Submitted by ANDRÉ LUIS VINAGRE PEREIRA null (andreluisvp@gmail.com) on 2018-03-29T18:17:43Z No. of bitstreams: 1 Dissertação Mestrado Final.pdf: 7001331 bytes, checksum: 858704904256f11c8131d5f17bd44a78 (MD5) / Approved for entry into archive by Cristina Alexandra de Godoy null (cristina@adm.feis.unesp.br) on 2018-04-02T12:53:22Z (GMT) No. of bitstreams: 1 pereira_alv_me_ilha.pdf: 7001331 bytes, checksum: 858704904256f11c8131d5f17bd44a78 (MD5) / Made available in DSpace on 2018-04-02T12:53:22Z (GMT). No. of bitstreams: 1 pereira_alv_me_ilha.pdf: 7001331 bytes, checksum: 858704904256f11c8131d5f17bd44a78 (MD5) Previous issue date: 2018-02-27 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Na manutenção preditiva, as análises dos sinais de vibração e das partículas do óleo são frequentemente utilizadas para o diagnóstico de falhas em redutores, porque elas contêm informações das condições de seus elementos mecânicos. Os sinais de vibração de um redutor geralmente têm muito ruído e a relação sinal-ruído é tão baixa que a extração de informações dos componentes do sinal é muito difícil, especialmente em situações práticas. Uma das soluções para este problema é a aplicação de técnicas de processamento do sinal para facilitar a obtenção de informações. Neste trabalho, uma técnica de cancelamento de ruído, a média temporal síncrona (TSA), e outra técnica da transformada contínua de wavelet de Morlet foram desenvolvidas para extração de recursos e diagnóstico de diferentes tipos de danos locais da engrenagem. Estas técnicas são aplicadas em sinais medidos em uma bancada experimental, que consiste em um par engrenado acoplado a um motor e a um gerador. Outro método para monitorar o estado do sistema é pela análise de partículas presente no óleo provenientes do desgaste das engrenagens. Avaliando a quantidade, formato, tamanho e material das partículas é possível obter informações das condições do equipamento e do tipo de desgaste ocorrido. Neste trabalho, foram feitas a análise do óleo pelas técnicas da ferrografia e contagem de partículas. A parte experimental deste trabalho foi dividida em dois experimentos. No primeiro experimento as condições de um par engrenado durante toda a sua vida útil foi monitorada, enquanto que no segundo experimento, um entalhe foi feito na raiz do dente simulando uma trinca por fadiga. A análise das partículas de óleo mostrou quais tipos de desgastes estava ocorrendo e a técnica da transformada contínua de wavelet mostrou-se precisa na identificação de falhas em dentes de engrenagens, sendo possível indicar em qual dente a falha estava se desenvolvendo. / At the predictive maintenance, the vibration signals analysis and oil particles analysis are frequently used to diagnose failures in a gearbox, because they contain information about the condition of its mechanic’s elements. The vibration signals of a gearbox usually have a lot of noise and the ratio ‘signal-noise’ is very low, making the extraction of information from the signals component very hard, especially in a practical situation. One of the solutions to this problem is the application of technics of signal processing, to improve the collection of information. At this study, a technique of noise cancellation, Temporal Synchronous Average (TSA) and another technique called continuous transform with the Morlet wavelet were executed for the extraction of resources and diagnostics of different type of gears local damages. Those methods are applied to signals measured on an experimental test stand, consisting of a gearbox with an engine and a generator. Another method for monitoring system wear is by analyzing wear particles in the oil generated due to the wear on the gears. By evaluating the quantity, shape, size and material of the particles it is possible to obtain information about the conditions of the equipment and the type of wear that has occurred. During this work, it was done the analysis of the oil by the techniques of ferrography and particle counting. The experimental part of this study was divided into two experiments. On the first experiment was monitored the conditions of a couple meshed throughout its useful life and in the second was made a notch in the root of the tooth simulating a crack by fatigue. The analysis of the oil particles showed what types of wear was occurring and the technique of the continuous wavelet transform was accurate in the identification of defects in gear's teeth, and it was possible to indicate which tooth was failing.

Orientador: Aparecido Carlos Gonçalves / Resumo: Na manutenção preditiva, as análises dos sinais de vibração e das partículas do óleo são frequentemente utilizadas para o diagnóstico de falhas em redutores, porque elas contêm informações das condições de seus elementos mecânicos. Os sinais de vibração de um redutor geralmente têm muito ruído e a relação sinal-ruído é tão baixa que a extração de informações dos componentes do sinal é muito difícil, especialmente em situações práticas. Uma das soluções para este problema é a aplicação de técnicas de processamento do sinal para facilitar a obtenção de informações. Neste trabalho, uma técnica de cancelamento de ruído, a média temporal síncrona (TSA), e outra técnica da transformada contínua de wavelet de Morlet foram desenvolvidas para extração de recursos e diagnóstico de diferentes tipos de danos locais da engrenagem. Estas técnicas são aplicadas em sinais medidos em uma bancada experimental, que consiste em um par engrenado acoplado a um motor e a um gerador. Outro método para monitorar o estado do sistema é pela análise de partículas presente no óleo provenientes do desgaste das engrenagens. Avaliando a quantidade, formato, tamanho e material das partículas é possível obter informações das condições do equipamento e do tipo de desgaste ocorrido. Neste trabalho, foram feitas a análise do óleo pelas técnicas da ferrografia e contagem de partículas. A parte experimental deste trabalho foi dividida em dois experimentos. No primeiro experimento as condições de um par engrenado d... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: At the predictive maintenance, the vibration signals analysis and oil particles analysis are frequently used to diagnose failures in a gearbox, because they contain information about the condition of its mechanic’s elements. The vibration signals of a gearbox usually have a lot of noise and the ratio ‘signal-noise’ is very low, making the extraction of information from the signals component very hard, especially in a practical situation. One of the solutions to this problem is the application of technics of signal processing, to improve the collection of information. At this study, a technique of noise cancellation, Temporal Synchronous Average (TSA) and another technique called continuous transform with the Morlet wavelet were executed for the extraction of resources and diagnostics of different type of gears local damages. Those methods are applied to signals measured on an experimental test stand, consisting of a gearbox with an engine and a generator. Another method for monitoring system wear is by analyzing wear particles in the oil generated due to the wear on the gears. By evaluating the quantity, shape, size and material of the particles it is possible to obtain information about the conditions of the equipment and the type of wear that has occurred. During this work, it was done the analysis of the oil by the techniques of ferrography and particle counting. The experimental part of this study was divided into two experiments. On the first experiment was monitored the... (Complete abstract click electronic access below) / Mestre

The main subject of this work represents the time-frequency signal analysis. Firstly, it intends to provide the most essential theoretical background with focus on the continuous wavelet transform, where also a comparison of the key features with its close relative the short-time Fourier transform is performed. Afterwards, there follows a demonstration of the purpose with a practical example. The particular aim is to create a phase vocoder solution for modifying the length of a sound record duration and pitch shifting. Here, in this place, the functional principles, design, procedure of assembling, outputs and achieved results are well documented.

Accelerometer data can be analyzed using a variety of methods which are effective in the clinical setting. Time-series analysis is used to analyze spatiotemporal variables in various populations. More recently, investigators have focused on gait complexity and the structure of spatiotemporal variations during walking and running. This study evaluated the use of time-series analyses to determine gait parameters during running. Subjects were college-age female soccer players. Accelerometer data were collected using GPS-embedded trunk-mounted accelerometers. Customized Matlab® programs were developed that included Gaussian continuous wavelet transform (CWT) to determine spatiotemporal characteristics, detrended fluctuation analysis (DFA) to examine gait complexity and autocorrelation analyses (ACF) to assess gait regularity. Reliability was examined using repeated running efforts and intraclass correlation. Proof of concept was determined by examining differences in each variable between various running speeds. Applicability was established by examining gait before and after fatiguing activity. The results showed most variables had excellent reliability. Test-retest R2 values for these variables ranged from 0.8 to 1.0. Low reliability was seen in bilateral comparisons of gait symmetry. Increases in running speed resulted in expected changes in spatiotemporal and acceleration variables. Fatiguing exercise had minimal effects on spatiotemporal variables but resulted in noticeable declines in complexity. This investigation shows that GPS-embedded trunk-mounted accelerometers can be effectively used to assess running gait. CWT and DFA yield reliable measures of spatiotemporal characteristics of gait and gait complexity. The effects of running speed and fatigue on these variables provides proof of concepts and applicability for this analytical approach. / Master of Science / Fitness trackers have become widely accessible and easy to use. So much so that athletic teams have been using them to track activity throughout the season. Researchers are able to manipulate data generated from the fitness monitors to assess many different variables including gait. Monitoring gait may generate important information about the condition of the individual. As a person fatigues, running form is theorized to breakdown, which increases injury risk. Therefore the ability to monitor gait may be advantageous in preventing injury. The purpose of this study is to show that the methods in this study are reproducible, respond reasonably to changes in speed, and to observe the changes of gait in the presence of fatigue or on tired legs. Three analyses are used in this study. The first method called autocorrelation, overlays acceleration signals of consecutive foot strikes, and determines the similarity between them. The second method utilizes a wave transformation technique that is able to determine foot contact times. The final method attempts to determine any pattern in the running stride. This method looks for changes in the structure of the pattern. Less structure would indicate a stride that is fatigued. The results showed that the methods of gait analysis used in this study were reproducible and responded appropriately with changes in speed. Small changes in gait were observed due to the presence of fatigue. Further investigation into the use of these methods to determine changes in gait due to the presence of fatigue are warranted.

Dans ce manuscrit, nous développons des méthodologies de détection et caractérisation de sources géomagnétiques et atmosphériques en se basant sur la transformée en ondelettes continues. Les techniques introduites se basent sur les caractéristiques temps-fréquence des ondes observées dans les séries temporelles magnétotelluriques (MT). A partir de ces procédures de détection, nous détaillons l'implémentation d'une stratégie de détermination des fonctions de réponse MT basée sur les statistiques robustes, et du bootstrap hiérarchique pour le calcul des incertitudes. Deux études MT sont également détaillées. La première étude MT concerne la caractérisation de la structure géoélectrique situé sous l'observatoire magnétique de Chambon-La-Forêt, France. La seconde étude concerne des mesures effectuées dans la vallée de Trisuli au Népal en mars 2016. L'objectif de cette campagne est la comparaison avec une étude effectuée en 1996. Nous discutons des effets topographiques sur les sondages MT. Nous présentons également une nouvelle interprétation de la distribution de conductivité dans le sous-sol de vallée de Trisuli. / In this manuscript, we detail the application of continuous wavelet transform to processing schemes for the detection and the characterisation of geomagnetic and atmospheric sources. Presented techniques are based on time-frequency properties of electromagnetic (EM) waves observed in magnetotellurics (MT) time series. We detail the application of these detection procedures in a MT processing scheme. To recover MT response functions, we use robust statistics and a hierarchical bootstrap approach for uncertainties determination. Interpretation of two datasets are also presented. The first MT study deals with the caracterisation of the resistivity distribution below the French National magnetic observatory of Chambon-la-Forêt. The second study details the interpretation of new MT soundings acquired in March 2016 in the Trisuli valley, Nepal. The main objective of this campaign was to compare the new soundings with an old campaign in 1996. We discuss topography effects on MT soundings and their implication on the resistivity distribution. We also introduce a new interpretation of the resistivity distribution in Trisuli valley.

Statistical parametric speech synthesis (SPSS) has seen improvements over recent years, especially in terms of intelligibility. Synthetic speech is often clear and understandable, but it can also be bland and monotonous. Proper generation of natural speech prosody is still a largely unsolved problem. This is relevant especially in the context of expressive audiobook speech synthesis, where speech is expected to be fluid and captivating. In general, prosody can be seen as a layer that is superimposed on the segmental (phone) sequence. Listeners can perceive the same melody or rhythm in different utterances, and the same segmental sequence can be uttered with a different prosodic layer to convey a different message. For this reason, prosody is commonly accepted to be inherently suprasegmental. It is governed by longer units within the utterance (e.g. syllables, words, phrases) and beyond the utterance (e.g. discourse). However, common techniques for the modeling of speech prosody - and speech in general - operate mainly on very short intervals, either at the state or frame level, in both hidden Markov model (HMM) and deep neural network (DNN) based speech synthesis. This thesis presents contributions supporting the claim that stronger representations of suprasegmental variation are essential for the natural generation of fundamental frequency for statistical parametric speech synthesis. We conceptualize the problem by dividing it into three sub-problems: (1) representations of acoustic signals, (2) representations of linguistic contexts, and (3) the mapping of one representation to another. The contributions of this thesis provide novel methods and insights relating to these three sub-problems. In terms of sub-problem 1, we propose a multi-level representation of f0 using the continuous wavelet transform and the discrete cosine transform, as well as a wavelet-based decomposition strategy that is linguistically and perceptually motivated. In terms of sub-problem 2, we investigate additional linguistic features such as text-derived word embeddings and syllable bag-of-phones and we propose a novel method for learning word vector representations based on acoustic counts. Finally, considering sub-problem 3, insights are given regarding hierarchical models such as parallel and cascaded deep neural networks.

3D seismic data are widely used for many different purposes. Despite different objectives, a common goal in almost all 3D seismic programs is to attain better understanding of the subsurface features. In gas injection projects, which are mainly for Enhanced Oil Recovery (EOR) and recently for environmental purposes, seismic data have an important role in the gas monitoring phase. This thesis deals with a 3D seismic investigation at the CO2 injection site at Ketzin, Germany. I focus on two critical aspects of the project: the internal architecture of the heterogeneous Stuttgart reservoir and the detectability of the CO2 response from surface seismic data. Conventional seismic methods are not able to conclusively map the internal reservoir architecture due to their limited seismic resolution. In order to overcome this limitation, I use the Continuous Wavelet Transform (CWT) decomposition technique, which provides frequency spectra with high temporal resolution without the disadvantages of the windowing process associated with the other techniques. Results from applying this technique reveal more of the details of sand bodies within the Stuttgart Formation. The CWT technique also helps to detect and map remnant gas on the top of the structure. In addition to this method, I also show that the pre-stack spectral blueing method, which is presented for the first time in this research, has an ability to enhance seismic resolution with fewer artifacts in comparison with the post-stack spectral blueing method. The second objective of this research is to evaluate the CO2 response on surface seismic data as a feasibility study for CO2 monitoring. I build a rock physics model to estimate changes in elastic properties and seismic velocities caused by injected CO2. Based on this model, I study the seismic responses for different CO2 injection geometries and saturations using one dimensional (1D) elastic modeling and two dimensional (2D) acoustic finite-difference modeling. Results show that, in spite of random and coherent noises and reservoir heterogeneity, the CO2 seismic response should be strong enough to be detectable on surface seismic data. I use a similarity-based image registration method to isolate amplitude changes due to the reservoir from amplitude changes caused by time shifts below the reservoir. In support of seismic monitoring using surface seismic data, I also show that acoustic impedance versus Poisson’s ratio cross-plot is a suitable attribute for distinguishing gas-bearing sands from brine-bearing sands. / CO2SINK Project

The Auditory Brainstem Respose signals are characteristic of the combination of neural activity responses in presence of sound stimuli, detected by the cortex and characterized by peaks and valleys. They are named by roman numerals (I, II, III, IV, V, VI and VII). The identification of these peaks is made by the classic manual process of analysis, which is based on the visualization of the signal generated by the sum of each sample. In the sum the morphological characteristics of the signal and the temporal aspects relevant waves made by Jewett are identified. However, in this visual process some difficulties may occur, regarding the recognition of patterns present, which may vary according to local, individual equipment and settings in the selected protocol. Making the analysis of ABR subject to the influence of many variables and a constant source of doubt about the reliability and agreement between examiners. In order to create a system to automatic detection of these peaks and self-learning, that takes into account the profile for evaluate from examiners this work was developed. The continuous wavelet transforms an innovative technique for the detection of peaks was used associate with a probabilistic model for classification based on the histograms with information provide by examiners. In evaluating of the system, based on the swat rate between the system and a manual technique an accuracy ranging for 74.3% to 99.7%, according to each waves. Thus the proposed technique is proved to be accurate especially in ABR that is a sign of low amplitude. / Os PEATE são sinais resultantes da combinação de respostas de atividades neurais a estímulos sonoros, detectados sobre o córtex, que se caracterizam por vales e picos, sendo nomeados por algarismos romanos (I, II, III, IV, V, VI e VII). O processo clássico de identificação desses picos é baseado na visualização do sinal gerado pela somatória de cada uma de suas componentes. Nele são identificadas as características morfológicas do sinal e os aspectos temporais relevantes constituídos pelas ondas de Jewett. No entanto, neste processo de identificação visual surgem dificuldades que tornam a análise visual dos PEATE uma fonte constante de dúvidas em relação à fidedignidade e concordância entre os examinadores. Com o objetivo de melhorar o processo de avaliação dos PEATE, foi desenvolvido um sistema de detecção automática para os picos, com capacidade de aprendizado que leva em consideração o perfil de marcação realizado por examinadores. Para a detecção de picos foi utilizada a Transformada Wavelet Contínua associado a mesma foi desenvolvido um classificador probabilístico baseado nos histogramas gerados a partir de marcações realizadas pelos profissionais. Na avaliação do sistema proposto, com base na taxa de acerto entre o sistema e a marcação manual, o mesmo apresentou uma acurácia variando de 74,3% a 99,7%, dependendo do tipo de onda analisada. Assim a técnica proposta se revela precisa, principalmente na presença de ruído característico de sinais biológicos, especialmente no PEATE, que é um sinal de amplitude baixa. / Doutor em Ciências

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The peripheral skin microcirculation reflects the overall health status of the cardiovascular system and can be examined non-invasively by laser methods to assess early cardiovascular disease (CVD) risk factors, i.e. oxidative stress and endothelial dysfunction. Examples of methods used for this task are the laser Doppler flowmetry (LDF) and laser fluorescence spectroscopy (LFS), which respectively allow tracing blood flow and the amounts of the coenzyme NAD(P)H (nicotamide adenine dinucleotide) that is involved in the cellular production of ATP (adenosine triphosphate) energy. In this work, these methods were combined with iontophoresis and PORH (post-occlusive reactive hyperaemia) reactive tests to assess skin microvascular function and oxidative stress in mice and human subjects. The main focus of the research was exploring the nonlinear dynamics of skin LDF and NAD(P)H time series by processing the signals with the wavelet transform analysis. The study of nonlinear fluctuations of the microcirculation and cell energy metabolism allows detecting dynamic oscillators reflecting the activity of microvascular factors (i.e. endothelial cells, smooth muscle cells, sympathetic nerves) and specific patterns of mitochondrial or glycolytic ATP production. Monitoring these dynamic factors is powerful for the prediction of general vascular/metabolic health conditions, and can help the study of the mechanisms at the basis of the rhythmic fluctuations of micro-vessels diameter (vasomotion). In this thesis, the microvascular and metabolic dynamic biomarkers were characterised <i>in-vivo</i> in a mouse model affected by oxidative stress and a human cohort of smokers. Data comparison, respectively, with results from control mice and non-smokers, revealed significant differences suggesting the eligibility of these markers as predictors of risk associated with oxidative stress and smoke. Moreover, a relevant link between microvascular and metabolic oscillators was observed during vasomotion induced by α-adrenergic (in mice) or PORH (in humans) stimulations, suggesting a possible role of cellular Ca²⁺oscillations of metabolic origin as drivers of vasomotion which is a theory poorly explored in literature. As future perspective, further exploration of these promising nonlinear biomarkers is required in the presence of risk factors different from smoke or oxidative stress and during vasomotion induced by stimuli different from PORH or α-adrenergic reactive challenges, to obtain a full picture on the use of these factors as predictors of risk and their role in the regulation of vasomotion.