928 resultados para Automatic annotation
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Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information Systems
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Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática
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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Informática
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Electricity markets worldwide suffered profound transformations. The privatization of previously nationally owned systems; the deregulation of privately owned systems that were regulated; and the strong interconnection of national systems, are some examples of such transformations [1, 2]. In general, competitive environments, as is the case of electricity markets, require good decision-support tools to assist players in their decisions. Relevant research is being undertaken in this field, namely concerning player modeling and simulation, strategic bidding and decision-support.
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The study of Electricity Markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring produced. Currently, lots of information concerning Electricity Markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge, to define realistic scenarios, essential for understanding and forecast Electricity Markets behaviour. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of Electricity Markets and the behaviour of the involved entities. In this paper we present an adaptable tool capable of downloading, parsing and storing data from market operators’ websites, assuring actualization and reliability of stored data.
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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e Computadores
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Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática
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A monitorização da atividade física é um tema que tem adquirido cada vez mais importância. Tal deve-se ao crescente sedentarismo da população em geral e adquirindo níveis muito elevados de importância devido a vários fatores como por exemplo o enorme crescimento tecnológico e menor tempo de lazer. Cada vez mais a população tem a tendência de substituir atividades como uma simples caminhada para o trabalho ou escola por algum tipo de tecnologia que reduz o consumo energético do corpo, sendo paradigmático o uso (excessivo) de viaturas automóveis. Em consequência da escassez de atividade física, doenças como a obesidade e problemas cardíacos têm vindo a aumentar nas várias faixas etárias, mas assume uma particular relevância em crianças. Nas últimas décadas têm aumentado as iniciativas de investigação com o objetivo de compreender os fatores que afetam a prática de atividade física para posteriormente a potenciar. Existem diversos métodos contudo, destaca-se preferencialmente os de observação direta, com observadores presentes. No entanto estes apresentam algumas limitações. Consequentemente são necessários esforços de investigação adicionais e novas técnicas ou metodologias. Nesta dissertação pretende-se contribuir ativamente para a investigação na área da promoção de atividade física através da utilização de vídeo, com uma análise realizada sobre dois pontos principais. Primeiro são analisadas métodos do estado de arte que requerem a presença de observadores e de que forma a captura de vídeos pode ser utilizada como alternativa ou complemento. De seguida, é realizado um estudo e avançada uma proposta inicial para utilizar mecanismos de processamento e classificação automática da atividade em alternativa ao observador humano.
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In this paper, a rule-based automatic syllabifier for Danish is described using the Maximal Onset Principle. Prior success rates of rule-based methods applied to Portuguese and Catalan syllabification modules were on the basis of this work. The system was implemented and tested using a very small set of rules. The results gave rise to 96.9% and 98.7% of word accuracy rate, contrary to our initial expectations, being Danish a language with a complex syllabic structure and thus difficult to be rule-driven. Comparison with data-driven syllabification system using artificial neural networks showed a higher accuracy rate of the former system.
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In the last few years the number of systems and devices that use voice based interaction has grown significantly. For a continued use of these systems the interface must be reliable and pleasant in order to provide an optimal user experience. However there are currently very few studies that try to evaluate how good is a voice when the application is a speech based interface. In this paper we present a new automatic voice pleasantness classification system based on prosodic and acoustic patterns of voice preference. Our study is based on a multi-language database composed by female voices. In the objective performance evaluation the system achieved a 7.3% error rate.
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Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica
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BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.
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Research Project submited as partial fulfilment for the Master Degree in Statistics and Information Management
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Dissertação para obtenção do Grau de Mestre em Engenharia Informática