Abstract
Several developments have been observed recently in areas such as Web development, social networks, interface design, recommendation systems and Geographic Information Systems (GIS). The integration of these developments can provide a superior experience, greater than the sum of their individual contributions, regarding user satisfaction. This paper proposes an integration of all these innovations in e-tourism, more specifically by the development of a Web based geographic information platform adaptable to any tourist region. As a case study, we also show how this platform was adapted to the Douro region, in Portugal. The Web platform developed as a proof of concept combines geospatial information from diverse and heterogeneous data sources, encompassing events, news, routes and points of interest (POI). This platform provides also a recommendation engine and features the possibility that users can contribute with content as part of the community, thus emerging a mini social network. © 2011 AISTI.

Abstract
This paper presents an initiative to involve ECE undergraduate students in the design and deployment of a network infrastructure for an academic laboratory. The project aims at attaining a reliable and secure network for an IC CAD environment. The students focused on employing secure authentication, accounting and storage with single sign-on, based on enterprise-grade, open-source protocols. This initiative proved to be highly motivating and allowed the students to develop knowledge and hands-on experience on the area of network security. The resulting network design and core infrastructure is herein described as well as its deployment in a real microelectronics design environment. © 2011 IEEE.

Abstract
The restoration and recovery of a defective skull can be performed through operative techniques to implant a customized prosthesis. Recently, image processing and surface reconstruction methods have been used for digital prosthesis design. In this paper we present a framework for prosthesis modeling. Firstly, we take the computed tomography (CT) of the skull and perform bone segmentation by thresholding. The obtained binary volume is processed by morphological operators, frame-by-frame, to get the inner and outer boundaries of the bone. These curves are used to initialize a 2D deformable model that generates the prosthesis boundary in each CT frame. In this way, we can fill the prosthesis volume which is the input for a marching cubes technique that computes the digital model of the target geometry. In the experimental results we demonstrate the potential of our technique and compare it with a related one.

Abstract
Computer aided diagnosis systems with the capability of automatically decide if a patient has or not a pathology and to hold the decision on the dificult cases, are becoming more frequent. The latter are afterwards reviewed by an expert reducing therefore time consuption on behalf of the expert. The number of cases to review depends on the cost of erring the diagnosis. In this work we analyse the incorporation of the option to hold a decision on the diagnostic of pathologies on the vertebral column. A comparison with several state of the art techniques is performed. We conclude by showing that the use of the reject option techniques is an asset in line with the current view of the research community.

Abstract
Image binarization is a common operation in the preprocessing stage in most Optical Music Recognition (OMR) systems. The choice of an appropriate binarization method for handwritten music scores is a difficult problem. Several works have already evaluated the performance of existing binarization processes in diverse applications. However, no goal-directed studies for music sheets documents were carried out. This paper presents a novel binarization method based in the content knowledge of the image. The method only needs the estimation of the staffline thickness and the vertical distance between two stafflines. This information is extracted directly from the gray level music score. The proposed binarization procedure is experimentally compared with several state of the art methods.

Abstract
While ordinal classification problems are common in many situations, induction of ordinal decision trees has not evolved significantly. Conventional trees for regression settings or nominal classification are commonly induced for ordinal classification problems. On the other hand a decision tree consistent with the ordinal setting is often desirable to aid decision making in such situations as credit rating. In this work we extend a recently proposed strategy based on constraints defined globally over the feature space. We propose a bootstrap technique to improve the accuracy of the baseline solution. Experiments in synthetic and real data show the benefits of our proposal. © 2011 IEEE.