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.

Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression?

In health related research it is common to have multiple outcomes of interest in a single study. These outcomes are often analysed separately, ignoring the correlation between them. One would expect that a multivariate approach would be a more efficient alternative to individual analyses of each outcome. Surprisingly, this is not always the case. In this article we discuss different settings of linear models and compare the multivariate and univariate approaches. We show that for linear regression models, the estimates of the regression parameters associated with covariates that are shared across the outcomes are the same for the multivariate and univariate models while for outcome-specific covariates the multivariate model performs better in terms of efficiency.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Kalman tracking linear predictor for vowel intelligibility enhancement on european portuguese HMM based speech synthesis

The recent developments on Hidden Markov Models (HMM) based speech synthesis showed that this is a promising technology fully capable of competing with other established techniques. However some issues still lack a solution. Several authors report an over-smoothing phenomenon on both time and frequencies which decreases naturalness and sometimes intelligibility. In this work we present a new vowel intelligibility enhancement algorithm that uses a discrete Kalman filter (DKF) for tracking frame based parameters. The inter-frame correlations are modelled by an autoregressive structure which provides an underlying time frame dependency and can improve time-frequency resolution. The system’s performance has been evaluated using objective and subjective tests and the proposed methodology has led to improved results.

Correlação entre a Extensão Linear do Tronco e o Alinhamento Segmentar do Membro Inferior Contralesional na Posição de Pé em Indivíduos Pós-AVE

O controlo postural (CP) do tronco é um pré-requisito para o movimento tendo como objetivo prevenir e minimizar as perturbações. Um CP adequado pressupõe estabilidade e orientação dos segmentos, sendo necessária uma orientação do tronco com componente de extensão linear para as atividades sentado e pé. Num acidente vascular encefálico (AVE), pode existir comprometimento do CP do tronco quer contralesional (CONTRA) como ipsilesional (IPSI). Neurofisiologicamente, parece haver uma projecção predominantemente ipsilateral para garantir um CP do tronco contralateralmente ao movimento, tornando-se importante perceber a correlação entre componentes do CP do tronco e o alinhamento do membro CONTRA. Contudo, atualmente são poucos os estudos direcionados para a o membro inferior (MI). Assim, uma vez que nesta população, assumir a posição de pé e realizar marcha constitui muitas vezes o seu principal objetivo, considerou-se relevante avaliar a correlação entre a extensão linear do tronco e o alinhamento segmentar do MI CONTRA na posição de pé. Participantes e Métodos: Estudo observacional, transversal, analítico com 11 indivíduos (idade média 70+10 anos, 54,5% homens). Como critérios de inclusão definiram-se AVE isquémico da artéria cerebral média, único e unilateral, em fase crónica. Recorrendo a um software de avaliação postural (SAPo) avaliou-se a extensão linear do tronco (alinhamento dos acrómios, alinhamento das espinhas ilíacas ântero-superiores (EIAS), ângulo formado entre os dois e o alinhamento vertical do tronco) e o alinhamento segmentar do MI (ângulo do joelho e ângulo entre o tronco e MI). Para a análise estatística inferencial utilizou-se o coeficiente de correlação de Pearson entre o ângulo dos acrómios e as EIAS, o alinhamento vertical do tronco e o alinhamento horizontal da pélvis IPSI com as variáveis de alinhamento do MI, para uma significância de 0,05. Resultados: Algumas correlações mostraram ser quase nulas (com valores entre -0,02 a 0,02) ou fracas como o alinhamento horizontal da pélvis IPSI com o ângulo do tronco e MI CONTRA (0,29 com p=0,39). O alinhamento do tronco e o ângulo do joelho apresentou moderada correlação (0,642), estatisticamente significativa (p=0,03). Conclusão: Os resultados deste estudo sugerem a existência de uma correlação positiva entre o alinhamento vertical do tronco e a extensão do joelho do MI CONTRA na posição de pé em indivíduos pós-AVE.