Abstract
The paper addresses new perspective of the PSO including a fractional block. The local gain is replaced by one of fractional order considering several previous positions of the PSO particles. The algorithm is evaluated for several well known test functions and the relationship between the fractional order and the convergence of the algorithm is observed. The fractional order influences directly the algorithm convergencerate demonstrating a large potential for developments. © Springer Science+Business Media Dordrecht 2014.

Abstract
This paper presents the first phase of the redevelopment of the Electric Vehicle Scenario Simulator (EVeSSi) tool. A new methodology to generate traffic demand scenarios for the Simulation of Urban MObility (SUMO) tool for urban traffic simulation is described. This methodology is based on a Portugal census database to generate a synthetic population for a given area under study. A realistic case study of a Portuguese city, Vila Real, is assessed. For this area the road network was created along with a synthetic population and public transport. The traffic results were obtained and an electric buses fleet was evaluated assuming that the actual fleet would be replaced in a near future. The energy requirements to charge the electric fleet overnight were estimated in order to evaluate the impacts that it would cause in the local electricity network.

Abstract
Electrical power quality (PQ) is a crucial competitive and developing factor to all economic activities. The economic impact resulting from a bad PQ would be drastic on all consumers. Computers, uninterruptible and switched power supplies (UPS), and fluorescent lamps/tubes are examples of nonlinear loads that have the consumption of a nonsinusoidal current, which cause disturbances in the power supply system (that may be severe or not). This study discusses residential generic power circuitry analysis and simulation, under nonlinear loads, in connection with undergraduate electrical engineering education. It briefly reviews some of the basic techniques, and presents a software tool that has been found to be very useful in the context. The tool has an easy-to-use, friendly interface, and can be used to teach design techniques or as a laboratory support to study the applicability of known methods to real situations. The students can perform simulations with their own data on Microsoft (TM) Windows (R)-based platforms. (c) 2011 Wiley Periodicals, Inc. Comput Appl Eng Educ 22:340-348, 2014; View this article online at ; DOI

Abstract
This paper introduces a fast Traffic Sign Recognition system developed for a robot, participant in the Autonomous Driving Competition in the Portuguese Festival of Robotics. The Autonomous Driving Robot performs detection and classification of traffic signs and traffic lights based on the analysis of images acquired by a camera mounted on its chassis. The proposed algorithm is composed of three processing stages: detection, pictogram extraction and classification. After the two firsts processing stages, a binary pattern matrix is obtained by color segmentation. In the classification stage two different neural networks were trained to recognize the traffic signs or the traffic light sign. Experimental results show that the system precision is very close to 100% whereas recall presents values above 90% in most of the signs. The proposed system also proves to be reliable and suitable for real-time processing.

Abstract
The point prediction quality is closely related to the model that explains the dynamic of the observed process. Sometimes the model can be obtained by simple algebraic equations but, in the majority of the physical systems, the relevant reality is too hard to model with simple ordinary differential or difference equations. This is the case of systems with nonlinear or nonstationary behaviour which require more complex models. The discrete time-series problem, obtained by sampling the solar radiation, can be framed in this type of situation. By observing the collected data it is possible to distinguish multiple regimes. Additionally, due to atmospheric disturbances such as clouds, the temporal structure between samples is complex and is best described by nonlinear models. This paper reports the solar radiation prediction by using hybrid model that combines support vector regression paradigm and Markov chains. The hybrid model performance is compared with the one obtained by using other methods like autoregressive (AR) filters, Markov AR models, and artificial neural networks. The results obtained suggests an increasing prediction performance of the hybrid model regarding both the prediction error and dynamic behaviour.

Abstract
The improvement of energy performance in buildings can be achieved through the integration of a Trombe wall system. The literature review reveals that more research work is still required to evaluate the real impact of this system on the building thermal performance. The study here presented aims to define a calculation methodology of the Trombe wall energy performance, based on ISO 13790:2008(E), adapted to the Portuguese climatic conditions. The massive wall thickness, the ventilation system and the external shutters influence in the system thermal performance is demonstrated. It was concluded that the highest contributions to the global heat gains is given by the heat transfer by conduction, convection and radiation. However, the existence of a ventilation system in the massive wall has a significant role in the thermal performance of the Trombe wall, which contribution increases with the increasing of the massive wall thickness. It was also applied the Portuguese thermal regulation to a residential building with this system. It was concluded that energy heating needs can be reduced in 16.36% if a Trombe wall is added to the building envelope. The results also showed that the proposed methodology provides a valid approach to compute the Trombe wall thermal performance.