Parallelization of web processing services on cloud computing: A case study of Geostatistical Methods

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

HIGH SENSITIVE PCR METHOD FOR DETECTION OF PATHOGENIC Leptospira spp. IN PARAFFIN-EMBEDDED TISSUES

This study describes the development and application of a new PCR assay for the specific detection of pathogenic leptospires and its comparison with a previously reported PCR protocol. New primers were designed for PCR optimization and evaluation in artificially-infected paraffin-embedded tissues. PCR was then applied to post-mortem, paraffin-embedded samples, followed by amplicon sequencing. The PCR was more efficient than the reported protocol, allowing the amplification of expected DNA fragment from the artificially infected samples and from 44% of the post-mortem samples. The sequences of PCR amplicons from different patients showed >99% homology with pathogenic leptospires DNA sequences. The applicability of a highly sensitive and specific tool to screen histological specimens for the detection of pathogenic Leptospira spp. would facilitate a better assessment of the prevalence and epidemiology of leptospirosis, which constitutes a health problem in many countries.

Context-aware personalization environment for mobile computing

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Production of aluminium based composites reinforced with embedded NiTi by friction stir welding

Dissertação para a obtenção do grau de Mestre em Engenharia Mecânica

Processo de desenvolvimento e difusão de cloud computing: estudo sobre as redes de colaboração no Brasil

Com base no relatório de Projecto III para o Programa Doutoral em Avaliação de Tecnologia (2011-2012)

Cloud computing adoption - Determinants of cloud computing adoption by firms

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Printed electronics for ubiquitous computing applications

Dissertação para obtenção do Grau de Doutor em Química

Chemical approaches to ubiquitous computing

Dissertação apresentada para obtenção do Grau de Doutor em Química, perfil de Química Física, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Mainroad: Cloud computing

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

Intrusion tolerant routing with data consensus in wireless sensor networks

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Estimating data divergence in cloud computing storage systems

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Personalization platform for multimodal ubiquitous computing applications

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Narrative Bifurcation, Cloud Computing Interfaces and Hitchcok

From a narratological perspective, this paper aims to address the theoretical issues concerning the functioning of the so called «narrative bifurcation» in data presentation and information retrieval. Its use in cyberspace calls for a reassessment as a storytelling device. Films have shown its fundamental role for the creation of suspense. Interactive fiction and games have unveiled the possibility of plots with multiple choices, giving continuity to cinema split-screen experiences. Using practical examples, this paper will show how this storytelling tool returns to its primitive form and ends up by conditioning cloud computing interface design.

Petri net based development of globally-asynchronous locally-synchronous distributed embedded systems

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

Heterogeneous computing with an algorithmic skeleton framework

The Graphics Processing Unit (GPU) is present in almost every modern day personal computer. Despite its specific purpose design, they have been increasingly used for general computations with very good results. Hence, there is a growing effort from the community to seamlessly integrate this kind of devices in everyday computing. However, to fully exploit the potential of a system comprising GPUs and CPUs, these devices should be presented to the programmer as a single platform. The efficient combination of the power of CPU and GPU devices is highly dependent on each device’s characteristics, resulting in platform specific applications that cannot be ported to different systems. Also, the most efficient work balance among devices is highly dependable on the computations to be performed and respective data sizes. In this work, we propose a solution for heterogeneous environments based on the abstraction level provided by algorithmic skeletons. Our goal is to take full advantage of the power of all CPU and GPU devices present in a system, without the need for different kernel implementations nor explicit work-distribution.To that end, we extended Marrow, an algorithmic skeleton framework for multi-GPUs, to support CPU computations and efficiently balance the work-load between devices. Our approach is based on an offline training execution that identifies the ideal work balance and platform configurations for a given application and input data size. The evaluation of this work shows that the combination of CPU and GPU devices can significantly boost the performance of our benchmarks in the tested environments, when compared to GPU-only executions.