Regional myocardial function in healthy adults: assessment through tissue Doppler echocardiography

OBJECTIVE: To characterize left ventricular regional myocardial function through tissue Doppler echocardiography in healthy adults and to assess the influence of aging in this function. METHODS: In 45 healthy volunteers divided in two groups (< 45 and > 45 years old) we assessed longitudinal and radial regional function (velocities, times intervals and velocity-time integrals). Data were compared in each group and between groups. RESULTS: Systolic function: a) longitudinal: higher velocities and integrals in lateral and inferior walls and in basal segments, with a trend to reduction of these parameters with aging; b) radial: higher basal velocities, no significant change with aging. Diastolic function: a) longitudinal: higher velocities in lateral and inferior walls and in basal segments. With aging e and e/a velocities and integrals decreased, a increased and older individuals showed lower percentage of segments with e/a >1; b) radial: aging was associated with lower e and higher a velocities. CONCLUSION: 1) Tissue Doppler echocardiography detects physiological differences between regional myocardial function of different ventricular segments, in velocities, times intervals and integrals, with physiological heterogeneity and asynchrony; 2) Many of these data are age dependent; 3) Our data contribute to define normal values, and may become useful when compared with data from populations with heart diseases.

Environnement local des défauts structurels et ordre à moyenne portée dans le silicium amorphe

Malgré une vaste littérature concernant les propriétés structurelles, électroniques et ther- modynamiques du silicium amorphe (a-Si), la structure microscopique de ce semi-cond- ucteur covalent échappe jusqu’à ce jour à une description exacte. Plusieurs questions demeurent en suspens, concernant par exemple la façon dont le désordre est distribué à travers la matrice amorphe : uniformément ou au sein de petites régions hautement déformées ? D’autre part, comment ce matériau relaxe-t-il : par des changements homo- gènes augmentant l’ordre à moyenne portée, par l’annihilation de défauts ponctuels ou par une combinaison de ces phénomènes ? Le premier article présenté dans ce mémoire propose une caractérisation des défauts de coordination, en terme de leur arrangement spatial et de leurs énergies de formation. De plus, les corrélations spatiales entre les défauts structurels sont examinées en se ba- sant sur un paramètre qui quantifie la probabilité que deux sites défectueux partagent un lien. Les géométries typiques associées aux atomes sous et sur-coordonnés sont extraites du modèle et décrites en utilisant les distributions partielles d’angles tétraédriques. L’in- fluence de la relaxation induite par le recuit sur les défauts structurels est également analysée. Le second article porte un regard sur la relation entre l’ordre à moyenne portée et la relaxation thermique. De récentes mesures expérimentales montrent que le silicium amorphe préparé par bombardement ionique, lorsque soumis à un recuit, subit des chan- gements structuraux qui laissent une signature dans la fonction de distribution radiale, et cela jusqu’à des distances correspondant à la troisième couche de voisins.[1, 2] Il n’est pas clair si ces changements sont une répercussion d’une augmentation de l’ordre à courte portée, ou s’ils sont réellement la manifestation d’un ordonnement parmi les angles dièdres, et cette section s’appuie sur des simulations numériques d’implantation ionique et de recuit, afin de répondre à cette question. D’autre part, les corrélations entre les angles tétraédriques et dièdres sont analysées à partir du modèle de a-Si.

Uma proposição para o cálculo de mapas de disparidade de imagens estéreo usando um interpolador neural baseado em funções de base radial

This study aims to seek a more viable alternative for the calculation of differences in images of stereo vision, using a factor that reduces heel the amount of points that are considered on the captured image, and a network neural-based radial basis functions to interpolate the results. The objective to be achieved is to produce an approximate picture of disparities using algorithms with low computational cost, unlike the classical algorithms

Desacoplamento de um gerador síncrono através de um controle adaptativo por modelo de referência baseado em funções de Base radial

An alternative nonlinear technique for decoupling and control is presented. This technique is based on a RBF (Radial Basis Functions) neural network and it is applied to the synchronous generator model. The synchronous generator is a coupled system, in other words, a change at one input variable of the system, changes more than one output. The RBF network will perform the decoupling, separating the control of the following outputs variables: the load angle and flux linkage in the field winding. This technique does not require knowledge of the system parameters and, due the nature of radial basis functions, it shows itself stable to parametric uncertainties, disturbances and simpler when it is applied in control. The RBF decoupler is designed in this work for decouple a nonlinear MIMO system with two inputs and two outputs. The weights between hidden and output layer are modified online, using an adaptive law in real time. The adaptive law is developed by Lyapunov s Method. A decoupling adaptive controller uses the errors between system outputs and model outputs, and filtered outputs of the system to produce control signals. The RBF network forces each outputs of generator to behave like reference model. When the RBF approaches adequately control signals, the system decoupling is achieved. A mathematical proof and analysis are showed. Simulations are presented to show the performance and robustness of the RBF network

Treinamento de uma rede neural de base radial usando computação evolutiva: implementação e aplicações

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo de solvatocromismo em líquidos moleculares orgânicos via método seqüencial Monte Carlo/Mecânica Quântica

Utilizou-se o método seqüencial Monte Carlo / Mecânica Quântica para obterem-se os desvios de solvatocromismo e os momentos de dipolo dos sistemas de moléculas orgânicas: Uracil em meio aquoso, -Caroteno em Ácido Oléico, Ácido Ricinoléico em metanol e em Etanol e Ácido Oléico em metanol e em Etanol. As otimizações das geometrias e as distribuições de cargas foram obtidas através da Teoria do Funcional Densidade com o funcional B3LYP e os conjuntos de funções de base 6-31G(d) para todas as moléculas exceto para a água e Uracil, as quais, foram utilizadas o conjunto de funções de base 6-311++G(d,p). No tratamento clássico, Monte Carlo, aplicou-se o algoritmo Metropólis através do programa DICE. A separação de configurações estatisticamente relevantes para os cálculos das propriedades médias foi implementada com a utilização da função de auto-correlação calculada para cada sistema. A função de distribuição radial dos líquidos moleculares foi utilizada para a separação da primeira camada de solvatação, a qual, estabelece a principal interação entre soluto-solvente. As configurações relevantes da primeira camada de solvatação de cada sistema foram submetidas a cálculos quânticos a nível semi-empírico com o método ZINDO/S-CI. Os espectros de absorção foram obtidos para os solutos em fase gasosa e para os sistemas de líquidos moleculares comentados. Os momentos de dipolo elétrico dos mesmos também foram obtidos. Todas as bandas dos espectros de absorção dos sistemas tiveram um desvio para o azul, exceto a segunda banda do sistema de Beta-Caroteno em Ácido Oléico que apresentou um desvio para o vermelho. Os resultados encontrados apresentam-se em excelente concordância com os valores experimentais encontrados na literatura. Todos os sistemas tiveram aumento no momento de dipolo elétrico devido às moléculas dos solventes serem moléculas polares. Os sistemas de ácidos graxos em álcoois apresentaram resultados muito semelhantes, ou seja, os ácidos graxos mencionados possuem comportamentos espectroscópicos semelhantes submetidos aos mesmos solventes. As simulações através do método seqüencial Monte Carlo / Mecânica Quântica estudadas demonstraram que a metodologia é eficaz para a obtenção das propriedades espectroscópicas dos líquidos moleculares analisados.

Algoritmos para mineria de datos con redes de neuronas

Este trabajo propone una serie de algoritmos con el objetivo de extraer información de conjuntos de datos con redes de neuronas. Se estudian dichos algoritmos con redes de neuronas Enhenced Neural Networks (ENN), debido a que esta arquitectura tiene algunas ventajas cuando se aproximan funciones mediante redes neuronales. En la red ENN los pesos de la matriz principal varián con cada patrón, por lo que se comete un error menor en la aproximación. Las redes de neuronas ENN reúnen la información en los pesos de su red auxiliar, se propone un método para obtener información de la red a través de dichos pesos en formas de reglas y asignando un factor de certeza de dichas reglas. La red ENN obtiene un error cuadrático medio menor que el error teórico de una aproximación matemática por ejemplo mediante polinomios de Taylor. Se muestra como una red ENN, entrenada a partir un conjunto de patrones obtenido de una función de variables reales, sus pesos asociados tienen unas relaciones similares a las que se veri_can con las variables independientes con dicha función de variables reales. Las redes de neuronas ENN aproximan polinomios, se extrae conocimiento de un conjunto de datos de forma similar a la regresión estadística, resolviendo de forma más adecuada el problema de multicolionalidad en caso de existir. Las relaciones a partir de los pesos asociados de la matriz de la red auxiliar se obtienen similares a los coeficientes de una regresión para el mismo conjunto numérico. Una red ENN entrenada a partir de un conjunto de datos de una función boolena extrae el conocimiento a partir de los pesos asociados, y la influencia de las variables de la regla lógica de la función booleana, queda reejada en esos pesos asociados a la red auxiliar de la red ENN. Se plantea una red de base radial (RBF) para la clasificación y predicción en problemas forestales y agrícolas, obteniendo mejores resultados que con el modelo de regresión y otros métodos. Los resultados con una red RBF mejoran al método de regresión si existe colinealidad entre los datos que se dispone y no son muy numerosos. También se detecta que variables tienen más importancia en virtud de la variable pronóstico. Obteniendo el error cuadrático medio con redes RBF menor que con otros métodos, en particular que con el modelo de regresión. Abstract A series of algorithms is proposed in this study aiming at the goal of producing information about data groups with a neural network. These algorithms are studied with Enheced Neural Networks (ENN), owing to the fact that this structure shows sever advantages when the functions are approximated by neural networks. Main matrix weights in th ENN vary on each pattern; so, a smaller error is produced when approximating. The neural network ENN joins the weight information contained in their auxiliary network. Thus, a method to obtain information on the network through those weights is proposed by means of rules adding a certainty factor. The net ENN obtains a mean squared error smaller than the theorical one emerging from a mathematical aproximation such as, for example, by means of Taylor's polynomials. This study also shows how in a neural network ENN trained from a set of patterns obtained through a function of real variables, its associated weights have relationships similar to those ones tested by means of the independent variables connected with such functions of real variables. The neural network ENN approximates polynomials through it information about a set of data may be obtained in a similar way than through statistical regression, solving in this way possible problems of multicollinearity in a more suitable way. Relationships emerging from the associated weights in the auxiliary network matrix obtained are similar to the coeficients corresponding to a regression for the same numerical set. A net ENN trained from a boolean function data set obtains its information from its associated weights. The inuence of the variables of the boolean function logical rule are reected on those weights associated to the net auxiliar of the ENN. A radial basis neural networks (RBF) for the classification and prediction of forest and agricultural problems is proposed. This scheme obtains better results than the ones obtained by means of regression and other methods. The outputs with a net RBF better the regression method if the collineality with the available data and their amount is not very large. Detection of which variables are more important basing on the forecast variable can also be achieved, obtaining a mean squared error smaller that the ones obtained through other methods, in special the one produced by the regression pattern.

The n-tuple network: coping with sub-optimality

The subject of this thesis is the n-tuple net.work (RAMnet). The major advantage of RAMnets is their speed and the simplicity with which they can be implemented in parallel hardware. On the other hand, this method is not a universal approximator and the training procedure does not involve the minimisation of a cost function. Hence RAMnets are potentially sub-optimal. It is important to understand the source of this sub-optimality and to develop the analytical tools that allow us to quantify the generalisation cost of using this model for any given data. We view RAMnets as classifiers and function approximators and try to determine how critical their lack of' universality and optimality is. In order to understand better the inherent. restrictions of the model, we review RAMnets showing their relationship to a number of well established general models such as: Associative Memories, Kamerva's Sparse Distributed Memory, Radial Basis Functions, General Regression Networks and Bayesian Classifiers. We then benchmark binary RAMnet. model against 23 other algorithms using real-world data from the StatLog Project. This large scale experimental study indicates that RAMnets are often capable of delivering results which are competitive with those obtained by more sophisticated, computationally expensive rnodels. The Frequency Weighted version is also benchmarked and shown to perform worse than the binary RAMnet for large values of the tuple size n. We demonstrate that the main issues in the Frequency Weighted RAMnets is adequate probability estimation and propose Good-Turing estimates in place of the more commonly used :Maximum Likelihood estimates. Having established the viability of the method numerically, we focus on providillg an analytical framework that allows us to quantify the generalisation cost of RAMnets for a given datasetL. For the classification network we provide a semi-quantitative argument which is based on the notion of Tuple distance. It gives a good indication of whether the network will fail for the given data. A rigorous Bayesian framework with Gaussian process prior assumptions is given for the regression n-tuple net. We show how to calculate the generalisation cost of this net and verify the results numerically for one dimensional noisy interpolation problems. We conclude that the n-tuple method of classification based on memorisation of random features can be a powerful alternative to slower cost driven models. The speed of the method is at the expense of its optimality. RAMnets will fail for certain datasets but the cases when they do so are relatively easy to determine with the analytical tools we provide.

Ellipsoidal harmonic (Lame) MRI shims

Ellipsoidal harmonics are presented as a basis function set for the design of shim coils for magnetic resonance imaging (MRI) or spectroscopy. MR shim coils may be either superconductive or resistive. Ellipsoidal harmonics form an orthogonal set over an ellipsoid and hence are appropriate in circumstances where the imaging or spectroscopic region of a magnet more closely conforms to an ellipsoid rather than a sphere. This is often the case in practice. The Cartesian form of ellipsoidal harmonics is discussed. A method for the design of streamline coil designs is detailed and patterns for third-order ellipsoidal (Lame) shims wound on a cylindrical surface are presented.

Comparação de desempenho entre a formulação direta do Método dos Elementos de Contorno com Funções Radiais e o Método dos Elementos Finitos em problemas de Poisson e Helmholtz

O presente trabalho objetiva avaliar o desempenho do MECID (Método dos Elementos de Contorno com Interpolação Direta) para resolver o termo integral referente à inércia na Equação de Helmholtz e, deste modo, permitir a modelagem do Problema de Autovalor assim como calcular as frequências naturais, comparando-o com os resultados obtidos pelo MEF (Método dos Elementos Finitos), gerado pela Formulação Clássica de Galerkin. Em primeira instância, serão abordados alguns problemas governados pela equação de Poisson, possibilitando iniciar a comparação de desempenho entre os métodos numéricos aqui abordados. Os problemas resolvidos se aplicam em diferentes e importantes áreas da engenharia, como na transmissão de calor, no eletromagnetismo e em problemas elásticos particulares. Em termos numéricos, sabe-se das dificuldades existentes na aproximação precisa de distribuições mais complexas de cargas, fontes ou sorvedouros no interior do domínio para qualquer técnica de contorno. No entanto, este trabalho mostra que, apesar de tais dificuldades, o desempenho do Método dos Elementos de Contorno é superior, tanto no cálculo da variável básica, quanto na sua derivada. Para tanto, são resolvidos problemas bidimensionais referentes a membranas elásticas, esforços em barras devido ao peso próprio e problemas de determinação de frequências naturais em problemas acústicos em domínios fechados, dentre outros apresentados, utilizando malhas com diferentes graus de refinamento, além de elementos lineares com funções de bases radiais para o MECID e funções base de interpolação polinomial de grau (um) para o MEF. São geradas curvas de desempenho através do cálculo do erro médio percentual para cada malha, demonstrando a convergência e a precisão de cada método. Os resultados também são comparados com as soluções analíticas, quando disponíveis, para cada exemplo resolvido neste trabalho.

Dense motion field estimation from myocardial boundary displacements

Minimally invasive cardiovascular interventions guided by multiple imaging modalities are rapidly gaining clinical acceptance for the treatment of several cardiovascular diseases. These images are typically fused with richly detailed pre-operative scans through registration techniques, enhancing the intra-operative clinical data and easing the image-guided procedures. Nonetheless, rigid models have been used to align the different modalities, not taking into account the anatomical variations of the cardiac muscle throughout the cardiac cycle. In the current study, we present a novel strategy to compensate the beat-to-beat physiological adaptation of the myocardium. Hereto, we intend to prove that a complete myocardial motion field can be quickly recovered from the displacement field at the myocardial boundaries, therefore being an efficient strategy to locally deform the cardiac muscle. We address this hypothesis by comparing three different strategies to recover a dense myocardial motion field from a sparse one, namely, a diffusion-based approach, thin-plate splines, and multiquadric radial basis functions. Two experimental setups were used to validate the proposed strategy. First, an in silico validation was carried out on synthetic motion fields obtained from two realistic simulated ultrasound sequences. Then, 45 mid-ventricular 2D sequences of cine magnetic resonance imaging were processed to further evaluate the different approaches. The results showed that accurate boundary tracking combined with dense myocardial recovery via interpolation/ diffusion is a potentially viable solution to speed up dense myocardial motion field estimation and, consequently, to deform/compensate the myocardial wall throughout the cardiac cycle. Copyright © 2015 John Wiley & Sons, Ltd.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

Optimization of structures modeled with a Meshfree approach

Radial basis functions are being used in different scientific areas in order to reproduce the geometrical modeling of an object/structure, as well as to predict its behavior. Due to its characteristics, these functions are well suited for meshfree modeling of physical quantities, which for instances can be associated to the data sets of 3D laser scanning point clouds. In the present work the geometry of a structure is modeled by using multiquadric radial basis functions, and its configuration is further optimized in order to obtain better performances concerning to its static and dynamic behavior. For this purpose the authors consider the particle swarm optimization technique. A set of case studies is presented to illustrate the adequacy of the meshfree model used, as well as its link to particle swarm optimization technique. © 2014 IEEE.

Otimização de placas compósitas laminadas utilizando redes neuronais e enxames de partículas

Trabalho Final de mestrado para obtenção do grau de Mestre em engenharia Mecância

Multiobjective optimization for node adaptation in the analysis of composite plates using a meshless collocation method

The bending of simply supported composite plates is analyzed using a direct collocation meshless numerical method. In order to optimize node distribution the Direct MultiSearch (DMS) for multi-objective optimization method is applied. In addition, the method optimizes the shape parameter in radial basis functions. The optimization algorithm was able to find good solutions for a large variety of nodes distribution.