Made available in DSpace on 2014-06-11T19:30:50Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-05-29Bitstream added on 2014-06-13T20:21:16Z : No. of bitstreams: 1 soares_wc_dr_ilha.pdf: 2948445 bytes, checksum: cf47c579c7e9a4f2d231373d9ed5f704 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Neste trabalho é feito um estudo dos métodos de redução de ruído aditivo em sinais de voz baseados em wavelets e, através deste estudo, propõe-se um novo método não-limiar para redução de ruído em sinais de voz no domínio wavelet. Em geral os sinais de voz podem estar contaminados com ruídos artificiais ou reais. O problema consiste que dado um sinal limpo adiciona-se o ruído branco ou colorido, obtendo assim o sinal ruidoso, ambos no domínio do tempo. O que se propõe neste trabalho, é aplicar a transformada wavelet, obtendo assim o sinal transformado no domínio wavelet, reduzindo ou atenuando o ruído sem o uso de limiar. Os métodos mais usados no domínio wavelet são os métodos de redução por limiar, pois permitem bons resultados para sinais contaminados por ruído branco, mas não são eficientes no processamento de sinais contaminados por ruído colorido, que é o tipo de ruído mais comum em situações reais. Nesses métodos, o limiar, geralmente, é calculado nos intervalos de silêncio e aplicado em todo o sinal. Os coeficientes no domínio wavelet são comparados com este limiar e aqueles que estão abaixo deste valor são eliminados ou reduzidos, fazendo assim uma aplicação linear deste limiar. Esta eliminação, na maioria das vezes, causa descontinuidades no tempo e na frequência no sinal processado. Além disso, a forma com que o limiar é calculado pode degradar os trechos de voz do sinal processado, principalmente nos casos em que o limiar depende fortemente da última janela do último trecho de silêncio. O método proposto nesta pesquisa consiste na execução de três processamentos, agindo de acordo com as suas características nas regiões de voz e silêncio, sem o uso de limiar. A execução dos três processamentos é sintetizada numa única função, denominada de função de transferência, que atua como um filtro no processamento do sinal... / In this work a study of the methods for speech noise reduction based on wavelets is done and, through this study, a new non-thresholding method for speech noise reduction in the wavelet domain is proposed. Generally, a speech signal may be corrupted by artificial or real noise. Let a clean signal be corrupted by white or colored noise, rising a noisy signal in time domain. This work proposes the wavelet application to which gives rise to in the wavelet domain. In this domain, noise is reduced or attenuated without a threshold use. After, the signal is recomposed using the inverse discrete wavelet transform. The most used methods in the wavelet domain wavelet are the thresholding reduction methods, because they allow good results for signals corrupted by white noise, but they do not have the same efficiency when processing signals corrupted by colored noise, this is the most common noise in real situations. In those methods, the threshold is usually calculated in the silence intervals and applied to the whole signal. The coefficients in the wavelet domain are compared with this threshold and those that have absolute value below this value are eliminated or reduced, making a linear application of this threshold. This elimination causes discontinuities in time and in the frequency of the processed signal. Besides, the form with that the threshold is applied can degrade the voice segments of the processed signal, principally in cases that the threshold depends strongly on the last window of the last silence segment. The method proposed in this research consists in the execution of three processing, acting according to their characteristics in the voice and silence segments, without the threshold use. The three processing execution is synthesized in an unique function, called transfer function, acting as a filter in the signal processing. This method has as main objective the overcoming... (Complete abstract click electronic access below)

For demanding applications, for example image or video processing, there may be computations that aren’t very suitable for digital signal processors. While a DSP processor is appropriate for some tasks, the instruction set could be extended in order to achieve higher performance for the tasks that such a processor normally isn’t actually design for. The platform used in this project is flexible in the sense that new hardware can be designed to speed up certain computations. This thesis analyzes the computational complex parts of JPEG2000. In order to achieve sufficient performance for JPEG2000, there may be a need for hardware acceleration. First, a JPEG2000 decoder was implemented for a DSP processor in assembler. When the firmware had been written, the cycle consumption of the parts was measured and estimated. From this analysis, the bottlenecks of the system were identified. Furthermore, new processor instructions are proposed that could be implemented for this system. Finally the performance improvements are estimated.

Society is increasingly concerned about the environmental impact of energy systems, and prefers to locate power lines underground. In future, certain socially/environmentally sensitive overhead transmission feeders will need to include underground cable sections. Fault location, especially when using travelling waves, become complicated when the combined transmission line includes a number of discontinuities, such as junction points, teed points and fault points. Consequently, a diverse range of fault locators were developed in this thesis, and the performance of the proposed fault locators investigated. For a combined transmission line (CTL), consisting of one or more overhead line sections and one or more underground cable sections, a hybrid fault location scheme is proposed. This utilises the robustness of an impedance based distance algorithm and the accuracy, but stability concerns, of a travelling wave based fault locator, to determine the faulted section. The distance algorithm can determine the approximate fault location, but if the fault is located near an “underground-overhead” junction point, the accuracy is not sufficient to decide whether the fault is located on the overhead or the underground section. This thesis proposes utilizing a single end travelling wave fault locator to improve the accuracy of the fault location decision. The single end travelling wave fault locator can determine the fault section according to the permutation of the polarity of the “special surges”, which is especially important when the fault is close to a junction point. However, this single end fault locator fails in certain “blind” areas, wand these require the use of a distance relay to help determine fault section. Simulation results demonstrated that this hybrid fault locator can reliably determine which section of the feeder is faulty. For all types of CTL, including teed networks, the multiple-end travelling wave fault locator, utilising the arrival time at the feeder ends of the first fault instigated surges, can estimate the fault location. One of the main features of the proposed fault locator is the classification of the time difference between the arrivals of a fault instigated surge at the feeder ends as standard values, when the fault is located at each of the junction points or teed points. Comparing the time differences measured during an actual fault with these standard values allows the faulted feeder section to be estimated. The simulation results show this multiple-end travelling wave fault locator is highly reliable and suitable for application on combined overhead and underground transmission lines.

Fault location within electric power systems is an important topic that helps reduce outage duration and increases reliability of the system. This paper explores the topic of fault location using traveling waves generated by fault conditions and the discrete wavelet transform used for time-frequency analysis. The single-ended and double-ended traveling wave methods are presented and evaluated on a single circuit and double circuit 500kV system modeled using MATLAB SIMULINK. Results are compared on the basis of wavelet used for analysis, sampling rate, and fault resistance.

Stethoscopes are the most commonly used medical devices for diagnosing heart conditions because they are inexpensive, noninvasive, and light enough to be carried around by a clinician. Auscultation with a stethoscope requires considerable skill and experience, but the introduction of digital stethoscopes allows for the automation of this task. Auscultation waveform segmentation, which is the process of determining the boundaries of heart sound and murmur segments, is the primary challenge in automating the diagnosis of various heart conditions. The purpose of this thesis is to improve the accuracy and efficiency of established techniques for detecting, segmenting, and classifying heart sounds and murmurs in digitized phonocardiogram audio files. Two separate segmentation techniques based on the discrete wavelet transform (DWT) and the simplicity transform are integrated into a MATLAB software system that is capable of automatically detecting and classifying sound segments. The performance of the two segmentation methods for recognizing normal heart sounds and several different heart murmurs is compared by quantifying the results with clinical and technical metrics. The two clinical metrics are the false negative detection rate (FNDR) and the false positive detection rate (FPDR), which count heart cycles rather than sound segments. The wavelet and simplicity methods have a 4% and 9% respective FNDR, so it is unlikely that either method would not detect a heart condition. However, the 22% and 0% respective FPDR signifies that the wavelet method is likely to detect false heart conditions, while the simplicity method is not. The two technical metrics are the true murmur detection rate (TMDR) and the false murmur detection rate (FMDR), which count sound segments rather than heart cycles. Both methods are equally likely to detect true murmurs given their 83% TMDR. However, the 13% and 0% respective FMDR implies that the wavelet method is susceptible to detecting false murmurs, while the simplicity method is not. Simplicity-based segmentation, therefore, demonstrates superior performance to wavelet-based segmentation, as both are equally likely to detect true murmurs, but only the simplicity method has no chance of detecting false murmurs.

This thesis deals with analysis of dynamic forces and vibrations created during cutting with saw. The analysis is done on both simulation and experimental level. Acquired signals are evaluated with signal tools as STFT, CWT and DWT.

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This paper demonstrates the implementation of R-CNN in terms of electromyography-related signals to recognize hand gestures. The signal acquisition is implemented using electrodes situated on the forearm, and the biomedical signals are generated to perform the signals preprocessing using wavelet packet transform to perform the feature extraction. The R-CNN methodology is used to map the specific features that are acquired from the wavelet power spectrum to validate and train how the architecture is framed. Additionally, the real-time test is completed to reach the accuracy of 96.48% compared to the related methods. This kind of result proves that the proposed work has the highest amount of accuracy in recognizing the gestures.

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Despite being the prefered approach for still-image compression for nearly a decade, wavelet-based coding for video has been slow to emerge, due primarily to the fact that the shift variance of the discrete wavelet transform hinders motion estimation and compensation crucial to modern video coders. Recently it has been recognized that a redundant, or overcomplete, wavelet transform is shift invariant and thus permits motion prediction in the wavelet domain. In this dissertation, other uses for the redundancy of overcomplete wavelet transforms in video coding are explored. First, it is demonstrated that the redundant-wavelet domain facilitates the placement of an irregular triangular mesh to video images, thereby exploiting transform redundancy to implement geometries for motion estimation and compensation more general than the traditional block structure widely employed. As the second contribution of this dissertation, a new form of multihypothesis prediction, redundant wavelet multihypothesis, is presented. This new approach to motion estimation and compensation produces motion predictions that are diverse in transform phase to increase prediction accuracy. Finally, it is demonstrated that the proposed redundant-wavelet strategies complement existing advanced video-coding techniques and produce significant performance improvements in a battery of experimental results.