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The finite-difference time-domain (FDTD) method is one of most widely used computational electromagnetics (CEM) methods to solve the Maxwell's equations for modern engineering problems. In biomedical applications, like the microwave imaging for early disease detection and treatment, the human tissues are considered as lossy and dispersive materials. The most popular model to describe the material properties of human body is the Debye model. In order to simulate the computational domain as an open region for biomedical applications, the complex-frequency-shifted perfectly matched layers (CFS-PML) are applied to absorb the outgoing waves. The CFS-PML is highly efficient at absorbing the evanescent or very low frequency waves. This thesis investigates the stability of the CFS-PML and presents some conditions to determine the parameters for the one dimensional and two dimensional CFS-PML.The advantages of the FDTD method are the simplicity of implementation and the capability for various applications. However the Courant-Friedrichs-Lewy (CFL) condition limits the temporal size for stable FDTD computations. Due to the CFL condition, the computational efficiency of the FDTD method is constrained by the fine spatial-temporal sampling, especially in the simulations with the electrically small objects or dispersive materials. Instead of modifying the explicit time updating equations and the leapfrog integration of the conventional FDTD method, the spatial filtered FDTD method extends the CFL limit by filtering out the unstable components in the spatial frequency domain. This thesis implements filtered FDTD method with CFS-PML and one-pole Debye medium, then introduces a guidance to optimize the spatial filter for improving the computational speed with desired accuracy.

A computação com DNA é um campo da Bioinformática que, através da manipulação de seqüências de DNA, busca a solução de problemas. Em 1994, o matemático Leonard Adleman, utilizando operações biológicas e manipulação de seqüências de DNA, solucionou uma instância de um problema intratável pela computação convencional, estabelecendo assim, o início da computação com DNA. Desde então, uma série de problemas combinatoriais vem sendo solucionada através deste modelo de programação. Este trabalho analisa a computação com DNA, com o objetivo de traçar algumas linhas básicas para quem deseja programar nesse ambiente. Para isso, são apresentadas algumas vantagens e desvantagens da computação com DNA e, também, alguns de seus métodos de programação encontrados na literatura. Dentre os métodos estudados, o método de filtragem parece ser o mais promissor e, por isso, uma metodologia de programação, através deste método, é estabelecida. Para ilustrar o método de Filtragem Seqüencial, são mostrados alguns exemplos de problemas solucionados a partir deste método. / DNA computing is a field of Bioinformatics that, through the manipulation of DNA sequences, looks for the solution of problems. In 1994 the mathematician Leonard Adleman, using biological operations and DNA sequences manipulation, solved an instance of a problem considered as intractable by the conventional computation, thus establishing the beginning of the DNA computing. Since then, a series of combinatorial problems were solved through this model of programming. This work studies the DNA computing, aiming to present some basic guide lines for those people interested in this field. Advantages and disadvantages of the DNA computing are contrasted and some methods of programming found in literature are presented. Amongst the studied methods, the filtering method appears to be the most promising and for this reason it was chosen to establish a programming methodology. To illustrate the sequential filtering method, some examples of problems solved by this method are shown.

A computação com DNA é um campo da Bioinformática que, através da manipulação de seqüências de DNA, busca a solução de problemas. Em 1994, o matemático Leonard Adleman, utilizando operações biológicas e manipulação de seqüências de DNA, solucionou uma instância de um problema intratável pela computação convencional, estabelecendo assim, o início da computação com DNA. Desde então, uma série de problemas combinatoriais vem sendo solucionada através deste modelo de programação. Este trabalho analisa a computação com DNA, com o objetivo de traçar algumas linhas básicas para quem deseja programar nesse ambiente. Para isso, são apresentadas algumas vantagens e desvantagens da computação com DNA e, também, alguns de seus métodos de programação encontrados na literatura. Dentre os métodos estudados, o método de filtragem parece ser o mais promissor e, por isso, uma metodologia de programação, através deste método, é estabelecida. Para ilustrar o método de Filtragem Seqüencial, são mostrados alguns exemplos de problemas solucionados a partir deste método. / DNA computing is a field of Bioinformatics that, through the manipulation of DNA sequences, looks for the solution of problems. In 1994 the mathematician Leonard Adleman, using biological operations and DNA sequences manipulation, solved an instance of a problem considered as intractable by the conventional computation, thus establishing the beginning of the DNA computing. Since then, a series of combinatorial problems were solved through this model of programming. This work studies the DNA computing, aiming to present some basic guide lines for those people interested in this field. Advantages and disadvantages of the DNA computing are contrasted and some methods of programming found in literature are presented. Amongst the studied methods, the filtering method appears to be the most promising and for this reason it was chosen to establish a programming methodology. To illustrate the sequential filtering method, some examples of problems solved by this method are shown.

A computação com DNA é um campo da Bioinformática que, através da manipulação de seqüências de DNA, busca a solução de problemas. Em 1994, o matemático Leonard Adleman, utilizando operações biológicas e manipulação de seqüências de DNA, solucionou uma instância de um problema intratável pela computação convencional, estabelecendo assim, o início da computação com DNA. Desde então, uma série de problemas combinatoriais vem sendo solucionada através deste modelo de programação. Este trabalho analisa a computação com DNA, com o objetivo de traçar algumas linhas básicas para quem deseja programar nesse ambiente. Para isso, são apresentadas algumas vantagens e desvantagens da computação com DNA e, também, alguns de seus métodos de programação encontrados na literatura. Dentre os métodos estudados, o método de filtragem parece ser o mais promissor e, por isso, uma metodologia de programação, através deste método, é estabelecida. Para ilustrar o método de Filtragem Seqüencial, são mostrados alguns exemplos de problemas solucionados a partir deste método. / DNA computing is a field of Bioinformatics that, through the manipulation of DNA sequences, looks for the solution of problems. In 1994 the mathematician Leonard Adleman, using biological operations and DNA sequences manipulation, solved an instance of a problem considered as intractable by the conventional computation, thus establishing the beginning of the DNA computing. Since then, a series of combinatorial problems were solved through this model of programming. This work studies the DNA computing, aiming to present some basic guide lines for those people interested in this field. Advantages and disadvantages of the DNA computing are contrasted and some methods of programming found in literature are presented. Amongst the studied methods, the filtering method appears to be the most promising and for this reason it was chosen to establish a programming methodology. To illustrate the sequential filtering method, some examples of problems solved by this method are shown.

With the shift toward online shopping, it has become necessary to customize customers' needs and give them more choices. Before making a purchase, buyers research the products' features. The recommender systems facilitate the search task for customers by narrowing down the search space within specific products that align with the customer's needs. A recommender system uses clustering to filter information, calculating the similarity between members of a cluster to determine the factors that will lead to more accurate predictions. We propose a new method for predicting scores in machine learning recommender systems using the Gaussian mixture model clustering and the Pearson correlation coefficient. The proposed method is applied to MovieLens data. The results are then compared to three commonly used methods: Pearson correlation coefficients, K-means, and fuzzy C-means algorithms. As a result of increasing the number of neighbors, our method shows a lower error than others. Additionally, the results depict that accuracy will increase as the number of users increases. Our model, for instance, is 5% more accurate than existing methods when the neighbor size is 30. Gaussian mixture clustering chooses similar users and takes into account the scores distance when choosing nearby users that are similar to the active user.

This work deals with the possibilities of filtering the ECG signal, representing the first part, which is the basis for successful delineation and follow diagnosis of the ECG signal. Filtration in this case is mean to suppress interference from electrical grid, noise of electrical grid. The content of the work is description of filters realized trough wavelet transform and linear filtering as a means to successful filtration of interference. There are method of stationary wavelet transform - dyadic wavelet transform, wavelet packet transform and wavelet wiener filtering method. Linear filtering includes two narrow-band FIR filters. The objective of this work is to propose different methods of wavelet and linear filters in Matlab, filtering of ECG signals and compare the success of filtration methods. ECG signals used in this work are from the CSE database.