In this thesis, a three-word and four-word Mandarin phrases speech recognition system is developed. This system contains two recordings of twenty-four thousand three-word phrases and twenty-two thousand four-word phrases in the database. And it applies MFCC, mono-syllable HMM¡¦s and speech-text alignment scheme to select the initial phrase candidates. A wavelet transform based vowel segmentation technique and a Mandarin pitch identification method is then followed to increase the phrase correct identification rate and obtain the final answer. Experimental results indicate that 92% and 96% correct rates can be achieved for three-word and four-word phrases recognition problems respectively, under the conditions that the first recording of this database is used for training and the second one is for testing. For the speaker-dependent case, the correct phrase can be found within 1 second, using a PC with Intel Celeron 2.4 GHz CPU and RedHat Linux 9.0 Operation System.

The partial discharges (PD) measurement approach in power equipments is a very important inspection technique for insulation deterioration assessment. The PD based approach possesses the greatest potential for further development. This study proposes a noncontact type acoustic measurement system. We first investigate an acoustic measurement method in the laboratory. To prove the accuracy of the acoustic measurements, we proceed with, in the laboratory, signal-pattern comparison between the acoustic measurement method and the pulse current method. This study creates polar-coordinate and discharge type identification patterns. We propose the use of the q-£p-t patterns, the polar-coordinate patterns and discharge type identification patterns, with mutual cross-reference, to identify the discharge type. Then this study applies the wavelet transform to suppress noises; a wavelet mother function most similar to the acoustic PD signals is chosen and then set the filtering threshold value for the wavelet transform. The signals' features will be extracted after the noises are eliminated. The experimental results indicate that the application of wavelet transform can effectively eliminate the field noises. Next, the features will be used to build the training database for the back-propagation neural network (BNN) to construct the discharge patterns' recognition and identification system. Finally, we apply the finished neural networks to field signal-pattern identification. The proposed acoustic measurement system is applied on line to epoxy-resin transformers, power distributors, and the like. The superior measurement results we obtained shall be able to correctly identify power equipment's PD fault types.

The possibility and feasibility of experimental detection of localized defects in tubes using laser-induced ultrasonic wave approach through Thermo Acousto Photonic Non Destructive Evaluation (TAP-NDE) and Signal processing through wavelet transform is examined in this research. Guided waves in cylindrical surfaces provide solutions for detection of different defects in the material. Several experiments were conducted to this respect. Wave propagation in both axial and circumferential directions was studied. The dispersive wave propagation of ultrasonic waves in hollow cylinders has been investigated experimentally, primarily for use in non-contact and nondestructive inspections of pipes and tubes. The laser ultrasonic waves propagated in cylindrical waveguides are particularly attractive because of their unique characteristics in the applications of nondestructive evaluation (NDE). Contrary to studies making use of only axially symmetric guided waves in hollow cylinders, here are analyzed also nonaxisymmetric waves. The analysis of data is made by using the Gabor wavelet transform. The capability of modeling the guided wave dispersion in hollow cylinders is used in developing guided wave experimental techniques for flaw detection. Good agreement was obtained when comparing the dispersion spectra between theory and experimentation. Measurement of group velocities of guided waves, which are obtained directly from the wavelet transform coefficients, can be used to determine allocation and sizing of flaws.

This dissertation addresses three different problems in power systems. The first problem is related to the fault location in complex topologies such as three terminal circuits with series compensation and mutually coupled line sections and distribution networks with distributed generation. Novel methods are presented by using traveling wave approach and wavelet transformation technique to overcome the difficulties introduced by the discontinuities and integrated components such as Metal Oxide Varistor (MOV) protected series capacitors and distributed generation in complex topologies. Simulation results show good correlation between the actual and estimated fault locations for all the studied cases. The second problem concerns the calculation techniques of voltage profiles along transmission lines. A simple yet effective approach to accurately and rapidly obtain the voltage profile along a transmission line during fault transients is presented. The objective of the presented method is to eliminate the need to use wave equations and line parameters provided that an electromagnetic Transients Program (EMTP) type transients simulator is available for generating bus voltage transients for a given fault. This is accomplished by developing a time series model to estimate the voltage at an intermediate point along the transmission line. The model is formed for each intermediate point separately. Once the model is obtained it can be used to predict the transient voltage at that point along the line during any fault in the system. The approach can potentially be useful as a post processor to a transient simulator and can be used by developers of transient animations and movies for illustrating fault-initiated propagation of traveling waves in power systems. The third problem is the lack of powerful visualization and animation methods, which can help understanding the complex behavior of power systems during transients. The goal of this part of the dissertation is to develop new animation and visualization methods for power system electromagnetic transients for both educational and research purposes. Proposed approaches are implemented in different environments such as MATLAB and Microsoft Visual Studio to show the effectiveness of two and three-dimensional visualization of power system transients. The implementations of the proposed methods provide better understanding of the power systems during transient phenomena due to the faults or switchings.

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In this work, the problem of detecting neuronal spikes or action potentials (AP) in noisy recordings from a Microelectrode Array (MEA) is investigated. In particular, the spike detection algorithms should be less complex and with low computational complexity so as to be amenable for real time applications. The use of the MEA is that it allows collection of extracellular signals from either a single unit or multiple (45) units within a small area. The noisy MEA recordings then undergo basic filtering, digitization and are presented to a computer for further processing. The challenge lies in using this data for detection of spikes from neuronal firings and extracting spatiotemporal patterns from the spike train which may allow control of a robotic limb or other neuroprosthetic device directly from the brain. The aim is to understand the spiking action of the neurons, and use this knowledge to devise efficient algorithms for Brain Machine Interfaces (BMIs). An effective BMI will require a realtime, computationally efficient implementation which can be carried out on a DSP board or FPGA system. The aim is to devise algorithms which can detect spikes and underlying spatio-temporal correlations having computational and time complexities to make a real time implementation feasible on a specialized DSP chip or an FPGA device. The time-frequency localization, multiresolution representation and analysis properties of wavelets make them suitable for analysing sharp transients and spikes in signals and distinguish them from noise resembling a transient or the spike. Three algorithms for the detection of spikes in low SNR MEA neuronal recordings are proposed: 1. A wavelet denoising method based on the Discrete Wavelet Transform (DWT) to suppress the noise power in the MEA signal or improve the SNR followed by standard thresholding techniques to detect the spikes from the denoised signal. 2. Directly thresholding the coefficients of the Stationary (Undecimated) Wavelet Transform (SWT) to detect the spikes. 3. Thresholding the output of a Teager Energy Operator (TEO) applied to the signal on the discrete wavelet decomposed signal resulting in a multiresolution TEO framework. The performance of the proposed three wavelet based algorithms in terms of the accuracy of spike detection, percentage of false positives and the computational complexity for different types of wavelet families in the presence of colored AR(5) (autoregressive model with order 5) and additive white Gaussian noise (AWGN) is evaluated. The performance is further evaluated for the wavelet family chosen under different levels of SNR in the presence of the colored AR(5) and AWGN noise. Chapter 1 gives an introduction to the concept behind Brain Machine Interfaces (BMIs), an overview of their history, the current state-of-the-art and the trends for the future. It also describes the working of the Microelectrode Arrays (MEAs). The generation of a spike in a neuron, the proposed mechanism behind it and its modeling as an electrical circuit based on the Hodgkin-Huxley model is described. An overview of some of the algorithms that have been suggested for spike detection purposes whether in MEA recordings or Electroencephalographic (EEG) signals is given. Chapter 2 describes in brief the underlying ideas that lead us to the Wavelet Transform paradigm. An introduction to the Fourier Transform, the Short Time Fourier Transform (STFT) and the Time-Frequency Uncertainty Principle is provided. This is followed by a brief description of the Continuous Wavelet Transform and the Multiresolution Analysis (MRA) property of wavelets. The Discrete Wavelet Transform (DWT) and its filter bank implementation are described next. It is proposed to apply the wavelet denoising algorithm pioneered by Donoho, to first denoise the MEA recordings followed by standard thresholding technique for spike detection. Chapter 3 deals with the use of the Stationary or Undecimated Wavelet Transform (SWT) for spike detection. It brings out the differences between the DWT and the SWT. A brief discussion of the analysis of non-stationary time series using the SWT is presented. An algorithm for spike detection based on directly thresholding the SWT coefficients without any need for reconstructing the denoised signal followed by thresholding technique as in the first method is presented. In chapter 4 a spike detection method based on multiresolution Teager Energy Operator is discussed. The Teager Energy Operator (TEO) picks up localized spikes in signal energy and thus is directly used for spike detection in many applications including R wave detection in ECG and various (alpha, beta) rhythms in EEG. Some basic properties of the TEO are discussed followed by the need for a multiresolution approach to TEO and the methods existing in literature. The wavelet decomposition and the subsampled signal involved at each level naturally lends it to a multiresolution TEO framework at the same time significantly reducing the computational complexity due the subsampled signal at each level. A wavelet-TEO algorithm for spike detection with similar accuracies as the previous two algorithms is proposed. The method proposed here differs significantly from that in literature since wavelets are used instead of time domain processing. Chapter 5 describes the method of evaluation of the three algorithms proposed in the previous chapters. The spike templates are obtained from MEA recordings, resampled and normalized for use in spike trains simulated as Poisson processes. The noise is modeled as colored autoregressive (AR) of order 5, i.e AR(5), as well as Additive White Gaussian Noise (AWGN). The noise in most human and animal MEA recordings conforms to the autoregressive model with orders of around 5. The AWGN Noise model is used in most spike detection methods in the literature. The performance of the proposed three wavelet based algorithms is measured in terms of the accuracy of spike detection, percentage of false positives and the computational complexity for different types of wavelet families. The optimal wavelet for this purpose is then chosen from the wavelet family which gives the best results. Also, optimal levels of decomposition and threshold factors are chosen while maintaining a balance between accuracy and false positives. The algorithms are then tested for performance under different levels of SNR with the noise modeled as AR(5) or AWGN. The proposed wavelet based algorithms exhibit a detection accuracy of approximately 90% at a low SNR of 2.35 dB with the false positives below 5%. This constitutes a significant improvement over the results in existing literature which claim an accuracy of 80% with false positives of nearly 10%. As the SNR increases, the detection accuracy increases to close to 100% and the false alarm rate falls to 0. Chapter 6 summarizes the work. A comparison is made between the three proposed algorithms in terms of detection accuracy and false positives. Directions in which future work may be carried out are suggested.

Die vorliegende Arbeit befasst sich mit der Anwendung von Methoden der Zeit-Frequenz-Analyse zur Diagnostik der Verzahnungen in Getrieben anhand von Schwingungssignalen. Es werden neue Analysemethoden aus der Zeit-Frequenz-Analyse zur Verbesserung der Diagnosetechnik, wie die “Koeffizientenanalyse aus Zeit-Frequenz-Darstellungen“ oder die “Drehwinkel-Ordnungs-Darstellungen in Polarkoordinaten“ eingeführt. Basierend auf diesen Analyseverfahren wird eine geeignete Diagnosestrategie vorgestellt, mit der auch bei instationären Betriebszuständen Verzahnungsschäden sicher erkannt werden können. Zur praktischen Anwendung der Zeit-Frequenz-Analyse in der Verzahnungsdiagnose wird ein Rechenprogramm entwickelt, in dem eine Vielzahl von Signalanalyseprozeduren implementiert sind, um somit eine zuverlässige diagnostische Beurteilung von Verzahnungszuständen zu ermöglichen. In einem umfangreichen Versuchsprogramm werden die Anwendungsmöglichkeiten der beschriebenen Mittel und Verfahren auf verschiedenen Verzahnungsschäden in mehreren Abstufungen demonstriert, und die Eignung der Methoden wird beurteilt.

This paper is intended to present wavelet Galerkin schemes for the boundary element method. Wavelet Galerkin schemes employ appropriate wavelet bases for the discretization of boundary integral operators. This yields quasisparse system matrices which can be compressed to O(N_J) relevant matrix entries without compromising the accuracy of the underlying Galerkin scheme. Herein, O(N_J) denotes the number of unknowns. The assembly of the compressed system matrix can be performed in O(N_J) operations. Therefore, we arrive at an algorithm which solves boundary integral equations within optimal complexity. By numerical experiments we provide results which corroborate the theory.

Thesis--Gerhard-Mercator-Universitat, 1999.

This research provides insight into changes in volumetric flushing of the Tampa Bay estuary caused by synoptic scale wind events. The two main studies of this dissertation involve 1) using wavelet analysis to investigate the link between the El Niño-Southern Oscillation (ENSO) and the frequency and strength of volumetric flushing driven by synoptic variability and 2) using a multi-decadal model simulation to examine how extratropical/winter storms and hurricanes affect the overall flushing rates for Tampa Bay, FL. In the first study, two analyses are performed on 55 years of observational data to investigate the effect of multiple small wind events on estuarine flushing. First I use subtidal observed water level as a proxy for mean tidal height to estimate the rate of volumetric bay outflow. Second, I use wavelet analysis on sea level and wind data to isolate the synoptic sea level and surface wind variance. For both analyses the long-term monthly climatology is removed to focus on the volumetric and wavelet variance anomalies. The overall correlation between the Oceanic Niño index and volumetric anomalies is small (r2=0.097) due to the seasonal dependence on the ENSO response. The mean monthly climatology between the synoptic wavelet variance of elevation and axial winds have similar seasonal behavior. During the winter, El Niño (La Niña) increases (decreases) the synoptic variability, but decreases (increases) it during the summer. The difference in winter El Niño/La Niña wavelet variances is about 20% of the climatological value. ENSO can swing the synoptic flushing of the bay by 0.22 bay volumes per month. These changes in circulation associated with synoptic variability have the potential to impact mixing and transport within the bay. In the second study, volumetric changes from large scale weather events are investigated using a numerical circulation model simulation (1975-2006) to find the cumulative impact of flushing on the bay by extreme events. The strong wind speeds, duration of high winds and wind direction during these events all affect the amount of water flushed in and out of the estuary. Normalized volume anomalies are largest when wind components blow up/down the estuary in the NE/SW direction. Wind induced normalized flushing rates for all 10 extratropical/winter storms range from 12% to 40% and from 14% to 40% for all 10 hurricanes. All storms discussed in this study caused winds greater than 15 m s-1 (~30 knots). The direction of the winds had an impact on the flushing rates during these extreme events. Storm9 (February 1998) and Hurricane Gabrielle (September 2001) experienced the smallest total volume changes (14% and 13%). Both storms experienced weak axial and co-axial winds causing volume changes to be small. The Storm of the Century (March 1993) and Hurricane Frances (September 2004) saw the largest total volume changes of 40%. They both had strong winds blowing in the NE direction. Hurricane Frances had two wind peaks and lingered in the area for approximately 48 hours, so both strength and duration of winds played a large role in the total volume change. Total inflow and outflow rates per year show that there is year to year variability of flushing in Tampa Bay.