Made available in DSpace on 2014-06-11T19:22:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-11-21Bitstream added on 2014-06-13T20:09:30Z : No. of bitstreams: 1 souza_em_dr_prud.pdf: 3668689 bytes, checksum: aa9f8f193e7b43db7e93ccc3d9a0428b (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / O multicaminho é um dos fenômenos que ocorre quando o sinal proveniente do Global Navigation Satellite System (GNSS) reflete em objetos localizados nas proximidades do levantamento e chega ao receptor via múltiplos caminhos. Geralmente, o receptor GNSS recebe além do sinal direto, também o sinal refletido, o qual é atrasado em relação ao sinal direto. Conseqüentemente, as medidas de pseudodistância (PD) e fase de batimento da onda portadora são rastreadas para um sinal composto, e não para o sinal direto, causando o erro do multicaminho. Esse efeito é uma fonte de erro significativa que ainda permanece como um desafio para a pesquisa, especialmente para o posicionamento relativo estático e cinemático em aplicações de alta precisão. Diferentemente dos demais erros, o multicaminho não é atenuado quando se formam as duplas diferenças (DD) em uma linha de base curta, por ser um efeito altamente dependente do local do levantamento. Pelo contrário, os erros de multicaminho podem aumentar no processo de dupla diferenciação. Nessa pesquisa foi proposta uma metodologia, viável em termos práticos e econômicos, capaz de detectar e corrigir o efeito do multicaminho nas observações de fase da onda portadora e PD L1 e/ou L2 para aplicações estáticas e cinemáticas, quer sejam pós-processadas ou em tempo real. Essa metodologia é baseada na Análise de Multirresolução (AMR) utilizando a Transformada de Wavelets (TW). A TW é aplicada para decompor as séries temporais dos resíduos das DDs do ajustamento em componentes de freqüências baixa e alta... / GNSS-multipath is a phenomenon that occurs when the signal from Global Navigation Satellite System (GNSS) reflects on objects surrounding the survey environment and reaches the receiver antenna through multiple paths. Usually, the GNSS receiver also collects the reflected signal, which is delayed in relation to the direct one. Consequently, the pseudorange (code) and carrier phase measurements are tracked for a composed signal, and not for the direct signal, causing a multipath error. This effect is a significant error source that still remains as a challenge for the research, especially for static and kinematic relative positioning in high-precision applications. Differently from other errors sources, multipath is not attenuated when the double differences (DD) are formed in a short baseline, because this error is highly dependent upon the surrounding environment. On the contrary, multipath errors can even increase in the double differentiation process. In this research a feasible and economic methodology, able of detecting and correcting the multipath effect from the carrier phase and pseudorange L1 and/or L2 for static and kinematic applications, post-processed or in real time. This approach is based on the Multiresolution Analysis (MRA) using the Wavelet Transform (WT)... (Complete abstract click electronic access below)

Multiresolution analysis (MRA) has fairly recently become important, and even essential, to image processing and signal analysis, and is thus having a growing impact on image and signal related areas. As one of the most famous family members of the MRA, the wavelet transform (WT) has demonstrated itself in numerous successful applications in various fields, and become one of the most powerful tools in the fields of image processing and signal analysis. Due to the fact that only the scale information is supplied in WT, the applications using the wavelet transform may be limited when the absolutely-referenced frequency and phase information are required. The Stockwell transform (ST) is a recently proposed multiresolution transform that supplies the absolutely-referenced frequency and phase information. However, the ST redundantly doubles the dimension of the original data set. Because of this redundancy, use of the ST is computationally expensive and even infeasible on some large size data sets. Thus, I propose the use of the discrete orthonormal Stockwell transform (DOST), a non-redundant version of ST. This thesis will continue to implement the theoretical research on the DOST and elaborate on some of our successful applications using the DOST. We uncover the fast calculation mechanism of the DOST using an equivalent matrix form that we discovered. We also highlight applications of the DOST in image compression and image restoration, and analyze the global and local translation properties. The local nature of the DOST suggests that it could be used in many other local applications.

Multiresolution analysis (MRA) has fairly recently become important, and even essential, to image processing and signal analysis, and is thus having a growing impact on image and signal related areas. As one of the most famous family members of the MRA, the wavelet transform (WT) has demonstrated itself in numerous successful applications in various fields, and become one of the most powerful tools in the fields of image processing and signal analysis. Due to the fact that only the scale information is supplied in WT, the applications using the wavelet transform may be limited when the absolutely-referenced frequency and phase information are required. The Stockwell transform (ST) is a recently proposed multiresolution transform that supplies the absolutely-referenced frequency and phase information. However, the ST redundantly doubles the dimension of the original data set. Because of this redundancy, use of the ST is computationally expensive and even infeasible on some large size data sets. Thus, I propose the use of the discrete orthonormal Stockwell transform (DOST), a non-redundant version of ST. This thesis will continue to implement the theoretical research on the DOST and elaborate on some of our successful applications using the DOST. We uncover the fast calculation mechanism of the DOST using an equivalent matrix form that we discovered. We also highlight applications of the DOST in image compression and image restoration, and analyze the global and local translation properties. The local nature of the DOST suggests that it could be used in many other local applications.

Thesis (MSc)--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: The nonlinear LULU smoothers excel at removing impulsive noise from sequences and possess a variety of theoretical properties that make it possible to perform a so-called Discrete Pulse Transform, which is a novel multiresolution analysis technique that decomposes a sequence into resolution levels with a large amount of structure, analogous to a Discrete Wavelet Transform. We explore the use of a one-dimensional Discrete Pulse Transform as the central element in a digital image compressor. We depend crucially on the ability of space-filling scanning orders to map the two-dimensional image data to one dimension, sacrificing as little image structure as possible. Both lossless and lossy image compression are considered, leading to five new image compression schemes that give promising results when compared to state-of-the-art image compressors. / AFRIKAANSE OPSOMMING: Die nielineêre LULU gladstrykers verwyder impulsiewe geraas baie goed uit rye en besit verskeie teoretiese eienskappe wat dit moontlik maak om ’n sogenoemde Diskrete Puls Transform uit te voer; ’n nuwe multiresolusie analise tegniek wat ’n ry opbreek in ’n versameling resolusie vlakke wat ’n groot hoeveelheid struktuur bevat, soortgelyk tot ’n Diskrete Golfie Transform. Ons ondersoek of ’n eendimensionele Diskrete Puls Transform as die sentrale element in ’n digitale beeld kompressor gebruik kan word. Ons is afhanklik van ruimtevullende skandeer ordes om die tweedimensionele beelddata om te skakel na een dimensie, sonder om te veel beeld struktuur te verloor. Vyf nuwe beeld kompressie skemas word bespreek. Hierdie skemas lewer belowende resultate wanneer dit met die beste hedendaagse beeld kompressors vergelyk word.

Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models.

Resumo: O multicaminho é um dos fenômenos que ocorre quando o sinal proveniente do Global Navigation Satellite System (GNSS) reflete em objetos localizados nas proximidades do levantamento e chega ao receptor via múltiplos caminhos. Geralmente, o receptor GNSS recebe além do sinal direto, também o sinal refletido, o qual é atrasado em relação ao sinal direto. Conseqüentemente, as medidas de pseudodistância (PD) e fase de batimento da onda portadora são rastreadas para um sinal composto, e não para o sinal direto, causando o erro do multicaminho. Esse efeito é uma fonte de erro significativa que ainda permanece como um desafio para a pesquisa, especialmente para o posicionamento relativo estático e cinemático em aplicações de alta precisão. Diferentemente dos demais erros, o multicaminho não é atenuado quando se formam as duplas diferenças (DD) em uma linha de base curta, por ser um efeito altamente dependente do local do levantamento. Pelo contrário, os erros de multicaminho podem aumentar no processo de dupla diferenciação. Nessa pesquisa foi proposta uma metodologia, viável em termos práticos e econômicos, capaz de detectar e corrigir o efeito do multicaminho nas observações de fase da onda portadora e PD L1 e/ou L2 para aplicações estáticas e cinemáticas, quer sejam pós-processadas ou em tempo real. Essa metodologia é baseada na Análise de Multirresolução (AMR) utilizando a Transformada de Wavelets (TW). A TW é aplicada para decompor as séries temporais dos resíduos das DDs do ajustamento em componentes de freqüências baixa e alta... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: GNSS-multipath is a phenomenon that occurs when the signal from Global Navigation Satellite System (GNSS) reflects on objects surrounding the survey environment and reaches the receiver antenna through multiple paths. Usually, the GNSS receiver also collects the reflected signal, which is delayed in relation to the direct one. Consequently, the pseudorange (code) and carrier phase measurements are tracked for a composed signal, and not for the direct signal, causing a multipath error. This effect is a significant error source that still remains as a challenge for the research, especially for static and kinematic relative positioning in high-precision applications. Differently from other errors sources, multipath is not attenuated when the double differences (DD) are formed in a short baseline, because this error is highly dependent upon the surrounding environment. On the contrary, multipath errors can even increase in the double differentiation process. In this research a feasible and economic methodology, able of detecting and correcting the multipath effect from the carrier phase and pseudorange L1 and/or L2 for static and kinematic applications, post-processed or in real time. This approach is based on the Multiresolution Analysis (MRA) using the Wavelet Transform (WT)... (Complete abstract click electronic access below) / Orientador: João Francisco Galera Monico / Coorientador: Aylton Pagamisse / Banca: Hélio Magalhães de Oliveira / Banca: Silvio Rogério Correia Freitas / Banca: Messias Meneguette Junior / Banca: Paulo de Oliveira Camargo / Doutor

In this thesis we will explore the theory behind wavelets. The main focus is on the discrete wavelet transform, although to reach this goal we will also introduce the discrete Fourier transform as it allow us to derive important properties related to wavelet theory, such as the multiresolution analysis. Based on the multiresolution it will be shown how the discrete wavelet transform can be formulated and show how it can be expressed in terms of a matrix. In later chapters we will see how the discrete wavelet transform can be generalized into two dimensions, and discover how it can be used in image processing.

This paper considers some numerical schemes for the approximate solution of conservation laws and various wavelet methods are reviewed. This is followed by the construction of wavelet spaces based on a polynomial framework for the approximate solution of conservation laws. Construction of a representation of the approximate solution in terms of an entropy satisfying Multiresolution Analysis (MRA) is defined. Finally, a proof of convergence of the approximate solution of conservation laws using the characterization provided by the basis functions in the MRA will be given.

O presente trabalho visa a utilização da transformada Wavelet no monitoramento do sistema elétrico no que diz respeito a problemas de qualidade da energia com o intuito de detectar, localizar e classificar os mesmos. A transformada Wavelet tem surgido na literatura como uma nova ferramenta para análise de sinais, utilizando funções chamadas Wavelet mãe para mapear sinais em seu domínio, fornecendo informações simultâneas nos domínios tempo e freqüência. A transformada Wavelet é realizada através de filtros decompondo-se um dado sinal em análise multiresolução. Por esta, obtém-se a detecção e a localização de distúrbios relacionados com a qualidade da energia decompondo-se o sinal em dois outros que representam uma versão de detalhes (correspondente as altas freqüências do sinal) e uma versão de aproximação (correspondente as baixas freqüências do sinal). A versão de aproximação é novamente decomposta obtendo-se novos sinais de detalhes e aproximações e assim sucessivamente. Sendo assim, os distúrbios podem ser detectados e localizados no tempo em função do seu conteúdo de freqüência. Estas informações fornecem também características únicas pertinentes a cada distúrbio, permitindo classificá-los. Desta forma, propõe-se neste trabalho o desenvolvimento de um algoritmo classificador automático de distúrbios relacionados com a qualidade da energia baseado unicamente nas decomposições obtidas da análise multiresolução. / The aim of the present dissertation is to apply the Wavelet transform to analyze power quality problems, detecting, localizing and classifying them. The topic Wavelet transform, has appeared in the literature as a new tool for the analysis of signals, using functions called mother Wavelet to map signals in its domain, supplying information in the time and frequency domain, simultaneously. Wavelet transform is accomplished through filters decomposing a provided signal in multiresolution analysis. The detection and localization of disturbances are obtained by decomposing a signal into two other signals that represent, a detailed version (high frequency signals) and a smoothed version (low frequency signals). The smoothed version is decomposed again, and new detailed and smoothed signals are obtained. This process is repeated as many times as necessary and the disturbances can be detected and localized in the time as a function of its level frequency. This information also supplies characteristics to each disturbance, allowing classifying them. Thus, this research presents a way to develop an automatic classifying algorithm of power quality disturbances, based only on multiresolution analysis.

An efficient numerical method for solving Schr&ouml / dinger&#039 / s and Poisson&#039 / s equations using a basis set of cubic B-splines is investigated. The method is applied to find both the wave functions and the corresponding eigenenergies of low-dimensional semiconductor structures. The computational efficiency of the method is explicitly shown by the multiresolution analysis, non-uniform grid construction and imposed boundary conditions by applying it to well-known single electron potentials. The method compares well with the results of analytical solutions and of the finite difference method.