Hub-and-spoke Interoperability: an out of the skies approach for large-scale data interoperability

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores

Corruption and Education: Empirical evidence from cross-country micro-data

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Spatial Patterns and Irregularities of the electoral data: general elections in Canada

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

QBC® for the diagnosis of human and canine american visceral leishmaniasis: preliminary data

"Quantitative Buffy Coat" (QBC®) is a direct and fast fluorescent method used for the identification of blood parasites. Since Leishmania chagasi circulates in blood, we decided to test it in American visceral leishmaniasis (AVL). Bone marrow (BM) and peripheral blood (PB) of 49 persons and PB of 31 dogs were analyzed. QBC® was positive in BM of 11/11 patients with AVL and in 1/6 patients with other diseases. Amastigotes were identified in PB of 18/22 patients with AVL and in none without AVL. The test was positive in 30 out of the 31 seropositive dogs and in 28/28 dogs with Leishmania identified in other tissues. QBC® is a promising method for diagnosis of human AVL, and possibly for the exam of PB of patients with AVL/AIDS, for the control of the cure and for the identification of asymptomatic carriers. Because it is fast and easy to collect and execute, QBC® should be evaluated for programs of reservoir control.

Collecting data for indoor mapping of the university of Münster via a location based game

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species

An important aspect of tropical medicine is analysis of geographic aspects of risk of disease transmission, which for lack of detailed public health data must often be reduced to an understanding of the distributions of critical species such as vectors and reservoirs. We examine the applicability of a new technique, ecological niche modeling, to the challenge of understanding distributions of such species based on municipalities in the state of São Paulo in which a group of 5 Lutzomyia sandfly species have been recorded. The technique, when tested based on independent occurrence data, yielded highly significant predictions of species' distributions; minimum sample sizes for effective predictions were around 40 municipalities.

Multivariate analysis applied to clinical analysis data

Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão Industrial

Data fusion with computational intelligence techniques: a case study of fuzzy inference for terrain assessment

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Evaluation and optimization of a session-based middleware for data management

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

A system’s approach to cache hierarchy-aware decomposition of data-parallel computations

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Hydrogen permeation in parabolic trough receivers

The continued economic and population development puts additional pressure on the already scarce energetic sources. Thus there is a growing urge to adopt a sustainable plan able to meet the present and future energetic demands. Since the last two decades, solar trough technology has been demonstrating to be a reliable alternative to fossil fuels. Currently, the trough industry seeks, by optimizing energy conversion, to drive the cost of electricity down and therefore to place itself as main player in the next energetic age. One of the issues that lately have gained considerable relevance came from the observation of significant heat losses in a large number of receiver modules. These heat losses were attributed to slow permeation of traces of hydrogen gas through the steel tube wall into the vacuum annulus. The presence of hydrogen gas in the absorber tube results from the decomposition of heat transfer fluid due to the long-term exposure to 400°C. The permeated hydrogen acts as heat conduction mean leading to a decrease in the receivers performance and thus its lifetime. In order to prevent hydrogen accumulation, it has been common practice to incorporate hydrogen getters in the vacuum annulus of the receivers. Nevertheless these materials are not only expensive but their gas absorbing capacity can be insufficient to assure the required level of vacuum for the receivers to function. In this work the building of a permeation measurement device, vulnerabilities detected in the construction process and its overcome are described. Furthermore an experimental procedure was optimized and the obtained permeability results, of different samples were evaluated. The data was compared to measurements performed by an external entity. The reliability of the comparative data was also addressed. In the end conclusions on the permeability results for the different samples characteristics, feasibility of the measurement device are drawn and recommendations on future line of work were made.

Combining WLAN fingerprint-based localization with sensor data for indoor navigation using mobile devices

This project proposes an approach for supporting Indoor Navigation Systems using Pedestrian Dead Reckoning-based methods and by analyzing motion sensor data available in most modern smartphones. Processes suggested in this investigation are able to calculate the distance traveled by a user while he or she is walking. WLAN fingerprint- based navigation systems benefit from the processes followed in this research and results achieved to reduce its workload and improve its positioning estimations.

New visualization model for large scale biosignals analysis

Benefits of long-term monitoring have drawn considerable attention in healthcare. Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent. However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task. In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out. Three case studies are presented and discussed and a usability study supports the reliability of the implemented work.

Complex networks and data mining: toward a new perspective for the understanding of complex systems

Complex systems, i.e. systems composed of a large set of elements interacting in a non-linear way, are constantly found all around us. In the last decades, different approaches have been proposed toward their understanding, one of the most interesting being the Complex Network perspective. This legacy of the 18th century mathematical concepts proposed by Leonhard Euler is still current, and more and more relevant in real-world problems. In recent years, it has been demonstrated that network-based representations can yield relevant knowledge about complex systems. In spite of that, several problems have been detected, mainly related to the degree of subjectivity involved in the creation and evaluation of such network structures. In this Thesis, we propose addressing these problems by means of different data mining techniques, thus obtaining a novel hybrid approximation intermingling complex networks and data mining. Results indicate that such techniques can be effectively used to i) enable the creation of novel network representations, ii) reduce the dimensionality of analyzed systems by pre-selecting the most important elements, iii) describe complex networks, and iv) assist in the analysis of different network topologies. The soundness of such approach is validated through different validation cases drawn from actual biomedical problems, e.g. the diagnosis of cancer from tissue analysis, or the study of the dynamics of the brain under different neurological disorders.