A presente dissertação tem como objetivo principal estudar a propagação acústica em escoamentos monofásicos. Para tal, são analisados sinais transientes de pressão fornecidos por sensores instalados em posições conhecidas na linha de teste, através do estudo de técnicas de análise de sinais, a fim de investigar se as variações do conteúdo espectral dos sinais são influenciadas pela ocorrência de vazamentos no duto. A análise dos sinais foi realizada nos planos temporal, frequencial, tempo-frequência e estatístico. Os resultados experimentais foram obtidos no oleoduto piloto do NETeF - Núcleo de Engenharia Térmica e Fluidos da USP - Universidade de São Paulo, com uma seção de testes com 1500 metros e diâmetro de 51,2 mm, com escoamento monofásico de água. Os resultados obtidos através da análise tempo-frequência mostraram-se satisfatórios, sendo esta técnica capaz de identificar a composição espectral instantânea de um sinal, ou seja, foi eficiente na identificação de picos de amplitude da frequência ao longo do eixo temporal. Além disso, a análise probabilística, através do desvio-padrão do sinal também mostrou-se eficiente exibindo uma disparidade significativa entre os sinais com e sem vazamento. / The present dissertation reports on the study of the acoustic propagation in single-phase flow. It analyzes the transient signals provided by pressure sensors in known locations in the test line through the study of signal analysis techniques to investigate if the variations in spectral content of the signals are influenced by the occurrence of leaks in the pipe. The analysis of signals was performed in the time, frequency, time-frequency and statistical plans. The experimental results were obtained in a 1500 meter-long and 51.2 millimeter-diameter pilot pipeline at the Center of Thermal Engineering and Fluids, with single-phase flow of water. The results obtained by time-frequency analysis were satisfactory, allowing identifying the spectral composition of an instantaneous signal, i.e., the analysis was effective in identifying the frequency amplitude peaks along the time axis. Moreover, probabilistic analysis using the standard deviation of the signal was also efficient, displaying a significant disparity between the signals with and without leakage.

Neste trabalho simulou-se o escoamento do fluxo de ar em um tubo, com um anteparo de formato triangular com arestas cortantes, posicionado no centro do tubo. O objetivo do estudo é a análise do comportamento do número de Strouhal em função do número de Reynolds. Para isto, foi utilizada a técnica da análise tempo-freqüência, baseada na transformada de Fourier e na transformada de Gabor. Os ensaios foram realizados com o fluxo com velocidades médias de escoamento de 3 a 10 m/s, sendo utilizado um sensor de pressão tipo piezo-resistivo para a detecção da flutuação de pressão ocasionada pelo desprendimento e formação dos vórtices. Os ensaios foram realizados em cinco etapas com o objetivo de se verificar a influência dos seguintes parâmetros na coleta de sinais e no fenômeno: ruído da rede elétrica; influência do anteparo e do ruído proveniente do escoamento do fluxo de ar; número de pontos da amostragem na coleta dos dados; do comprimento da tubulação; e posicionamento do sensor. Pode-se observar, a sensibilidade do sistema de medição através do ensaio realizado sem o anteparo, sendo verificada a influência do ruído do escoamento de ar pelo tubo; pode-se observar também uma pequena interferência do ruído da rede elétrica predominantemente para velocidades abaixo de 3 m/s. Apesar das influências citadas, e utilizando a transformada de Gabor para análise dos sinais, observou-se um sinal mais intenso na freqüência dos vórtices para as velocidades de escoamento, podendo-se comprovar que o número de Strouhal permanece quase constante e é independente do número de Reynolds, devendo-se ressaltar que esta conclusão é valida para números de Reynolds compreendidos na faixa de 3000 a 100000. No experimento obteve-se um fator de sensibilidade (freqüência vórtices/velocidade média) de 8,2 Hz/m/s, e número de Strouhal médio de 0,196. / This work concerns the simulation of an air flux through a pipe with a triangular bluff body positioned inside it. In order to study the behavior of the Strouhal number in function of the Reynolds number. For this, the time-frequency analysis technique was used, based on Fourier transform and the Gabor transform. The experiments were carried out with an air flux velocity ranging from 3 to 10 m/s and using a piezoresistive pressure sensor to detect pressure fluctuations caused by the shedding and vortex formation. The experimental procedures were divided in five stages to make it possible to verify the influence of the following parameters in the signal data acquisition: electric network noise, the bluff body presence and the noise generated due to its presence, number of sampling data points, tubing length and sensor positioning. The sensitivity of the experiment could be observed testing the air flowing with no bluff body inside the pipe. Thus, it was possible to investigate the influence of the noise generated due to this flux limiting body. It could be also observed, mainly at 3 m/s or less, the noise generated due to the electric network. Despite the listed influences, and with the use of the Gabor transform, a more intense signal on the vortex frequency for the flow velocity was observed, showing that the Strouhal number remains almost constant and is independent of the Reynolds number. It is important to recall that this conclusion is valid for Reynolds numbers between 3000 and 100000. In the experiments the factor of sensitivity (vortex frequency/mean velocity) obtained was 8,2 Hz/m/s and the mean Strouhal number 0,196.

Cette thèse porte sur l'utilisation de la magnétoencéphalographie (MEG) comme outil d'étude de la dynamique des réponses corticales durant la production de mots. Les données empiriques accumulées dans la littérature sont évaluées au regard des modèles psycho- et neuro-linguistiques de la production des mots et du langage. Nous réalisons une exploration de l'évolution des modèles psycho-linguistiques et effectuons en ce sens une revue de la littérature MEG. Les forces et limites de la technique et des données empiriques existantes sont considérées et utilisées pour établir un protocole de dénomination d'images qui soit compatible avec la MEG. Nous développons ensuite une étude empirique réalisée en MEG, en utilisant une manipulation visuo-sémantique pour explorer la dynamique des réponses corticales. Cette étude démontre une large réponse bi-hémisphérique avec des différences inter-conditions précoces (~100ms) dans la BA8 et des différences dans le gyrus cingulaire antérieur, le cortex médial temporal antérieur droit à 207ms et dans la jonction temporo-pariétale à 233ms après apparition du stimulus. Des différences entre les conditions apparaissent tardivement dans le cuneus droit et suggèrent également un traitement visuel en cours. Nos résultats questionnent le timing estimé pour les traitements phonologiques et sémantiques suggérés par les modèles sériels actuels de production du langage. A la lumière de la revue de la littérature et de l'étude empirique conduite, nous évaluons les modèles existants et discutons des directions potentielles pour les recherches futures. / This thesis concerns the use of magnetoencephalography [MEG] as a tool for investigating the dynamics of the cortical response during word production. The evidence gathered is considered in the context of existing psycho- and neuro-linguistic models of word and speech production. An exploration of the evolution of psycholinguistic models is performed, motivating a review of the MEG literature. The strengths and limitations of the technique and existing evidence are considered, and used to guide the design of a picture naming protocol compatible with MEG. An empirical MEG study is then developed and implemented using a visuo-semantic manipulation to explore the dynamics of the cortical response. This study demonstrates a broad, bi-hemispheric response with early (~100ms) between-conditions differences in bilateral BA8 and anterior cingulate cortex, in right anterior medial temporal cortex at 207ms, and a difference in right temporo-parietal junction at 233ms post stimulus. Late between conditions differences in the right cuneus also suggest ongoing visual processing. Our findings question the timing estimated for semantic and phonological processing suggested by current serial models of speech processing. In the light of the review and empirical study, a contextual evaluation of existing models is performed and potential future avenues of investigation are discussed.

This thesis deals with the topic of high resolution spectral analysis. In the first part, selected methods are presented and afterwards compared based on the Matlab implementations. The problematics of reduction of crossterms in quadratic time–frequency distributions is also covered. The second part is focused on the implementation and optimization of the algorithm for real-time computation of smoothed Wigner distribution function.

This work deals with the metods frequency and time frequency analysis of stabilometric signal. In the introroduction is described theory about posturography and posturographic measurment. The work contains describtion of stabilometric parametrs in time domain (1D and 2D parametrs) and in frequency domain. The aim is create review of basic metods used to processing and preprocessing of stabilometric signals and comparing this methods . In work is realized ferquency analysis used Frourier transfrmation and Burg method and time-frequency analysis used Short time Frourier transformation and Wavelet transformation. One part of program is aimed on comparison of this methods.

The Human brain consists of mutually connected neuronal populations that build anatomically and functionally separated structures. To understand human brain activity and connectivity, it is crucial to describe how these structures are connected and how information is spread. Commonly used methods often work with data from scalp EEG, with a limited number of contacts, and are incapable of observing dynamic changes during cognitive processes or different behavioural states. In addition, connectivity studies almost never analyse pathological parts of the brain, which can have a crucial impact on pathology research and treatment. The aim of this work is connectivity analysis and its evolution in time during cognitive tasks using data from intracranial EEG. Physiological processes in cognitive stimulation and the local connectivity of pathology in the epileptic brain during wake and sleep were analysed. The results provide new insight into human brain physiology research. This was achieved by an innovative approach which combines connectivity methods with EEG spectral power calculation. The second part of this work focuses on seizure onset zone (SOZ) connectivity in the epileptic brain. The results describe the functional isolation of the SOZ from the surrounding tissue, which may contribute to clinical research and epilepsy treatment.

abstract: The marked increase in the inflow of remotely sensed data from satellites have trans- formed the Earth and Space Sciences to a data rich domain creating a rich repository for domain experts to analyze. These observations shed light on a diverse array of disciplines ranging from monitoring Earth system components to planetary explo- ration by highlighting the expected trend and patterns in the data. However, the complexity of these patterns from local to global scales, coupled with the volume of this ever-growing repository necessitates advanced techniques to sequentially process the datasets to determine the underlying trends. Such techniques essentially model the observations to learn characteristic parameters of data-generating processes and highlight anomalous planetary surface observations to help domain scientists for making informed decisions. The primary challenge in defining such models arises due to the spatio-temporal variability of these processes. This dissertation introduces models of multispectral satellite observations that sequentially learn the expected trend from the data by extracting salient features of planetary surface observations. The main objectives are to learn the temporal variability for modeling dynamic processes and to build representations of features of interest that is learned over the lifespan of an instrument. The estimated model parameters are then exploited in detecting anomalies due to changes in land surface reflectance as well as novelties in planetary surface landforms. A model switching approach is proposed that allows the selection of the best matched representation given the observations that is designed to account for rate of time-variability in land surface. The estimated parameters are exploited to design a change detector, analyze the separability of change events, and form an expert-guided representation of planetary landforms for prioritizing the retrieval of scientifically relevant observations with both onboard and post-downlink applications. / Dissertation/Thesis / Doctoral Dissertation Computer Engineering 2019

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19273号 / 医博第4037号 / 新制||医||1011(附属図書館) / 32275 / 京都大学大学院医学研究科医学専攻 / (主査)教授 渡邉 大, 教授 村井 俊哉, 教授 高橋 淳 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Nonstationary signals are widely observed in many real-world applications, e.g., radar, sonar, radio astronomy, communication, acoustics, and vibration applications. Joint time-frequency (TF) domain representations provide a time-varying spectrum for their analyses, discrimination, and classifications. Nonstationary signals commonly exhibit sparse occupancy in the TF domain. In this dissertation, we incorporate such sparsity to enable robust TF analysis in impaired observing environments. In practice, missing data samples frequently occur during signal reception due to various reasons, e.g., propagation fading, measurement obstruction, removal of impulsive noise or narrowband interference, and intentional undersampling. Missing data samples in the time domain lend themselves to be missing entries in the instantaneous autocorrelation function (IAF) and induce artifacts in the TF representation (TFR). Compared to random missing samples, a more realistic and more challenging problem is the existence of burst missing data samples. Unlike the effects of random missing samples, which cause the artifacts to be uniformly spread over the entire TF domain, the artifacts due to burst missing samples are highly localized around the true instantaneous frequencies, rendering extremely challenging TF analyses for which many existing methods become ineffective. In this dissertation, our objective is to develop novel signal processing techniques that offer effective TF analysis capability in the presence of burst missing samples. We propose two mutually related methods that recover missing entries in the IAF and reconstruct high-fidelity TFRs, which approach full-data results with negligible performance loss. In the first method, an IAF slice corresponding to the time or lag is converted to a Hankel matrix, and its missing entries are recovered via atomic norm minimization. The second method generalizes this approach to reduce the effects of TF crossterms. It considers an IAF patch, which is reformulated as a low-rank block Hankel matrix, and the annihilating filter-based approach is used to interpolate the IAF and recover the missing entries. Both methods are insensitive to signal magnitude differences. Furthermore, we develop a novel machine learning-based approach that offers crossterm-free TFRs with effective autoterm preservation. The superiority and usefulness of the proposed methods are demonstrated using simulated and real-world signals. / Electrical and Computer Engineering

Non-stationary signals, particularly frequency modulated (FM) signals which arecharacterized by their time-varying instantaneous frequencies (IFs), are fundamental to radar, sonar, radio astronomy, biomedical applications, image processing, speech processing, and wireless communications. Time-frequency (TF) analyses of such signals provide two-dimensional mapping of time-domain signals, and thus are regarded as the most preferred technique for detection, parameter estimation, analysis and utilization of such signals. In practice, these signals are often received with compressed measurements as a result of either missing samples, irregular samplings, or intentional under-sampling of the signals. These compressed measurements induce undesired noise-like artifacts in the TF representations (TFRs) of such signals. Compared to random missing data, burst missing samples present a more realistic, yet a more challenging, scenario for signal detection and parameter estimation through robust TFRs. In this dissertation, we investigated the effects of burst missing samples on different joint-variable domain representations in detail. Conventional TFRs are not designed to deal with such compressed observations. On the other hand, sparsity of such non-stationary signals in the TF domain facilitates utilization of sparse reconstruction-based methods. The limitations of conventional TF approaches and the sparsity of non-stationary signals in TF domain motivated us to develop effective TF analysis techniques that enable improved IF estimation of such signals with high resolution, mitigate undesired effects of cross terms and artifacts and achieve highly concentrated robust TFRs, which is the goal of this dissertation. In this dissertation, we developed several TF analysis techniques that achieved the aforementioned objectives. The developed methods are mainly classified into two three broad categories: iterative missing data recovery, adaptive local filtering based TF approach, and signal stationarization-based approaches. In the first category, we recovered the missing data in the instantaneous auto-correlation function (IAF) domain in conjunction with signal-adaptive TF kernels that are adopted to mitigate undesired cross-terms and preserve desired auto-terms. In these approaches, we took advantage of the fact that such non-stationary signals become stationary in the IAF domain at each time instant. In the second category, we developed a novel adaptive local filtering-based TF approach that involves local peak detection and filtering of TFRs within a window of a specified length at each time instant. The threshold for each local TF segment is adapted based on the local maximum values of the signal within that segment. This approach offers low-complexity, and is particularly useful for multi-component signals with distinct amplitude levels. Finally, we developed knowledge-based TFRs based on signal stationarization and demonstrated the effectiveness of the proposed TF techniques in high-resolution Doppler analysis of multipath over-the-horizon radar (OTHR) signals. This is an effective technique that enables improved target parameter estimation in OTHR operations. However, due to high proximity of these Doppler signatures in TF domain, their separation poses a challenging problem. By utilizing signal self-stationarization and ensuring IF continuity, the developed approaches show excellent performance to handle multiple signal components with variations in their amplitude levels. / Electrical and Computer Engineering