Abstract
This paper describes an architecture which can be used to access geographic information from mobile devices with limited display and processing characteristics, such as Personal Digital Assistants (PDA) or Mobile Phones. The information may come from different sources leading to an interoperable solution. M-GIS obtains geographic information from sources described using Geogxraphy Markup Language (GML) from one or more Web Feature Servers allowing an access independent from its format or physical location, as long as the information is according to the specification. Using XSLT, GML is transformed to a graphical format in Scalable Vector Graphics (SVG) which can then be manipulated in the mobile device. M-GIS follows a client/server architecture and the client application was developed using Java Mobile Information Device Profile technology. The results enable us to conclude that the mobile system, designed and developed with the use of open standards and representation formats to access geographic information, is a viable solution with some limitations. The main limitations of this approach have to do with the amount of information which the client can, at the moment, accommodate.

Abstract
The Support Vector Machine algorithm is sensitive to the choice of parameter settings. If these are not set correctly, the algorithm may have a substandard performance. Suggesting a good setting is thus an important problem. We propose a meta-learning methodology for this purpose and exploit information about the past performance of different settings. The methodology is applied to set the width of the Gaussian kernel. We carry out an extensive empirical evaluation, including comparisons with other methods (fixed default ranking; selection based on cross-validation and a heuristic method commonly used to set the width of the SVM kernel). We show that our methodology can select settings with low error while providing significant savings in time. Further work should be carried out to see how the methodology could be adapted to different parameter setting tasks.

Abstract
This paper proposes a multi-objective genetic algorithm to optimize a manipulator trajectory. The planner has several objectives namely the minimization of the space and join arm displacements and the energy required in the trajectory, without colliding with any obstacles in the workspace. Simulations results are presented for robots with two and three degrees of freedom, considering the optimization of two and three objectives.

Abstract
This paper describes a study of planning vehicle routes for the collection of urban recyclable waste. The aim is to create collection routes for every day of the month, to be repeated every month, minimizing the operation cost. Two important features of the problem are the planning of a relatively long period of time and the separate collection of three types of waste. The collection operation was modelled in accordance to the practice of the company that manages the collection system. Heuristic techniques were developed to solve the model in three phases: definition of the geographic zones served by the vehicles, definition of the waste type to collect on each day of the month, definition of the collection routes. Preliminary results suggest that significant economies in collection costs are possible.

Abstract

Abstract
Semiconductor nano-particles, or quantum dots, with their relatively high quantum yields, narrow luminescence spectrum, outstanding photostability and the ability to tune their optical properties, are ideal for biological tagging applications and a very powerful tool for chemical sensors. In this paper an overview of this rapidly expanding area of research is presented. Additionally, some results are shown, in the framework of optical oxygen sensors, which establish quantum dots as suitable temperature and intensity references for application in luminescence based chemical sensors.