Este trabalho apresenta o estudo e o desenvolvimento de um algoritmo híbrido para detecção, classificação e localização de faltas em sistemas com três terminais utilizando como principal ferramenta a transformada wavelet (TW) em suas versões discreta (TWD) e estacionária (TWE). O algoritmo é dito híbrido, pois alia duas metodologias para localizar a falta. A primeira baseada na análise de componentes de alta freqüência (ondas viajantes) e a segunda, baseada na extração dos componentes fundamentais para o cálculo da impedância aparente. A metodologia proposta foi concebida de maneira a trabalhar com dados sincronizados dos três terminais ou apenas dados locais para estimar a localização da falta. O localizador híbrido escolhe automaticamente qual a melhor técnica de localização ser utilizada para alcançar uma localização confiável e precisa. Deste modo, um método pode suprir as dificuldades do outro, ou, no mínimo, fornecer mais informações para que, junto ao conhecimento do operador, uma localização próxima da ótima possa ser alcançada. Com o objetivo de testar e validar a aplicabilidade do algoritmo de localização de faltas híbrido para linhas com três terminais, utilizou-se de dados de sinais faltosos obtidos através de simulações do software ATP (Altenative Transients Program), levando-se em conta a variação de diversos parâmetros que poderiam influenciar o desempenho do algoritmo proposto. Os resultados alcançados pelo algoritmo frente às situações avaliadas são bastante animadores, apontando a uma promissora aplicabilidade do mesmo. / This work presents a study and development of a hybrid algorithm for fault detection, classification and location in tree terminal lines based on wavelet transform (WT). It will be presented in two versions: discrete wavelet transform (DWT) and stationary wavelet transform (SWT). The algorithm is called hybrid because it uses two fault location methodologies: one based on fundamental components and other based on traveling waves. The proposed methodology works either with synchronized tree terminal data or only local data. The hybrid fault locator chooses automatically which location technique to be used in order to reach a reliable and accurate fault location. In this manner, this technique can avoid some difficulties present in other techniques, aiming to reach an optimized fault location. The proposed hybrid fault location was evaluated by simulated fault signals obtained by alternative transient program (ATP). In the tests, several parameters, which would influence the performance of the hybrid algorithm, were varied, such as: fault inception angle, fault resistance, fault type, etc. The results obtained by the proposed methodology are very encouraging and it points out to a very promising application.

Power quality is a main subject to considerable attentions from utilities and customers owing to the popular uses of many non-linear electronic equipment in recent years. Harmonics, voltage swell, voltage sag, and, power interruption could downgrade the service quality. To ensure the power quality, detecting harmonic and voltage disturbances becomes an important issue. In other words, a detection method with classification capability will be helpful for detecting disturbances. The thesis proposed two models of power quality detection for power system disturbances using voltage-current(V-I) characteristics in the time domain with hybrid wavelets grey relational analysis (WGRA), and self-organizing feature map network (WSOM). Morlet wavelets are responsible for extracting features from voltages and currents. GRA and SOM were employed to identify the types of various disturbance patterns. Computer simulations have demonstrated the computational efficiency and accurate recognition capability for power quality detection and discrimination with an IEEE 14-Bus power system.

La presente tesis doctoral se centra en el estudio de la transformada wavelet no diezmada (undecimated wavelet transform, UWT), así como en su aplicación a procesos de reducción de ruido y detección de señales. Como principales ventajas frente a la wavelet discreta tradicional (discrete wavelet transform, DWT), la wavelet no diezmada presenta invarianza frente a desplazamientos de la señal de entrada, no produce pérdida de resolución temporal en las sucesivas escalas de descomposición, y además proporciona una información adicional que desde el punto de vista de un procesado lineal sería redundante, pero que en combinación con procesados no lineales puede dar lugar a mejoras importantes en aplicaciones tales como el análisis de señales o la reducción de ruido. En este contexto, los objetivos principales de esta tesis van a ser dos. Por un lado, profundizar en el estudio de la transformada wavelet no diezmada, tratando de establecer un marco unificado para las distintas denominaciones e implementaciones existentes en la literatura. Por otro lado, plantear la aplicación de esta transformada al desarrollo de algoritmos para la reducción de ruido. En este sentido, se va a considerar el problema del ruido de grano en evaluación no destructiva por ultrasonidos. Este tipo de ruido se debe a las reflexiones en las pequeñas discontinuidades internas de los materiales, que se introducen en la misma banda de frecuencias que los defectos a detectar. Por ello no se pueden eliminar con los filtrados clásicos para ruido blanco. La tesis aborda este problema planteando algoritmos específicos, que aprovechan la diferente distribución espacio-temporal de la señal y el ruido en el dominio de la wavelet no diezmada. La metodología empleada para llevar a cabo los objetivos anteriores, incluye el desarrollo e implementación de nuevos métodos de procesado utilizando el lenguaje MATLAB(R). A lo largo de la tesis se plantean cuatro esquemas distintos de reducción de ruido de grano ultrasónico basados en las diferentes posibilidades de implementación y reconstrucción que ofrece la transformada UWT. / Pardo Gómez, E. (2011). Trasformadas Wavelet no diezmadas para reducción de ruido y detección de señáles: Aplicaciones en Evaluación No Destructiva por Ultrasonidos [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11518 / Palancia

This work deals with theory of wavelet transform and Mallat’s algorithm. It also includes the programming method of creating VST plug-in modules and describes the developement of the plug-in module, witch uses the modificated coeficients of wavelet transform to applicate the music effect.

We propose a recursive formula for moments of scaling function and sum rule. It is shown that some quadrature formulae has a higher degree of accuracy under proposed moment condition. On this basis we obtain higher accuracy formula for wavelet expansion coefficients which are needed to start the fast wavelet transform and estimate convergence rate of wavelet approximation and sampling of smooth functions. We also present a direct algorithm for solving refinement equation.

Time-Frequency decomposition is a signal processing method for analyzing and extracting information from aperiodic signals. Analysis of these signals are ineffective when done using the Fourier transform, instead these signals must be analyzed in the time and frequency domain simultaneously. The current tools for Time-Frequency analysis are either proprietary or computationally expensive making it prohibitive for researchers to use. This thesis investigates the computational aspects of signal processing with a focus on Time-Frequency analysis using wavelets. We develop algorithms that compute and plot the Time-Frequency decomposition automatically, and implement them in C++ as a framework. As a result our framework is significantly faster than MATLAB, and can be easily incorporated into applications that require Time-Frequency analysis. The framework is applied to identify the Event Related Spectral Perturbation of EEG signals; and to vibrational analysis by identifying the mechanical modal parameters of oscillating machines. / Thesis / Master of Applied Science (MASc)

International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / Because all video compression schemes introduce artifacts into the compressed video images, degradation occurs. These artifacts, generated by a wavelet-based compression scheme, will vary with the compression ratio and input imagery, but do show some consistent patterns across applications. There are a number of design trade-offs that can be made to mitigate the effect of these artifacts. By understanding the artifacts introduced by video compression and being able to anticipate the amount of image degradation, the video compression can be configured in a manner optimal to the application under consideration in telemetry.

The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy.

Various climate models have been developed to analyze and predict climate change; however, model uncertainties cannot be easily overcome. A statistical approach has been presented in this paper to calculate the distributions of future climate change based on an ensemble of the Weather Research and Forecasting (WRF) models. Wavelet analysis has been adopted to de-noise the WRF model output. Using the de-noised model output, we carry out Bayesian analysis to decrease uncertainties in model CAM_KF, RRTM_KF and RRTM_GRELL for each downscaling region. / text

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