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Thesis (M.S.)--University of Hawaii at Manoa, 2006. / Includes bibliographical references (leaves 40-42). / viii, 42 leaves, bound ill. 29 cm

Orientador: Renato da Rocha Lopes / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-25T08:10:05Z (GMT). No. of bitstreams: 1 Takahata_AndreKazuo_D.pdf: 10418132 bytes, checksum: 71c4bb92534425059f1397eb4fc919a2 (MD5) Previous issue date: 2014 / Resumo: Neste trabalho consideramos técnicas de processamento de sinais que têm como objetivo aumentar a resolução de imagens da subsuperfície geradas com dados sísmicos. Uma das técnicas consideradas é a deconvolução unidimensional, que tem como finalidade eliminar distorções causadas pelas limitações em banda de frequência da fonte sísmica, bem como pela absorção de componentes e distorções de fase ocorridas durante a propagação da onda sísmica. Nesta tese, analisamos tanto métodos chamados supervisionados, em que estão disponíveis medidas adicionais às medidas sísmicas, que podem guiar o processo de deconvolução, quanto os métodos não supervisionados, em que apenas as medidas sísmicas são consideradas. Em particular, tratamos dos métodos de filtragem de Wiener e mínimos quadrados para os métodos supervisionados. Nos métodos não supervisionados, discutimos as hipóteses para o funcionamento dos métodos envolvendo as estatísticas referentes à refletividade de subsuperfície e do espectro de fase do pulso sísmico. Em particular, analisamos principalmente uso do filtro de erro de predição, que utiliza estatísticas de segunda ordem (SOS) e requer um pulso de fase mínima, e mostramos nossa contribuição sobre um método que utiliza estatísticas de ordem superior (HOS) chamado de "banded independent component analysis" (B-ICA) e que não exige que o pulso seja de fase mínima. Por fim, realizamos um estudo de caso envolvendo dados obtidos em um poço e dados sísmicos com fim de ilustrar nossa análise. Na deconvolução bidimensional são tratadas, além das distorções pela fonte sísmica consideradas na abordagem unidimensional, distorções causadas pela geometria de aquisição de dados e de variações de velocidade de propagação sísmica causadas por complexidades geológicas. Tais distorções podem ser quantificadas em imagens sísmicas obtidas pela técnica de imageamento chamada migração em profundidade pré-empilhamento (PSDM) por meio de uma relação de convolução bidimensional entre a refletividade da subsuperfície e uma função de resolução. Sob hipóteses adequadas, a função de resolução pode ser modelada como uma função de espalhamento pontual (PSF) e a deconvolução bidimensional, portanto, consiste em atenuar o efeito dessas PSFs. Neste trabalho revisamos os aspectos básicos desta modelagem e da estimação das PSFs, bem como do processo de imageamento, e mostramos a nossa contribuição para a deconvolução bidimensional por meio de um método de filtragem inversa / Abstract: In this work, we consider signal processing techniques that aim to improve the resolution of images of the subsurface of the Earth generated from seismic data. One such technique is uni-dimensional deconvolution, which aims to eliminate distortions caused by limitations in the seismic source frequency band, as well as distorting effects caused by frequency components absorption and phase changes during seismic propagation. We analyze both supervised methods, in which reference signals are used in addition to the seismic measurements to determine the decovolution filter, as well as unsupervised methods, in which only the seismic measurements are used. Particularly, we analyze Wiener filtering and least squares methods on the supervised case. As for the unsupervised algorithms, we discuss the hypotheses that underlie these methods, which are based on the statistics of the reflectivity of the subsurface and the phase spectrum of the wavelet pulse. We analyze especially the use of the prediction error filter, which uses second order statistics (SOS) and requires a minimum phase wavelet, and we show our contribution on a method that uses higher order statistics (HOS) called banded independent component analysis (B-ICA), which does not requires that the wavelet be minimum phase. We also present a case study using log data measured in a borehole and seismic data in order to illustrate our analysis. In bidimensional deconvolution, we consider, besides the seismic source distortions considered in the 1D approach, distortions in seismic imaging caused by the acquisition geometry and velocity model complexity associated with the geological structure of the subsurface. These distortions can be quantified in seismic images created through the technique called prestack depth migration (PSDM) using a 2D convolution model between the reflectivity of the subsurface and the so-called resolution function. Under appropriate hypotheses, the resolution function can be seen as a point spread function (PSF). Thus, the objective of 2D deconvolution is to attenuate the effect of these PSFs. In this work, we review the basic aspects of the 2D convolutional model and PSF estimation, as well as the imaging process, and we show our contribution on 2D deconvolution using an inverse filtering approach / Doutorado / Telecomunicações e Telemática / Doutor em Engenharia Elétrica

This study sought to determine whether or not it may be possible to evoke ‘the musical effect' – the emotional response perceived by music listeners – using white noise as a sound-source and real-time digital signal processing techniques. This information was considered to be valuable as in a world driven by technological progress the potential use of new or different technologies in creating music could lead to the development of new methods of – and tools for – composition and performance. More specifically this research asked the question 'what is music?' and investigated how humans – both trained musicians and untrained people – perceive it. The elements of music were investigated for their affective strengths and new fields of research explored for insights into emotion identification in music. Thereafter the focus shifted into the realm of Digital Signal Processing. Common operations and techniques for signal manipulation were investigated and an understanding of the field as a whole was sought. The culmination of these two separate, yet related, investigations was the design and implementation of a listening experiment conducted on adult subjects. They were asked to listen to various manipulated noise-signals and answer a questionnaire with regard to their perceptions of the audio material. The data from the listening experiment suggest that certain DSP techniques can evoke ‘the musical effect’. Various musical elements were represented via digital techniques and in many cases respondents reported perceptions which suggest that some effect was felt. The techniques implemented and musical elements represented were discussed, and possible applications for these techniques, both musical and non-musical, were explored. Areas for further research were discussed and include the implementation of even more DSP techniques, and also into garnering a more specific idea of the emotion perceived by respondents in response to the experiment material.

Dissertation presented in completion for the Masters Diploma in Technology: Electrical Engineering at the M.L Technikon, 1989. / This thesis describes the design work undertaken at the Anglo American Electronics Laboratory to implement an electronic instrumentation system to evaluate the condition of steel wire ropes. / M

M.Ing. (Electrical And Electronic Engineering) / An AC/DC force commutated controlled converter, designed for use in a pulsed laser power supply, is presented here. The converter is controlled with a digital signal processor (DSP). The converter has to supply a stable DC voltage to the load, while drawing sinusoidal currents at unity power factor from the supply grid. The design of such a converter is discussed here, as well as the experimental evaluation of a prototype converter able to supply 30kW to a load.

A range of signal processing techniques have been adopted and developed as a methodology which can be used in developing an intelligent surface electromyography (SEMG) signal classifier. An intelligent SEMG signal classifier would be used for recognising and treatment of musculoskeletal pain and some neurological disorders by physiotherapists and occupational therapists. SEMG signals displays the electrical activity from a skeletal muscle which is detected by placing surface electrodes placed on the skin over the muscle. The key factors of this research were the investigation into digital signal processing using various analysis schemes and the use of the Artificial Neural Network (ANN) for signal classification of normal muscle activity. The analysis schemes explored for the feature extraction of the signals were the Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and Discrete Wavelet Packet Transform (DWPT).Traditional analysis methods such as FFT could not be used alone, because muscle diagnosis requires time-based information. CWT, which was selected as the most suitable for this research, includes time-based information as well as scales, and can be converted into frequencies, making muscle diagnosis easier. CWT produces a scalogram plot along with its corresponding frequency-time based spectrum plot. Using both of these plots, overviewed extracted features of the dominant frequencies and the related scales can be selected for inputs to train and validate an ANN. The purpose of this research is to classify (SEMG) signals for normal muscle activity using different extracted features in an ANN. The extracted features of the SEMG signals used in this research using CWT were the mean and median frequencies of the average power spectrum and the RMS values at scales 8, 16, 32, 64 and 128. SEMG signals were obtained for a 10 second period, sampled at 2048 Hz and digitally filtered using a Butterworth band pass filter (5 to 500 Hz, 4th order). They were collected from normal vastus lateralis and vastus medialis muscles of both legs from 45 male subjects at 25%, 50%, and 75% of their Maximum Voluntary Isometric Contraction (MVIC) force of the quadriceps. The ANN is a computer program which acts like brain neurons, recognises, learns data and produces a model of that data. The model of that data becomes the target output of an ANN. Using the first 35 male subjects' data sets of extracted features, the ANN was trained and then validated with the last 10 male subjects' data sets of the untrained extracted features. The results showed how accurate the untrained data were classified as normal muscle activity. This methodology of using CWT for extracting features for analysing and classifying by an ANN for SEMG signals has shown to be sound and successful for the basis implementation in developing an intelligent SEMG signal classifier.

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