Thesis (M.S.)--State University of New York at Binghamton, Industrial and Systems Engineering Department, 2006. / Includes bibliographical references.

Thesis (Ph.D.)--University of Delaware, 2007. / Principal faculty advisor: Busby N. O. Attoh-Okine, Dept. of Civil & Environmental Engineering. Includes bibliographical references.

No description available.

Hilbert-Huang Transform (HHT) is a data analysis tool, first developed in 1998, which can be used to extract the periodic components embedded within oscillatory data. This thesis is dedicated to the understanding, application, and development of this tool. First, the background theory of HHT will be described and compared with other spectral analysis tools. Then, a number of applications will be presented, which demonstrate the capability for HHT to dissect and analyze the periodic components of different oscillatory data. Finally, a new algorithm is presented which expands HHT ability to analyze discontinuous data. The sum result is the creation of a number of useful tools developed from the application of HHT, as well as an improvement of the HHT tool itself.

Os tubos de Geradores de Vapor em Reatores Nucleares do tipo PWR são submetidos a diferentes níveis de tensões e carregamento em altas temperaturas, reduzindo sua vida útil devido o surgimento de defeitos e corrosão. A inspeção por Correntes Parasitas é um ensaio não destrutivo usado para diagnosticar defeitos de corrosão e descontinuidades na superfície externa e interna em tubos de trocadores de calor. Esses tubos estão sujeitos a danos por diferentes mecanismos de degradação mecânica e química, tais como trincas por fadiga e corrosão sob tensão. Os sinais de inspeção por Correntes Parasitas são afetados por diferentes ruídos dificultando sua análise pelo inspetor. Esse trabalho apresenta os resultados da análise dos sinais de Correntes Parasitas usando a Transformada de Hilbert-Huang (THH) funcionando como filtro de ruídos (De-noising), como uma técnica alternativa de processamento e análise de sinais. A Transformada de Hilbert-Huang teve esse nome atribuído pela agência espacial norte-americana (NASA) para o resultado da reunião de dois processos, um método de decomposição empiricamente modal (Empirical Mode Decomposition EMD), seguido da análise espectral de Hilbert (Hilbert Spectral Analysis HSA). Os sinais de inspeção por correntes parasitas possuem características de transiente, não estacionário e não linear. A transformada de Hilbert-Huang aplicada neste trabalho forneceu dois recursos alternativos em processamento de sinais; o pré-processamento que funcionou como filtro de ruídos, e outro de análise de sinais, responsável pela identificação das características tempo-frequência-energia do sinal. / The nuclear power plant steam generator tubes are subjected to different levels of stress and loading at high temperatures, reducing its lifetime due to the development of defects and corrosion. The Eddy Current Testing (ECT) is a nondestructive testing used to diagnose defects of corrosion and discontinuities in the inner and outer surface of heat exchanger tubes. These tubes are subject to failure by different mechanisms of chemical and mechanical degradation such as fatigue and stress corrosion crack. The ECT signals are affected by different noises making the analysis a difficult task to the inspector. This dissertation presents the results of the main characteristics from the ECT signals using the Hilbert-Huang Transform (HHT) as an alternative method for the processing and signal analysis. The Hilbert-Huang Transform has its name given by the American National Aeronautics and Space Administration, NASA, as the result of Empirical Mode Decomposition (EMD) and the Hilbert Spectral Analysis (HSA) methods. The Eddy Current signals are transient, nonstationary and nonlinear. The Hilbert-Huang Transform applied in this work provided two alternative proceedings in signal processing, one in signal pre-processing acting as noise filter (De-noising) and another as signal analysis, which identifies the characteristics of signal time-frequency-energy.

Os tubos de Geradores de Vapor em Reatores Nucleares do tipo PWR são submetidos a diferentes níveis de tensões e carregamento em altas temperaturas, reduzindo sua vida útil devido o surgimento de defeitos e corrosão. A inspeção por Correntes Parasitas é um ensaio não destrutivo usado para diagnosticar defeitos de corrosão e descontinuidades na superfície externa e interna em tubos de trocadores de calor. Esses tubos estão sujeitos a danos por diferentes mecanismos de degradação mecânica e química, tais como trincas por fadiga e corrosão sob tensão. Os sinais de inspeção por Correntes Parasitas são afetados por diferentes ruídos dificultando sua análise pelo inspetor. Esse trabalho apresenta os resultados da análise dos sinais de Correntes Parasitas usando a Transformada de Hilbert-Huang (THH) funcionando como filtro de ruídos (De-noising), como uma técnica alternativa de processamento e análise de sinais. A Transformada de Hilbert-Huang teve esse nome atribuído pela agência espacial norte-americana (NASA) para o resultado da reunião de dois processos, um método de decomposição empiricamente modal (Empirical Mode Decomposition EMD), seguido da análise espectral de Hilbert (Hilbert Spectral Analysis HSA). Os sinais de inspeção por correntes parasitas possuem características de transiente, não estacionário e não linear. A transformada de Hilbert-Huang aplicada neste trabalho forneceu dois recursos alternativos em processamento de sinais; o pré-processamento que funcionou como filtro de ruídos, e outro de análise de sinais, responsável pela identificação das características tempo-frequência-energia do sinal. / The nuclear power plant steam generator tubes are subjected to different levels of stress and loading at high temperatures, reducing its lifetime due to the development of defects and corrosion. The Eddy Current Testing (ECT) is a nondestructive testing used to diagnose defects of corrosion and discontinuities in the inner and outer surface of heat exchanger tubes. These tubes are subject to failure by different mechanisms of chemical and mechanical degradation such as fatigue and stress corrosion crack. The ECT signals are affected by different noises making the analysis a difficult task to the inspector. This dissertation presents the results of the main characteristics from the ECT signals using the Hilbert-Huang Transform (HHT) as an alternative method for the processing and signal analysis. The Hilbert-Huang Transform has its name given by the American National Aeronautics and Space Administration, NASA, as the result of Empirical Mode Decomposition (EMD) and the Hilbert Spectral Analysis (HSA) methods. The Eddy Current signals are transient, nonstationary and nonlinear. The Hilbert-Huang Transform applied in this work provided two alternative proceedings in signal processing, one in signal pre-processing acting as noise filter (De-noising) and another as signal analysis, which identifies the characteristics of signal time-frequency-energy.

Le rehaussement de la parole est un domaine du traitement du signal qui prend de plus en plus d'ampleur. En effet, dans un monde où la télécommunication connaît un véritable essor, les technologies se doivent d'être de plus en plus performantes afin de satisfaire au plus grand nombre. Les applications qui nécessitent un rehaussement de la parole sont très nombreuses, la plus évidente étant sans doute celle de la téléphonie mobile, où de nombreux bruits environnants peuvent gêner la qualité et l'intelligibilité du signal de parole transmis. Il existe à ce jour de nombreuses techniques de rehaussement de la parole. Celles-ci peuvent d'ores et déjà se décliner en deux catégories distinctes. En effet, certaines techniques utilisent plusieurs microphones et sont qualifiées de multi-capteur, tandis que d'autres techniques n'en utilisent qu'un seul et sont alors qualifiées de mono-capteur.Le présent sujet de recherche se situe dans la catégorie des techniques mono-capteurs qui utilisent principalement les propriétés statistiques de la parole et du bruit afin de réduire au mieux le signal de bruit. La Décomposition Modale Empirique, ou EMD, est une méthode de transformée de signaux qui est apparue récemment et qui suscite de plus en plus l'intérêt des chercheurs en rehaussement de la parole. L'EMD s'avère être une méthode de décomposition de signal très efficace car, contrairement aux transformées plus classiques, l'EMD est une transformée non linéaire et non stationnaire. Ses propriétés statistiques, en réponse au bruit blanc gaussien, ont permis de conclure sur le comportement de cette approche similaire à un banc de filtres quasi-dyadique. Les méthodes existantes de rehaussement de la parole basée sur la modélisation par EMD s'appuient toutes sur ce comportement dans leur démarche de réduction de bruit, et leur efficacité n'est validée que dans le cas de signaux de parole corrompus par du bruit blanc gaussien. Cependant, un algorithme de réduction de bruit n'est intéressant que s'il est efficace sur des bruits environnants de tous les jours. Ces travaux de recherche visent ainsi à déterminer les caractéristiques de l'EMD face à des signaux de parole corrompus par des bruits"réels", avant de comparer ces caractéristiques à ceux issues de signaux de parole corrompus par du bruit blanc gaussien. Les conclusions de cette étude sont finalement mises en pratiques dans le développement d'un système de réduction de bruit qui vise à séparer au mieux le bruit du signal de parole, et ce quel que soit le type de bruit rencontré.

Parkinson’s disease is a clinical syndrome manifesting with slowness and instability. As it is a progressive disease with varying symptoms, repeated assessments are necessary to determine the outcome of treatment changes in the patient. In the recent past, a computer-based method was developed to rate impairment in spiral drawings. The downside of this method is that it cannot separate the bradykinetic and dyskinetic spiral drawings. This work intends to construct the computer method which can overcome this weakness by using the Hilbert-Huang Transform (HHT) of tangential velocity. The work is done under supervised learning, so a target class is used which is acquired from a neurologist using a web interface. After reducing the dimension of HHT features by using PCA, classification is performed. C4.5 classifier is used to perform the classification. Results of the classification are close to random guessing which shows that the computer method is unsuccessful in assessing the cause of drawing impairment in spirals when evaluated against human ratings. One promising reason is that there is no difference between the two classes of spiral drawings. Displaying patients self ratings along with the spirals in the web application is another possible reason for this, as the neurologist may have relied too much on this in his own ratings.

Thesis (Ph. D.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains x, 92 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 75-76).

Magnetic resonance imaging of neuronal activity is one of the most promising techniques in modern psychiatric research. While clear functional links with phenotypic variables have been established and detailed networks of activity robustly identified, fMRI scans have not yet yielded the robust biomarkers of psychiatric diseases, such as schizophrenia, which would allow for their use as a clinical diagnostic tool. One possible explanation for the lack of such results is that neural activity is highly non- stationary, whereas most analysis techniques assume that signal properties remain relatively static over time. Time-frequency analysis is a family of analytic techniques which do not assume that data is stationary, and thus is well suited to the analysis of neural time series. Resting state fMRI scans from a publicly available dataset were decomposed using the Wavelet transform and Hilbert Huang Transform, techniques from time-frequency analysis. The results of these processes were then used as the basis for calculating several properties of the fMRI signal within each voxel. The wavelet transform, a simpler technique, generated measures which showed broad differences between patients with schizophrenia and healthy controls but failed to reach statistical significance in the vast majority of situations. The Hilbert Huang transform, in contrast, showed significant increases in certain measures throughout areas associated with sensory processing, dysfunction in which is a symptom of schizophrenia. These results support the use of analysis techniques able to capture the nonstationarities in neural data and encourages the use of such techniques to explore the nature of the neural differences in psychiatric disorders.