Abstract

Abstract
Intelligent Wheelchair (IW) is a new concept aiming to allow higher autonomy to people with lower mobility such as disabled or elderly individuals. Some of the more recent IWs have a multimodal interface, enabling multiple command modes such as joystick, voice commands, head movements, or even facial expressions. In these IW it may be very useful to provide the user with the best way of driving it through an adaptive interface. This paper describes the foundations for creating a simple methodology for extracting user profiles, which can be used to adequately select the best IW command mode for each user. The methodology is based on an interactive wizard composed by a flexible set of simple tasks presented to the user, and a method for extracting and analyzing the user's execution of those tasks. The results achieved showed that it is possible to extract simple user profiles, using the proposed method. Thus, the approach may be further used to extract more complete user profiles, just by extending the set of tasks used, enabling the adaptation of the IW interface to each user's characteristics.

Abstract
RoboCup is an international cooperative research project aimed at promoting research in Artificial Intelligence and Robotics. It includes a simulation league where two teams of 11 players compete in a robotic soccer game very similar to real soccer. Teams exhibit very complex strategies in these games that are very difficult to analyze by conventional observation methods. This paper presents an approach to the visualization of simulated robotic soccer games using the RapidMiner software package. Various visualizations were developed using Andrew's Curves, Survey Plots, several types of Parallel Coordinate visualizations and Radial Coordinate Visualizations. These visualizations enabled to take some interesting conclusions about the differences between games of FC Portugal robotic soccer team using different formations and against distinct opponents.

Abstract
In soccer, the level of performance is determined by a number of a complex variables interrelated: technique, tactics, psychological factors and finally, fitness. Because of this, analyzing this information in a real-time, even for soccer experts like professional coaches has become an impossible task. Automatic event detection tools occupy an important role in this reality, although nowadays there isn't any tool capable of producing information capable of helping a professional coach choosing his team strategy for a specific game. In this research project an automatic event detection tool is purposed and, a set of game statistics defined by a group of sports researchers. All the teams present in the 2009 RoboCup tournament have a pass success rate superior to 65%. These statistics provide an interesting viewpoint on how to evaluate a team performance, such as the importance of dominating the opposing team field without losing the control of our own (this can be seen on the top 3 zone dominance statistics). In the future this project will serve as a base for building a Framework capable of simulating a match between two heterogeneous soccer teams and produce reliable information for optimizing the team performance.

Abstract
Simulation is a very useful tool to gather new information about an implemented model, because it can run artificial environments instead of putting in risk some entities that are influenced in the real process. The simulation of physical, chemical and biological processes in coastal ecosystems is used as a way to understand the system internal dynamics and to predict its evolution over time, in order to promote behaviors environmentally friendly and to induce effective and efficient management of the ecosystem as a whole. However, there are several ways of translating and interpreting the data provided by the simulation such as applying appropriate data mining models. This paper describes an approach that uses a Decision Tree model to produce intuitive information about the influence of several environmental variables on the growth conditions of bivalve species within an aquaculture exploration in a coastal ecosystem. This information is captured by relating the values of simulated variables, like water temperature or organic matter, with the length of the bivalve's shell, extrapolating information about the organic or physical conditions that increase or decrease the growth of the bivalve species, and guiding the stakeholders to locations for the best practice of the seeding process.

Abstract
Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. This paper presents a comparative study of four classification algorithms for static hand gesture classification using two different hand features data sets. The approach used consists in identifying hand pixels in each frame, extract features and use those features to recognize a specific hand pose. The results obtained proved that the ANN had a very good performance and that the feature selection and data preparation is an important phase in the all process, when using low-resolution images like the ones obtained with the camera in the current work.