Heart rate variability (HRV) analysis has proved to be an important tool for assessing autonomic nervous system. For instance, it has been used during dipyridamole echocardiographic test to differentiate ischemic from nonischemic responses [6]. RR Interval analysis can provide additional information that can lead to early detection of a possible change in the activity of the autonomic nervous system. HRV analysis can be done using Wavelet Transform. This thesis presents a modification of an existing algorithm for extracting the R-R interval from EKG data sets and the use of wavelet transform (WT) technique to compute the timerequency domain energy quantities. The project used data obtained previously from a study of the effects of two pharmacological agents, atropine and propranolol, on laboratory rats. Results showed that the ratio of high frequency energy over the total energy (HF/total) of atropine treated rats was higher than baseline (control).

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There are two critical research needs for the study of hydrocarbon combustion in high speed flows: 1) combustion diagnostics with adequate temporal and spatial resolution, and 2) mathematical techniques that can extract key information from large datasets. The goal of this work is to address these needs, respectively, by the use of high speed and multi-perspective chemiluminescence and advanced mathematical algorithms. To obtain the measurements, this work explored the application of high speed chemiluminescence diagnostics and the use of fiber-based endoscopes (FBEs) for non-intrusive and multi-perspective chemiluminescence imaging up to 20 kHz. Non-intrusive and full-field imaging measurements provide a wealth of information for model validation and design optimization of propulsion systems. However, it is challenging to obtain such measurements due to various implementation difficulties such as optical access, thermal management, and equipment cost. This work therefore explores the application of FBEs for non-intrusive imaging to supersonic propulsion systems. The FBEs used in this work are demonstrated to overcome many of the aforementioned difficulties and provided datasets from multiple angular positions up to 20 kHz in a supersonic combustor. The combustor operated on ethylene fuel at Mach 2 with an inlet stagnation temperature and pressure of approximately 640 degrees Fahrenheit and 70 psia, respectively. The imaging measurements were obtained from eight perspectives simultaneously, providing full-field datasets under such flow conditions for the first time, allowing the possibility of inferring multi-dimensional measurements. Due to the high speed and multi-perspective nature, such new diagnostic capability generates a large volume of data and calls for analysis algorithms that can process the data and extract key physics effectively. To extract the key combustion dynamics from the measurements, three mathematical methods were investigated in this work: Fourier analysis, proper orthogonal decomposition (POD), and wavelet analysis (WA). These algorithms were first demonstrated and tested on imaging measurements obtained from one perspective in a sub-sonic combustor (up to Mach 0.2). The results show that these algorithms are effective in extracting the key physics from large datasets, including the characteristic frequencies of flow—flame interactions especially during transient processes such as lean blow off and ignition. After these relatively simple tests and demonstrations, these algorithms were applied to process the measurements obtained from multi-perspective in the supersonic combustor. compared to past analyses (which have been limited to data obtained from one perspective only), the availability of data at multiple perspective provide further insights into the flame and flow structures in high speed flows. In summary, this work shows that high speed chemiluminescence is a simple yet powerful combustion diagnostic. Especially when combined with FBEs and the analyses algorithms described in this work, such diagnostics provide full-field imaging at high repetition rate in challenging flows. Based on such measurements, a wealth of information can be obtained from proper analysis algorithms, including characteristic frequency, dominating flame modes, and even multi-dimensional flame and flow structures. / Ph. D.

The primary purpose of this study is to evaluate the use of Doppler Lidar sensors for assessing track weakening that would indicate early stages of track instability. Such track weakening could lead to gage widening or track buckling due to rail thermal expansion. A series of tests are performed at the Transportation Technology Center's High Tonnage Loop, where two sections of track are "doctored" to have weaker lateral strength, one on a tangent and another one in a curve. Multiple tests are performed at speeds ranging from 10 – 40 mph, during which the lateral and vertical deflections of the rail are measured under the weight of the passing wheels of a heavily-loaded gondola. The track weakness is created by removing the rail spikes from eight consecutive ties. The measurements from the soft sections are compared with a track section on a tangent that is determined to have nominally sufficient ("good") stiffness. The measurement system consists of four Doppler Lidar units, two oriented toward the rail gage face to measure lateral rail movement, and two directed to the top of the rail to measure vertical rail movement. The combination of the vertical and lateral measurements is used as an indicator of a lack of rail stability if larger-than-normal movement of the rail is detected in either direction. The data collected is analyzed through various methods designed to differentiate sections of track including Gaussian Mixture Model sorting algorithms, inspection via Short Time Fourier Transforms, Discrete Wavelet Transforms, and manual inspection. None of the methods can be done automatically; they each require a different amount of setup and pre-processing before the raw data can be made suitable for the analysis offered by each. The pre-processing can account for dropped data and can be used to identify some false positives such as switches or lubricators. The test results indicate that the system provides a distinctly different measurement in the sections that are doctored to have less track stability than the section with nominally sufficient stiffness. The detection of the loose track in the tangent sections, however, proves to be less reliable. For those, a mostly ad hoc approach is necessary to match the measured data with video images to pinpoint the exact location of the measurements. It is not clear to what extent such approaches would be feasible in practice. Further evaluations of the test data may be used to shed more light on practical analysis methods—possibly wavelets—that are more automated and less ad hoc. They can also provide alternative system setups or designs of experiments for future tests at TTC or on revenue service tracks. / Master of Science / The purpose of this study is to evaluate the effectiveness of a set of Doppler Lidar sensors for their ability to determine the locations of weaker sections of railroad track. These weaker sections could cause damage to the track or passing trains by deforming or buckling under load. A set of tests are performed at the Transportation Technology Center's High Tonnage Loop to evaluate these capabilities. The track had two sections, one of curved track the other of straight track, where the rail was purposefully weakened by removing retaining spikes from the railroad ties. The weakened sections were created by removing the vertical retaining spikes in eight consecutive ties. The tests were conducted at speeds of between 10 to 40 mph, and the sensors measured both the vertical and lateral movement of both rails. The results of these measurements were compared with the unaffected rail. The collected data is analyzed using various data processing techniques. These techniques included using a sorting algorithm to find sections of track with different characteristics as well as inspecting the time and frequency content of the data. None of these methods are automated, and each requires specific setup and adjustment to be effective. The data also needs to be prepared by correcting for any missing or incorrect data points. The tests indicate that the system is able to differentiate between the purposefully weakened track and the rest of the track, however the clearest results of this were for the weakened track in the curve. The straight track results were able to be found with the addition of aligning the video, Lidar, and GPS data sets. It is not clear whether the system could be improved to detect this type of weakness in straight track in practice. Additional testing and evaluation could serve to expand the range of data analysis methods used in differentiating the track conditions and could serve to automate the process. Additionally, alternative test setups could provide further information as to the capabilities of the sensors to detect different types of weakened track.

This thesis studies the application of the wavelet filter bank (WFB) in perceptual audio coding by providing brief overviews of perceptual coding, psychoacoustics, wavelet theory, and existing wavelet coding algorithms. Furthermore, it describes the poor frequency localization property of the WFB and explores one filter design method, in particular, for improving channel separation between the wavelet bands. A wavelet audio coder has also been developed by the author to test the new filters. Preliminary tests indicate that the new filters provide some improvement over other wavelet filters when coding audio signals that are stationary-like and contain only a few harmonic components, and similar results for other types of audio signals that contain many spectral and temporal components. It has been found that the WFB provides a flexible decomposition scheme through the choice of the tree structure and basis filter, but at the cost of poor localization properties. This flexibility can be a benefit in the context of audio coding but the poor localization properties represent a drawback. Determining ways to fully utilize this flexibility, while minimizing the effects of poor time-frequency localization, is an area that is still very much open for research.

Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelet transformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.

The identification of energy scales in the region of Isoscalar Giant Quadrupole Resonance (ISGQR) is motivated by their potential use in understanding how an ordered collective motion transforms into a disordered motion of intrinsic single-particle degrees-of-freedom in many-body quantum systems. High energy-resolution measurements of the ISGQR were obtained by proton inelastic scattering at Ep= 200 MeV using the K600 magnetic Spectrometer at iThemba LABS. The nuclei 58Ni, 90Zr, 120Sn and 208Pb, associated with closed shells, were investigated. Both the Fourier transform and Wavelet analysis were used to extract characteristic energy scales and were later compared with the results from the theoretical microscopic Quasi-particle Phonon Model (QPM), including contributions from collective and non-collective states. The scales found in the experimental data were in good agreement with the QPM. This provides a strong argument that the observed energy scales result from the decay of the collective modes into 2p-2h states. The different scale regions were tested directly by reconstruction of measured energy spectra using the Inverse Fourier Transform and the Continuous Wavelet Transform (CWT), together with a comparison to a previously available reconstruction using the Discrete Wavelet Transform (DWT).

Esta dissertação apresenta o desenvolvimento e avaliação de um método para a segmentação de vasos sangüíneos em imagens de retina, em que se usa a transformada wavelet contínua bidimensional combinada com classificação supervisionada. A segmentação dos vasos é a etapa inicial para a análise automática das imagens, cujo objetivo é auxiliar a comunidade médica na detecção de doenças. Entre outras doenças, as imagens podem revelar sinais da retinopatia diabética, uma das principais causas de cegueira em adultos, que pode ser prevenida se detectada em um diagnóstico precoce. A abordagem apresentada consiste na geração de segmentações pela classificação supervisionada de pixels nas classes \"vaso\" e \"não vaso\". As características usadas para classificação são obtidas através da transformada wavelet contínua bidimensional usando a wavelet de Gabor. Resultados são avaliados nos bancos públicos DRIVE e STARE de imagens coloridas através da análise ROC (\"receiver operating characteristic\", ou característica de operação do receptor). O método atinge áreas sob curvas ROC de 0.9614 e 0.9671 nos bancos DRIVE e STARE, respectivamente, ligeiramente superiores àquelas apresentadas por outros métodos do estado da arte. Apesar de bons resultados ROC, a análise visual revela algumas dificuldades do método, como falsos positivos ao redor do disco óptico e de patologias. A wavelet de Gabor mostra-se eficiente na detecção dos vasos, superando outros filtros lineares. Bons resultados e uma classificação rápida são obtidos usando o classificador bayesiano em que as funções de densidade de probabilidade condicionais às classes são descritas por misturas de gaussianas. A implementação do método está disponível na forma de \"scripts\" código aberto em MATLAB para pesquisadores interessados em detalhes de implementação, avaliação ou desenvolvimento de métodos. / This dissertation presents the development and evaluation of a method for blood vessel segmentation in retinal images which combines the use of the two-dimensional continuous wavelet transform with supervised classification. Segmentation of the retinal vasculature is the first step towards automatic analysis of the images, aiming at helping the medical community in detecting diseases. Among other diseases, the images may reveal signs of diabetic retinopathy, a leading cause of adult blindness, which can be prevented if identified early enough. The presented approach produces segmentations by supervised classification of each image pixel as \"vessel\" or \"nonvessel\", with pixel features derived using the two-dimensional continuous Gabor wavelet transform. Results are evaluated on publicly available DRIVE and STARE color image databases using ROC (receiver operating characteristic) analysis. The method achieves areas under ROC curves of 0.9614 and 0.9671 on the DRIVE and STARE databases, respectively, being slightly superior than that presented by state-of-the-art approaches. Though good ROC results are presented, visual inspection shows some typical difficulties of the method, such as false positives on the borders of the optic disc and pathologies. The Gabor wavelet shows itself efficient for vessel enhancement, outperforming other linear filters. Good segmentation results and a fast classification phase are obtained using the Bayesian classifier with class-conditional probability density functions described as Gaussian mixtures. The method\'s implementation is available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.

A área de monitoração e diagnóstico vem apresentando um acentuado desenvolvimento nos últimos anos com a introdução de novas técnicas de diagnóstico bem como vem contando com a contribuição dos computadores no processamento das informações e das técnicas de diagnósticos. A contribuição da inteligência artificial na automatização do diagnóstico de defeito vem se desenvolvendo continuamente e a crescente automação na indústria vêm de encontro a estas novas técnicas. Na área nuclear, é crescente a preocupação com a segurança nas instalações, e têm sido procuradas técnicas mais eficazes para aumentar o nível de segurança [59]. Algumas usinas nucleares já possuem instaladas, em algumas máquinas, sensores que permitem a verificação de suas condições operacionais. Desta forma, este trabalho também pode colaborar nesta área, ajudando no diagnóstico das condições de operação das máquinas, mais especificamente, no diagnóstico das condições dos rolamentos. O principal objetivo deste trabalho é detectar e classificar os tipos de defeitos apresentados pelos rolamentos analisados e para tal desenvolveu-se uma nova técnica de extração de característica dos sinais de aceleração, baseando-se no Zero Crossing da Transformada de Wavelet contribuindo com o desenvolvimento desta dinâmica área. Como técnica de inteligência artificial foi utilizada a Lógica Paraconsistente Anotada com dois valores (LPA2v), oferecendo a sua contribuição na automação do diagnóstico de defeitos, pois esta lógica pode tratar inclusive de resultados contraditórios que as técnicas de extração de características possam apresentar. Foi desenvolvido um programa de computador onde varias técnicas de extração de características foram utilizadas para realização de diagnóstico das condições de operação dos rolamentos. Este programa foi testado através de dados experimentais obtidas em uma bancada de ensaios para rolamentos onde defeitos previamente conhecidos foram utilizados para avaliar o desempenho das novas técnicas utilizadas. Este trabalho também se concentrou na identificação de defeitos em sua fase inicial procurando utilizar acelerômetros, pois são sensores robustos, de baixo custo e facilmente encontrados na indústria em geral. Os resultados deste trabalho foram obtidos através da utilização de um banco de dados experimental e verificou-se que os resultados de diagnósticos de defeitos mostraramse bons para defeitos em fase inicial. / The monitoring and diagnosis area is presenting an impressive development in recent years with the introduction of new diagnosis techniques as well as with the use the computers in the processing of the information and of the diagnosis techniques. The contribution of the artificial intelligence in the automation of the defect diagnosis is developing continually and the growing automation in the industry meets this new techniques. In the nuclear area, the growing concern with the safety in the facilities requires more effective techniques that have been sought to increase the safety level. Some nuclear power stations have already installed in some machines, sensors that allow the verification of their operational conditions. In this way, the present work can also collaborate in this area, helping in the diagnosis of the operational condition of the machines. This work presents a new technique for characteristic extraction based on the Zero Crossing of Wavelet Transform, contributing with the development of this dynamic area. The technique of artificial intelligence was used in this work the Paraconsistente Logic of Annotation with Two values (LPA2v), contributing with the automation of the diagnosis of defects, because this logic can deal with contradictory results that the techniques of feature extraction can present. This work also concentrated on the identification of defects in its initial phase trying to use accelerometers, because they are robust sensors, of low cost and can be easily found the industry in general. The results obtained in this work were accomplished through the use of an experimental database, and it was observed that the results of diagnoses of defects shown good results for defects in their initial phase.