15 resultados para Process control -- Data processing

em Repositório Científico do Instituto Politécnico de Lisboa - Portugal


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Trabalho de Projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores

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Coronary artery disease (CAD) is currently one of the most prevalent diseases in the world population and calcium deposits in coronary arteries are one direct risk factor. These can be assessed by the calcium score (CS) application, available via a computed tomography (CT) scan, which gives an accurate indication of the development of the disease. However, the ionising radiation applied to patients is high. This study aimed to optimise the protocol acquisition in order to reduce the radiation dose and explain the flow of procedures to quantify CAD. The main differences in the clinical results, when automated or semiautomated post-processing is used, will be shown, and the epidemiology, imaging, risk factors and prognosis of the disease described. The software steps and the values that allow the risk of developingCADto be predicted will be presented. A64-row multidetector CT scan with dual source and two phantoms (pig hearts) were used to demonstrate the advantages and disadvantages of the Agatston method. The tube energy was balanced. Two measurements were obtained in each of the three experimental protocols (64, 128, 256 mAs). Considerable changes appeared between the values of CS relating to the protocol variation. The predefined standard protocol provided the lowest dose of radiation (0.43 mGy). This study found that the variation in the radiation dose between protocols, taking into consideration the dose control systems attached to the CT equipment and image quality, was not sufficient to justify changing the default protocol provided by the manufacturer.

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Data analytic applications are characterized by large data sets that are subject to a series of processing phases. Some of these phases are executed sequentially but others can be executed concurrently or in parallel on clusters, grids or clouds. The MapReduce programming model has been applied to process large data sets in cluster and cloud environments. For developing an application using MapReduce there is a need to install/configure/access specific frameworks such as Apache Hadoop or Elastic MapReduce in Amazon Cloud. It would be desirable to provide more flexibility in adjusting such configurations according to the application characteristics. Furthermore the composition of the multiple phases of a data analytic application requires the specification of all the phases and their orchestration. The original MapReduce model and environment lacks flexible support for such configuration and composition. Recognizing that scientific workflows have been successfully applied to modeling complex applications, this paper describes our experiments on implementing MapReduce as subworkflows in the AWARD framework (Autonomic Workflow Activities Reconfigurable and Dynamic). A text mining data analytic application is modeled as a complex workflow with multiple phases, where individual workflow nodes support MapReduce computations. As in typical MapReduce environments, the end user only needs to define the application algorithms for input data processing and for the map and reduce functions. In the paper we present experimental results when using the AWARD framework to execute MapReduce workflows deployed over multiple Amazon EC2 (Elastic Compute Cloud) instances.

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Lossless compression algorithms of the Lempel-Ziv (LZ) family are widely used nowadays. Regarding time and memory requirements, LZ encoding is much more demanding than decoding. In order to speed up the encoding process, efficient data structures, like suffix trees, have been used. In this paper, we explore the use of suffix arrays to hold the dictionary of the LZ encoder, and propose an algorithm to search over it. We show that the resulting encoder attains roughly the same compression ratios as those based on suffix trees. However, the amount of memory required by the suffix array is fixed, and much lower than the variable amount of memory used by encoders based on suffix trees (which depends on the text to encode). We conclude that suffix arrays, when compared to suffix trees in terms of the trade-off among time, memory, and compression ratio, may be preferable in scenarios (e.g., embedded systems) where memory is at a premium and high speed is not critical.

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Cork processing involves a boiling step to make the cork softer, which consumes a high volume of water and generates a wastewater with a high organic content, rich in tannins. An assessment of the final wastewater characteristics and of the boiling water composition along the boiling process was performed. The parameters studied were pH, color, total organic carbon (TOC), chemical and biochemical oxygen demands (COD, BOD5, BOD20), total suspended solids (TSS), total phenols and tannins (TP, TT). It was observed that the water solutes extraction power is significantly reduced for higher quantities of cork processed. Valid relationships between parameters were established not only envisaging wastewater characterization but also to provide an important tool for wastewater monitoring and for process control/optimization. Boiling water biodegradability presented decreasing values with the increase of cork processed and for the final wastewater its value is always lower than 0.5, indicating that these wastewaters are very difficult to treat by biological processes. The biodegradability was associated with the increase of tannin content that can rise up to 0.7 g/L. These compounds can be used by other industries when concentrated and the clarified wastewater can be reused, which is a potential asset in this wastewater treatment.

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Dissertação apresentada à Escola Superior de Educação de Lisboa para a obtenção do Grau de Mestre em Ciências da Educação - especialidade Supervisão em Educação

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Trabalho Final de Mestrado elaborado no Laboratório Nacional de Engenharia Civil (LNEC) para a obtenção do grau de Mestre em Engenharia Civil pelo Instituto Superior de Engenharia de Lisboa no âmbito do protocolo de cooperação entre o ISEL e o LNEC

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Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Educação Artística, na especialização de Teatro na Educação

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Relatório do Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

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Trabalho de Projeto para obtenção do grau de Mestre em Engenharia de Eletrónica e Telecomunicações

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Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Automação e Electrónica Industrial

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Este texto incide sobre o papel da supervisão pedagógica no contexto da avaliação de desempenho docente (ADD), procurando aprofundar a forma como a dimensão formativa da avaliação foi equacionada e desenvolvida nas escolas. Para tal, foram realizados dois estudos num agrupamento de escolas da periferia de Lisboa, abrangendo professores avaliadores e avaliados do 1º e do 2º/3º ciclos. Os estudos tinham como objetivos gerais: i) conhecer asconceções de avaliadores e avaliados sobre os fundamentos e as práticas de avaliação de desempenho desenvolvidas nos seus contextos profissionais; ii) e definir o papel que avaliadores e avaliados atribuem à supervisão neste processo. Para a recolha de dados usou-se a entrevista semi-diretiva, recorrendo-se à análise de conteúdo para tratamento dos dados. O confronto dos resultados das entrevistas permite concluir que as conceções sobre a avaliação de desempenho dos docentes dos diferentes ciclos são semelhantes, mas o processo de avaliação e de supervisão foi vivido de forma distinta. O papel da supervisão na ADD depende, em larga escala, da competência dos avaliadores como supervisores e como professores e é facilitado pela existência prévia de uma cultura de colaboração entre docentes.

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Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde

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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

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Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis algorithms. This paper proposes a new mean squared error based approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense. The effectiveness of the proposed method is illustrated using simulated and real hyperspectral images.