Abstract
With the recent expansion of services available in the Web, the Internet is becoming more and more a network of services, at a global scale. These services cover the majority of business areas, such as: bank account management, trip reservation, hotel bookings, information search portals, social networks, etc. A related subarea is the georreferencing of objects and people. The appearance of services like Google Earth and Virtual Earth has created enormous Web based service creation opportunities, allowing the interconnection of the digital and real worlds. In this work we present a project intended for locating the physical places associated with a person, based on his email address. This project implements a search portal that associates a user to an email and a set of user defined locations.

Abstract
Assistive technology enables people to achieve independence in the accomplishment of their daily tasks and enhance their quality of life. Visual information is the basis for most navigational tasks, so visually impaired individuals are at disadvantage due to the lack of information or given insufficient information about their surrounding environment. With the recent advances in inclusive technology it is possible to extend the support given to people with visual disabilities during their mobility. In this context we propose a new algorithm to recognize landmarks suitably placed on sidewalks. The proposed algorithm uses a combination of Peano-Hilbert Space Filling Curves for dimension reduction of image data and Ensemble Empirical Mode Decomposition (EEMD) to pre-process the image, resulting on a fast and efficient recognition method and revealing a promising solution.

Abstract

Abstract
Information technology and information systems have evolved dramatically over the last half-century, playing an absolutely central and crucial role in the success of today's organizations. Therefore, the complexity of the information systems function has also increased significantly, which requires the use of more evolved software tools to support the information system function activities. In this paper we present a matrix of the main tools available for the information system function, which can help Chief Information Officers to identify the right tools to use in their departments.

Abstract
Wireless capsule endoscopy (WCE) is a revolutionary, patient-friendly imaging technique that enables non-invasive visual inspection of the patient's digestive tract, especially small intestine. However, the time-consuming task of reviewing the endoscopic data a burden for the physicians. This limitation was the propose a novel strategy for automatic discrimination of WCE images related to ulcer, the most common finding of digestive tract Towards this direction, WCE data are processed with Bidimensional Ensemble Empirical Mode Decomposition to reveal their inherent structural components, and also to reconstruct a new refined image. Then, texture information is extracted by analyzing the intrinsic second/higher-order correlation of the original image and by calculating the lacunarity index of the refined image. Experimental results demonstrated promising classification accuracy (97%) exhibiting high potential towards a complete computer-aided diagnosis system.

Abstract
High-resolution computed tomography (HRCT) became an essential tool in detection, characterization and follow-up of lung diseases. In this paper we focus on lung emphysema, a long-term and progressive disease characterized by the destruction of lung tissue. The lung patterns are represented by different features vectors, extracted from statistical texture analysis methods (spatial gray level dependence, gray level run length method and gray level difference method). Support vector machine (SVM) was trained to discriminate regions of healthy lung tissue from emphysematous regions. The SVM model optimization was performed in the training dataset through a cross validation methodology, along a grid search. Three usual kernel functions were tested in each of the features sets. This study highlights the importance of the kernel choice and parameters tuning to obtain models that allow high level performance of the SVM classifier.