Sistema de vigilância de morbilidade pediátrica no Hospital Geral do Bengo

Angola tem registado avanços significativos na redução da mortalidade infantil. Não obstante, verifica-se ainda a necessidade de implementar novas medidas no sentido de reduzir a morbimortalidade infantil. Para atingir este objectivo foi implementado um Sistema de Vigilância de Morbilidade (SVM) no serviço de Pediatria do Hospital Geral do Bengo (HGB). O objectivo deste trabalho é a identificação das doenças mais frequentes detectadas no serviço de urgência do HGB.

Classificação e agregação automática de notícias desportivas

Mestrado em Engenharia Informática - Área de Especialização em Arquiteturas, Sistemas e Redes

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

Tradutor da língua gestual portuguesa modelo de tradução bidireccional

O projeto tem como objetivo desenvolver e avaliar um modelo que facilita o acesso para pessoas surdas ou com deficiência auditiva, o acesso ao conteúdo digital - em particular o conteúdo educacional e objetos de aprendizagem – a criação de condições para uma maior inclusão social de surdos e deficientes auditivos. Pretende-se criar um modelo bidirecional, em que permite a pessoas com deficiências auditivas, possam se comunicar com outras pessoas, com a tradução da Língua Gestual Portuguesa (LGP) para a Língua Portuguesa (LP) e que outras pessoas não portadoras de qualquer deficiência auditiva possam por sua vez comunicar com os surdos ou deficientes auditivos através da tradução da LP para a LGP. Há um conjunto de técnicas que poderíamos nos apoiar para desenvolver o modelo e implementar a API de tradução da LGP em LP. Muitos estudos são feitos com base nos modelos escondidos de Markov (HMM) para efetuar o reconhecimento. Recentemente os estudos estão a caminhar para o uso de técnicas como o “Dynamic Time Warping” (DTW), que tem tido mais sucesso do que outras técnicas em termos de performance e de precisão. Neste projeto optamos por desenvolver a API e o Modelo, com base na técnica de aprendizagem Support Vector Machines (SVM) por ser uma técnica simples de implementar e com bons resultados demonstrados em reconhecimento de padrões. Os resultados obtidos utilizando esta técnica de aprendizagem foram bastante ótimos, como iremos descrever no decorrer do capítulo 4, mesmo sabendo que utilizamos dois dispositivos para capturar dados de descrição de cada gesto. Toda esta tese integra-se no âmbito do projeto científico/ investigação a decorrer no grupo de investigação GILT, sob a coordenação da professora Paula Escudeiro e suportado pela Fundação para Ciência e Tecnologia (FCT).

Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Speed control of induction machine based on direct torque control method

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

Análise comparada da classificação “pixel a pixel” e orientada por objecto de uma imagem WorldView 2 de Maputo, Moçambique

O desenvolvimento das tecnologias associadas à Detecção Remota e aos Sistemas de Informação Geográfica encontram-se cada vez mais na ordem do dia. E, graças a este desenvolvimento de métodos para acelerar a produção de informação geográfica, assiste-se a um crescente aumento da resolução geométrica, espectral e radiométrica das imagens, e simultaneamente, ao aparecimento de novas aplicações com o intuito de facilitar o processamento e a análise de imagens através da melhoria de algoritmos para extracção de informação. Resultado disso são as imagens de alta resolução, provenientes do satélite WorldView 2 e o mais recente software Envi 5.0, utilizados neste estudo. O presente trabalho tem como principal objectivo desenvolver um projecto de cartografia de uso do solo para a cidade de Maputo, com recurso ao tratamento e à exploração de uma imagem de alta resolução, comparando as potencialidades e limitações dos resultados extraídos através da classificação “pixel a pixel”, através do algoritmo Máxima Verossimilhança, face às potencialidades e eventuais limitações da classificação orientada por objecto, através dos algoritmos K Nearest Neighbor (KNN) e Support Vector Machine (SVM), na extracção do mesmo número e tipo de classes de ocupação/uso do solo. Na classificação “pixel a pixel”, com a aplicação do algoritmo classificação Máxima Verosimilhança, foram ensaiados dois tipos de amostra: uma primeira constituída por 20 classes de ocupação/uso do solo, e uma segunda por 18 classes. Após a fase de experimentação, os resultados obtidos com a primeira amostra ficaram aquém das espectativas, pois observavam-se muitos erros de classificação. A segunda amostra formulada com base nestes erros de classificação e com o objectivo de os minimizar, permitiu obter um resultado próximo das espectativas idealizadas inicialmente, onde as classes de interesse coincidem com a realidade geográfica da cidade de Maputo. Na classificação orientada por objecto foram 4 as etapas metodológicas utilizadas: a atribuição do valor 5 para a segmentação e 90 para a fusão de segmentos; a selecção de 15 exemplos sobre os segmentos gerados para cada classe de interesse; bandas diferentemente distribuídas para o cálculo dos atributos espectrais e de textura; os atributos de forma Elongation e Form Factor e a aplicação dos algoritmos KNN e SVM. Confrontando as imagens resultantes das duas abordagens aplicadas, verificou-se que a qualidade do mapa produzido pela classificação “pixel a pixel” apresenta um nível de detalhe superior aos mapas resultantes da classificação orientada por objecto. Esta diferença de nível de detalhe é justificada pela unidade mínima do processamento de cada classificador: enquanto que na primeira abordagem a unidade mínima é o pixel, traduzinho uma maior detalhe, a segunda abordagem utiliza um conjunto de pixels, objecto, como unidade mínima despoletando situações de generalização. De um modo geral, a extracção da forma dos elementos e a distribuição das classes de interesse correspondem à realidade geográfica em si e, os resultados são bons face ao que é frequente em processamento semiautomático.

Botnet cluster identification

Botnets are a group of computers infected with a specific sub-set of a malware family and controlled by one individual, called botmaster. This kind of networks are used not only, but also for virtual extorsion, spam campaigns and identity theft. They implement different types of evasion techniques that make it harder for one to group and detect botnet traffic. This thesis introduces one methodology, called CONDENSER, that outputs clusters through a self-organizing map and that identify domain names generated by an unknown pseudo-random seed that is known by the botnet herder(s). Aditionally DNS Crawler is proposed, this system saves historic DNS data for fast-flux and double fastflux detection, and is used to identify live C&Cs IPs used by real botnets. A program, called CHEWER, was developed to automate the calculation of the SVM parameters and features that better perform against the available domain names associated with DGAs. CONDENSER and DNS Crawler were developed with scalability in mind so the detection of fast-flux and double fast-flux networks become faster. We used a SVM for the DGA classififer, selecting a total of 11 attributes and achieving a Precision of 77,9% and a F-Measure of 83,2%. The feature selection method identified the 3 most significant attributes of the total set of attributes. For clustering, a Self-Organizing Map was used on a total of 81 attributes. The conclusions of this thesis were accepted in Botconf through a submited article. Botconf is known conferênce for research, mitigation and discovery of botnets tailled for the industry, where is presented current work and research. This conference is known for having security and anti-virus companies, law enforcement agencies and researchers.

Land cover mapping analysis and urban growth modeling using remote sensing techniques

The rapid growth of big cities has been noticed since 1950s when the majority of world population turned to live in urban areas rather than villages, seeking better job opportunities and higher quality of services and lifestyle circumstances. This demographic transition from rural to urban is expected to have a continuous increase. Governments, especially in less developed countries, are going to face more challenges in different sectors, raising the essence of understanding the spatial pattern of the growth for an effective urban planning. The study aimed to detect, analyse and model the urban growth in Greater Cairo Region (GCR) as one of the fast growing mega cities in the world using remote sensing data. Knowing the current and estimated urbanization situation in GCR will help decision makers in Egypt to adjust their plans and develop new ones. These plans should focus on resources reallocation to overcome the problems arising in the future and to achieve a sustainable development of urban areas, especially after the high percentage of illegal settlements which took place in the last decades. The study focused on a period of 30 years; from 1984 to 2014, and the major transitions to urban were modelled to predict the future scenarios in 2025. Three satellite images of different time stamps (1984, 2003 and 2014) were classified using Support Vector Machines (SVM) classifier, then the land cover changes were detected by applying a high level mapping technique. Later the results were analyzed for higher accurate estimations of the urban growth in the future in 2025 using Land Change Modeler (LCM) embedded in IDRISI software. Moreover, the spatial and temporal urban growth patterns were analyzed using statistical metrics developed in FRAGSTATS software. The study resulted in an overall classification accuracy of 96%, 97.3% and 96.3% for 1984, 2003 and 2014’s map, respectively. Between 1984 and 2003, 19 179 hectares of vegetation and 21 417 hectares of desert changed to urban, while from 2003 to 2014, the transitions to urban from both land cover classes were found to be 16 486 and 31 045 hectares, respectively. The model results indicated that 14% of the vegetation and 4% of the desert in 2014 will turn into urban in 2025, representing 16 512 and 24 687 hectares, respectively.

FPGA field oriented control of an axial flux motor-in-wheel

This paper presents the design and the prototype implementation of a three-phase power inverter developed to drive a motor-in-wheel. The control system is implemented in a FPGA (Field Programmable Gate Array) device. The paper describes the Field Oriented Control (FOC) algorithm and the Space Vector Modulation (SVM) technique that were implemented. The control platform uses a Spartan-3E FPGA board, programmed with Verilog language. Simulation and experimental results are presented to validate the developed system operation under different load conditions. Finally are presented conclusions based on the experimental results.

Field oriented control of an axial flux permanent magnet synchronous motor for traction solutions

Electric Vehicles (EVs) are increasingly used nowadays, and different powertrain solutions can be adopted. This paper describes the control system of an axial flux Permanent Magnet Synchronous Motor (PMSM) for EVs powertrain. It is described the implemented Field Oriented Control (FOC) algorithm and the Space Vector Modulation (SVM) technique. Also, the mathematical model of the PMSM is presented. Both, simulation and experimental, results with different types of mechanical load are presented. The experimental results were obtained using a laboratory test bench. The obtained results are discussed.

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.