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.

Abstract
The coefficient diagram method is primarily an algebraic control design method whose objective is to easily obtain a good controller with minimum user effort. As a matter of fact, if a system model, in the form of linear differential equations, is known, the user only need to define a time-constant and the controller order. The later can be established regarding the expected disturbance type via a lookup table first published by Koksal and Hamamci in 2004. However an inaccuracy in this table was detected and pointed-out in the present work. Moreover the above mentioned table was expanded in order to enclose any k order type disturbance

Abstract

Abstract
The paper presents a study about optimal supply of the energy service, using simulations of network operation scenarios, in order to optimize resources and minimize the variables: operation cost, energy losses, generation cost and consumers shedding. These simulations create optimal operation models of the network, allowing the system operator obtain knowledge to take pre-established procedures that must be performed in situations of contingency in order to forecast and minimize drawbacks. The simulations were performed using a multiobjective particle swarm optimization algorithm. The algorithm was applied to the IEEE 14 Bus network where the optimal power flow was evaluated by the MATPOWER tool to establish an optimal electrical working model to minimize the associated costs.