-Time Robotic Multi Camera Vision System, in a case study involving a real world competition of autonomous driving. Design practices from vision and real-time research areas are applied into a Real-Time Robotic Vision application, thus exemplifying good algorithm design practices, the advantages of employing the "zero copy one pass" methodology and associated trade-offs leading to the selection of a controller platform. The vision tasks under study are: (i) recognition of a "flat" signal; and (ii) track following, requiring 3D reconstruction. This research firstly improves the used algorithms for the mentioned tasks and finally selects the controller hardware. Optimization for the shown algorithms yielded from 1.5 times to 190 times improvements, always with acceptable quality for the target application, with algorithm optimization being more important on lower computing power platforms. Results also include a 3-cm and five-degree accuracy for lane tracking and 100% accuracy for signalling panel recognition, which are better than most results found in the literature for this application. Clear results comparing different PC platforms for the mentioned Robotic Vision tasks are also shown, demonstrating trade-offs between accuracy and computing power, leading to the proper choice of control platform. The presented design principles are portable to other applications, where Real-Time constraints exist.

Abstract
This Special Issue on Electrical and Computer Engineering includes selected papers from the 1st edition of the Symposium on Electrical and Computer Engineering (ECE 2015), one of the symposia included in the 1st Doctoral Congress in Engineering, held at FEUP, 11-12 June, 2015. ECE 2015 was an important forum for presenting the research activities of ECE students, particularly from the Doctoral Program in Electrical and Computer Engineering, at FEUP (PDEEC). ECE 2015 received a total of 42 two-page abstracts. The review process was carried out by members of the Symposium Scientific Committee and other reviewers. Each abstract was reviewed by at least two reviewers, and checked by the Program Committee. 37 abstracts were finally accepted and appear in the Symposium book of abstracts. From the 37 abstracts, 17 were presented in four oral sessions, and 20 in one poster session. We were very pleased to include two keynote talks: “The Internet of Things - Latest Trends and Future Perspectives” by Carlos Azeredo Leme, University of Lisbon, Portugal; “A Perspective on Virtual Radio Access Networks” by Luís M. Correia, University of Lisbon, Portugal. Six papers were invited to submit extended versions to this special issue, that were further reviewed and published in this issue.We would like to sincerely thank the authors for submitting these extended versions, and we thank the special issue reviewers for the careful evaluation and feedback provided to the authors. We also would like to express our gratitude to Luís Miguel Costa, for supporting the organization of this Special issue of the U.Porto Journal of Engineering.Finally, we are very pleased to give the readership of this Special Issue on ECE examples of the research developed by PDEEC students, from a vast area covered by the Electrical and Computer Engineering at FEUP and at the associated research institutes and research centers.