Laser vibration sensing can be used to classify military targets by its unique vibration signature. A coherent laser radar receives the target´s rapidly oscillating surface vibrations and by using proper demodulation and Doppler technique, stationary, radially moving and even accelerating targets can be taken care of. A frequency demodulation method developed at the former FOA, is for the first time validated against real data with turbulence, scattering, rain etc. The issue is to find a robust and reliable system for target recognition and its performance is therefore compared with some frequency distribution methods. The time frequency distributions have got a crucial drawback, they are affected by interference between the frequency and amplitude modulated multicomponent signals. The system requirements are believed to be fulfilled by combining the FOA method with the new statistical method proposed here, the combination being suggested as aimpoint for future investigations.

It is commonly accepted that the induced noise on DFT phase spectrum has a negligible effect on speech intelligibility for short durations of analysis windows, as the early intelligibility studies pointed out. This fact is confirmed by recent intelligibility studies as well. Based on this phenomenon, classical speech enhancement algorithms do not modify DFT phase spectrum and only make changes in the DFT magnitude spectrum. However, in recent studies it is also indicated that these classical speech enhancement algorithms are not capable of improving the intelligibility scores of noise degraded speech signals. In other words, the contained information in a noise degraded signal cannot be increased by classical enhancement methods. Instead the ease of listening, i.e. quality, can be improved. Hence additional effort can be made to increase the amount of quality improvement using both DFT magnitude and DFT phase. Therefore if the performances of the classical methods are to be improved in terms of speech quality, the effect of DFT phase on speech quality needs to be studied. In this work, the contribution of DFT phase on speech quality is investigated through some simulations using an objective quality assessment criterion. It is concluded from these simulations that, the phase spectrum has a significant effect on speech quality for short durations of analysis windows. Furthermore, phase values of low frequency components are found to have the largest contribution to this quality improvement. Under the motivation of these results, a new enhancement method is proposed which modifies the phase of certain low frequency components as well as the magnitude spectrum. The proposed algorithm is implemented in MATLAB environment. The results indicate that the proposed system improves the performance of the classical methods in terms of speech quality.

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This thesis applies an electromagnetic feature extraction technique to design electromagnetic target classifiers for conductors, dielectrics and dielectric coated conductors using their natural resonance related late-time scattered responses. Classifier databases contain scattered data at only a few aspects for each candidate target. The targets are dielectric spheres of varying sizes and refractive indices, perfectly conducting spheres varying sizes and dielectric coated conducting spheres of varying refractive indices and thickness in coating. The applied classifier design technique is suitable for real-time target classification because of the computational efficiency of feature extraction and decision making approaches. The Wigner-Ville Distribution (WD) is employed in this study in addition to the Principal Components Analysis (PCA) technique to extract target features mainly from late-time target responses. WD is applied to the back-scattered responses at different aspects. To decrease aspect dependency, feature vectors are extracted from selected late-time portions of the WD outputs that include natural resonance related information. Principal components analysis is also used to fuse the feature vectors and/or late-time target responses extracted from reference aspects of a given target into a single characteristic feature vector for each target to further reduce aspect dependency.

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.

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