Generalized Extraction of Real-Time Parameters for Homogeneous Synchronous Dataflow Graphs

23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2015). 4 to 6, Mar, 2015. Turku, Finland.

Response Time Analysis for Fixed-Priority Tasks with Multiple Probabilistic Parameters

Demo presented in 12th Workshop on Models and Algorithms for Planning and Scheduling Problems (MAPSP 2015). 8 to 12, Jun, 2015. La Roche-en-Ardenne, Belgium. Extended abstract.

Equipamentos hospitalares de diagnóstico por imagem, seu estudo, projeto e implementação

Neste trabalho foi desenvolvido um estudo detalhado dos equipamentos de imagiologia médica, que recorrem ao uso de radiação-X, bem como da ressonância magnética. No seguimento deste estudo foram realizadas diversas atividades com equipamentos reais, desde instalações, reparações, manutenções, até ao seu desmantelamento. Este tipo de atividade permitiu ter uma melhor perceção do funcionamento de cada equipamento e o tipo de trabalho que é realizado por um engenheiro eletrotécnico na PHILIPS no sector healthcare. Durante estas atividades foi possível fazer um estudo da qualidade de imagem, em termos de fatores geométricos, em que foi estudada a distorção, a ampliação e a penumbra de uma imagem. Todos estes parâmetros foram alvos de estudo de forma a poder obter imagens com grande qualidade, mas sem que seja comprometida a saúde do doente, devido à elevada exposição de radiação que corpo humano pode absorver. Este estudo tem como intuito perceber como é que a variação de certos parâmetros irá alterar a qualidade da imagem. Desta forma pretende-se perceber como podem ser calibrados os equipamentos de diagnóstico por imagem, para que o técnico de diagnóstico e terapêutica apenas tenha de indicar qual a parte do corpo humano a radiografar, sendo que a máquina se coloca automaticamente nos parâmetros pré-definidos sem qualquer intervenção humana.

A potentiometric magnetic immunoassay for rapid detection of Salmonella typhimurium

Potentiometric detection with homemade polymeric membrane microelectrodes was coupled to a magnetic sandwich immunoassay for Salmonella typhimurium determination. Cadmium and sodium ion selective electrodes were used respectively as indicator and pseudo-reference electrodes and were prepared in pipette tips to allow potentiometric measurements in microliter sample volumes. In the proposed method, the concentration of S. typhimurium was proportional to the amount of cadmium released upon dissolution of a CdS nanoparticle labeled to the secondary detection antibody. The limit of detection was 2 cells per 100 μL. The immunomagnetic assay with potentiometric detection is suitable for sensitive and rapid (average total time per assay of 75 minutes) detection of S. typhimurium in milk samples. The proposed method is easy to perform, safe, sensitive, and low cost and has potential for in situ analysis.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.