Um sistema inteligente de classificação de sinais de EEG para Interface Cérebro-Computador

The Brain-Computer Interfaces (BCI) have as main purpose to establish a communication path with the central nervous system (CNS) independently from the standard pathway (nervous, muscles), aiming to control a device. The main objective of the current research is to develop an off-line BCI that separates the different EEG patterns resulting from strictly mental tasks performed by an experimental subject, comparing the effectiveness of different signal-preprocessing approaches. We also tested different classification approaches: all versus all, one versus one and a hierarchic classification approach. No preprocessing techniques were found able to improve the system performance. Furthermore, the hierarchic approach proved to be capable to produce results above the expected by literature

Análise teórica de uma nova técnica de processamento de sinais interferométricos baseada na modulação triangular da fase óptica

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Algoritmo eficiente para aquisição de sinais de GPS por software

O NAVSTAR/GPS (NAVigation System with Timing And Ranging/Global Po- sitioning System), mais conhecido por GPS, _e um sistema de navegacão baseado em sat_elites desenvolvido pelo departamento de defesa norte-americano em meados de 1970. Criado inicialmente para fins militares, o GPS foi adaptado para o uso civil. Para fazer a localização, o receptor precisa fazer a aquisição de sinais dos satélites visíveis. Essa etapa é de extrema importância, pois é responsável pela detecção dos satélites visíveis, calculando suas respectivas frequências e fases iniciais. Esse processo pode demandar bastante tempo de processamento e precisa ser implementado de forma eficiente. Várias técnicas são utilizadas atualmente, mas a maioria delas colocam em conflito questões de projeto tais como, complexidade computacional, tempo de aquisição e recursos computacionais. Objetivando equilibrar essas questões, foi desenvolvido um método que reduz a complexidade do processo de aquisição utilizando algumas estratégias, a saber, redução do efeito doppler, amostras e tamanho do sinal utilizados, além do paralelismo. Essa estratégia é dividida em dois passos, um grosseiro em todo o espaço de busca e um fino apenas na região identificada previamente pela primeira etapa. Devido a busca grosseira, o limiar do algoritmo convencional não era mais aceitável. Nesse sentido, um novo limiar foi estabelecido baseado na variância dos picos de correlação. Inicialmente, é feita uma busca com pouca precisão comparando a variância dos cinco maiores picos de correlação encontrados. Caso a variância ultrapasse um certo limiar, a região de maior pico torna-se candidata à detecção. Por fim, essa região passa por um refinamento para se ter a certeza de detecção. Os resultados mostram que houve uma redução significativa na complexidade e no tempo de execução, sem que tenha sido necessário utilizar algoritmos muito complexos.

Uso de correntropia na generalização de funções cicloestacionárias e aplicações para a extração de características de sinais modulados

Information extraction is a frequent and relevant problem in digital signal processing. In the past few years, different methods have been utilized for the parameterization of signals and the achievement of efficient descriptors. When the signals possess statistical cyclostationary properties, the Cyclic Autocorrelation Function (CAF) and the Spectral Cyclic Density (SCD) can be used to extract second-order cyclostationary information. However, second-order cyclostationary information is poor in nongaussian signals, as the cyclostationary analysis in this case should comprise higher-order statistical information. This paper proposes a new mathematical tool for the higher-order cyclostationary analysis based on the correntropy function. Specifically, the cyclostationary analysis is revisited focusing on the information theory, while the Cyclic Correntropy Function (CCF) and Cyclic Correntropy Spectral Density (CCSD) are also defined. Besides, it is analytically proven that the CCF contains information regarding second- and higher-order cyclostationary moments, being a generalization of the CAF. The performance of the aforementioned new functions in the extraction of higher-order cyclostationary characteristics is analyzed in a wireless communication system where nongaussian noise exists.

Formalização da tolerância à ausência de dados no processamento de sinais discretos

Dissertação para a obtenção do Grau de Doutor em Engenharia Electrotécnica

Processamento de sinais eletroencefalográficos durante protocolo experimental de teste de interferência de stroop

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Processamento e análise de imagens na caracterização da superfície do titânio submetido a um ensaio de cultura de células

This work aims to develop a methodology for analysis of images using overlapping, which assists in identification of microstructural features in areas of titanium, which may be associated with its biological response. That way, surfaces of titanium heat treated for 08 (eight) different ways have been subjected to a test culture of cells. It was a relationship between the grain, texture and shape of grains of surface of titanium (attacked) trying to relate to the process of proliferation and adhesion. We used an open source software for cell counting adhered to the surface of titanium. The juxtaposition of images before and after cell culture was obtained with the aid of micro-hardness of impressions made on the surface of samples. From this image where there is overlap, it is possible to study a possible relationship between cell growth with microstructural characteristics of the surface of titanium. This methodology was efficient to describe a set of procedures that are useful in the analysis of surfaces of titanium subjected to a culture of cells

Estudo sobre o efeito dos parâmetros de processamento dos pós e sinterização do aço inox 316l reforçado com NbC

This masther dissertation presents a contribution to the study of 316L stainless steel sintering aiming to study their behavior in the milling process and the effect of isotherm temperature on the microstructure and mechanical properties. The 316L stainless steel is a widely used alloy for their high corrosion resistance property. However its application is limited by the low wear resistance consequence of its low hardness. In previous work we analyzed the effect of sintering additives as NbC and TaC. This study aims at deepening the understanding of sintering, analyzing the effect of grinding on particle size and microstructure and the effect of heating rate and soaking time on the sintered microstructure and on their microhardness. Were milled 316L powders with NbC at 1, 5 and 24 hours respectively. Particulates were characterized by SEM and . Cylindrical samples height and diameter of 5.0 mm were compacted at 700 MPa. The sintering conditions were: heating rate 5, 10 and 15◦C/min, temperature 1000, 1100, 1200, 1290 and 1300◦C, and soaking times of 30 and 60min. The cooling rate was maintained at 25◦C/min. All samples were sintered in a vacuum furnace. The sintered microstructure were characterized by optical and electron microscopy as well as density and microhardness. It was observed that the milling process has an influence on sintering, as well as temperature. The major effect was caused by firing temperature, followed by the grinding and heating rate. In this case, the highest rates correspond to higher sintering.

Síntese e caracterização de cerâmicas nanométricas para produção de gás de síntese

In this work, ceramic powders belonging to the system Nd2-xSrxNiO4 (x = 0, 0.4, 0.8, 1.2 and 1.6) were synthesized for their use as catalysts to syngas production partial. It was used a synthesis route, relatively new, which makes use of gelatin as organic precursor. The powders were analyzed at several temperatures in order to obtain the perovskite phase and characterized by several techniques such as thermal analysis, X-rays diffraction, Rietveld refinement method, specific surface area, scanning electron microscopy, energy dispersive spectroscopy of X-rays and temperature programmed reduction. The results obtained using these techniques confirmed the feasibility of the synthesis method employed to obtain nanosized particles. The powders were tested in differential catalytic conditions for dry reforming of methane (DRM) and partial oxidation of methane (POM), then, some systems were chosen for catalytic integrals test for (POM) indicating that the system Nd2-xSrxNiO4 for x = 0, 0.4 and 1.2 calcined at 900 °C exhibit catalytic activity on the investigated experimental conditions in this work without showing signs of deactivation

Estudo do efeito da adição da escória de aciaria no processamento e propriedades finais de telhas cerâmicas

This research presents an overview of the addition steelwork dust of ceramic shingles in order to contribute to the utilization use of such residue. The ceramic industry perspective in the Brazilian State of Piauí is quite promising. Unlike other productive sectors, the ceramic industry uses basically natural raw materials. Its final products are, in short, the result of transforming clay compounds. These raw materials are composed primarily of aluminum oxide, silicon, iron, sodium, magnesium, end calcium, among others. It was verified that steelwork dust is composed primarily of these same oxides, so that its incorporation in to structural ceramics is a very reasonable idea. Both clay and steelwork powder were characterized by AG, XRF, XRD, TGA and DTA. In addition, steelwork dust samples containing (0%, 5%, 10%, 15%, 20% and 25%) were extruded and burned at 800°C, 850°C, 900°C and 950°C. Then t echnological tests of linear shrinkage, water uptake, apparent porosity, apparent density and flexural strengthwere carried at. The results showed the possibility of using steelwork powder in ceramic shingles until 15% significant improvement in physical and mechanical properties. This behavior shows the possibility of burning at temperatures lower than 850ºC, thus promoting a product final cost reduction

Comportamento magnético de compósitos de matriz polimérica com adição de ferritas

Polymer matrix composites offer advantages for many applications due their combination of properties, which includes low density, high specific strength and modulus of elasticity and corrosion resistance. However, the application of non-destructive techniques using magnetic sensors for the evaluation these materials is not possible since the materials are non-magnetizable. Ferrites are materials with excellent magnetic properties, chemical stability and corrosion resistance. Due to these properties, these materials are promising for the development of polymer composites with magnetic properties. In this work, glass fiber / epoxy circular plates were produced with 10 wt% of cobalt or barium ferrite particles. The cobalt ferrite was synthesized by the Pechini method. The commercial barium ferrite was subjected to a milling process to study the effect of particle size on the magnetic properties of the material. The characterization of the ferrites was carried out by x-ray diffraction (XRD), field emission gun scanning electron microscopy (FEG-SEM) and vibrating sample magnetometry (VSM). Circular notches of 1, 5 and 10 mm diameter were introduced in the composite plates using a drill bit for the non-destructive evaluation by the technique of magnetic flux leakage (MFL). The results indicated that the magnetic signals measured in plates with barium ferrite without milling and cobalt ferrite showed good correlation with the presence of notches. The milling process for 12 h and 20 h did not contribute to improve the identification of smaller size notches (1 mm). However, the smaller particle size produced smoother magnetic curves, with fewer discontinuities and improved signal-to-noise ratio. In summary, the results suggest that the proposed approach has great potential for the detection of damage in polymer composites structures

Supressão do ruído de rolamento superficial utilizando a transformada Curvelet

Among the many types of noise observed in seismic land acquisition there is one produced by surface waves called Ground Roll that is a particular type of Rayleigh wave which characteristics are high amplitude, low frequency and low velocity (generating a cone with high dip). Ground roll contaminates the relevant signals and can mask the relevant information, carried by waves scattered in deeper regions of the geological layers. In this thesis, we will present a method that attenuates the ground roll. The technique consists in to decompose the seismogram in a basis of curvelet functions that are localized in time, in frequency, and also, incorporate an angular orientation. These characteristics allow to construct a curvelet filter that takes in consideration the localization of denoise in scales, times and angles in the seismogram. The method was tested with real data and the results were very good

Implementação do algoritmo (RTM) para processamento sísmico em arquiteturas não convencionais

With the growth of energy consumption worldwide, conventional reservoirs, the reservoirs called "easy exploration and production" are not meeting the global energy demand. This has led many researchers to develop projects that will address these needs, companies in the oil sector has invested in techniques that helping in locating and drilling wells. One of the techniques employed in oil exploration process is the reverse time migration (RTM), in English, Reverse Time Migration, which is a method of seismic imaging that produces excellent image of the subsurface. It is algorithm based in calculation on the wave equation. RTM is considered one of the most advanced seismic imaging techniques. The economic value of the oil reserves that require RTM to be localized is very high, this means that the development of these algorithms becomes a competitive differentiator for companies seismic processing. But, it requires great computational power, that it still somehow harms its practical success. The objective of this work is to explore the implementation of this algorithm in unconventional architectures, specifically GPUs using the CUDA by making an analysis of the difficulties in developing the same, as well as the performance of the algorithm in the sequential and parallel version

Sistema inteligente para estimar a porosidade em sedimentos a partir da análise de sinais GPR

This Thesis presents the elaboration of a methodological propose for the development of an intelligent system, able to automatically achieve the effective porosity, in sedimentary layers, from a data bank built with information from the Ground Penetrating Radar GPR. The intelligent system was built to model the relation between the porosity (response variable) and the electromagnetic attribute from the GPR (explicative variables). Using it, the porosity was estimated using the artificial neural network (Multilayer Perceptron MLP) and the multiple linear regression. The data from the response variable and from the explicative variables were achieved in laboratory and in GPR surveys outlined in controlled sites, on site and in laboratory. The proposed intelligent system has the capacity of estimating the porosity from any available data bank, which has the same variables used in this Thesis. The architecture of the neural network used can be modified according to the existing necessity, adapting to the available data bank. The use of the multiple linear regression model allowed the identification and quantification of the influence (level of effect) from each explicative variable in the estimation of the porosity. The proposed methodology can revolutionize the use of the GPR, not only for the imaging of the sedimentary geometry and faces, but mainly for the automatically achievement of the porosity one of the most important parameters for the characterization of reservoir rocks (from petroleum or water)

Classificação de distúrbios na rede elétrica usando redes neurais e wavelets

Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in systems, looking for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances - providing an automatic assessment - techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbance s occurrences in the network. This work presents a methodology based on the Discrete Wavelet Transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks