In this work, power supply noise contribution, at a particular node on the power grid, from clock/power gated blocks is maximized at particular time and the synthetic gating patterns of the blocks that result in the maximum noise is obtained for the interval 0 to target time. We utilize wavelet based analysis as wavelets are a natural way of characterizing the time-frequency behavior of the power grid. The gating patterns for the blocks and the maximum supply noise at the Point of Interest at the specified target time obtained via a Linear Programming (LP) formulation (clock gating) and Genetic Algorithm based problem formulation (Power Gating).

Tomographic MR (Magnetic Resonance) and sonographic biosignal processing are important non-invasive diagnostic methods used in a medicine. A noise added into processed data by an amplifier of tomograph receiving part and by circuits of sonograph is resulting in a body organ diagnosis degradation. Image data are stored in a standardized DICOM medical file format. Methods using wavelet analysis for noise suppression in image data have been designed and their comparation with classical methods has been made in this work. The MATLAB was utilized for data processing and data rewriting back to the DICOM format.

No description available.

The objective of this thesis is to investigate the flow conditions that lead to coarse grain entrainment at near incipient motion conditions. Herein, a new conceptual approach is proposed, which in addition to the magnitude of hydrodynamic force or flow power, takes into account the duration of the flow event. Two criteria for inception of grain entrainment, namely the critical impulse and critical energy concepts, are proposed and compared. These frameworks adopt a force or energy perspective, considering the momentum or energy transfer from each flow event to the particle respectively, to describe the phenomenon. A series of conducted mobile particle experiments, are analyzed to examine the validity of the proposed approaches. First a set of bench-top experiments incorporates an electromagnet which applies pulses of known magnitude and duration to a steel spherical particle in a controlled fashion, so as to identify the critical level for entrainment. The utility of the above criteria is also demonstrated for the case of entrainment by the action of turbulent flow, via analysis of a series of flume experiments, where both the history of hydrodynamic forces exerted on the particle as well as its response are recorded simultaneously. Statistical modeling of the distribution of impulses, as well as conditional excess impulses, is performed using distributions from Extreme Value Theory to effectively model the episodic nature of the occurrence of these events. For the examined uniform and low mobility flow conditions, a power law relationship is proposed for describing the magnitude and frequency of occurrence of the impulse events. The Weibull and exponential distributions provide a good fit for the time between particle entrainments. In addition to these statistical tools, a number of Adaptive Neuro-Fuzzy Inference Systems employing different input representations are used to learn the nonlinear dynamics of the system and perform statistical prediction. The performance of these models is assessed in terms of their broad validity, efficiency and forecast accuracy. Even though the impulse and energy criteria are deeply interrelated, the latter is shown to be advantageous with regard to its performance, applicability and extension ability. The effect of single or multiple highly energetic events carried by certain coherent flow structures (mainly strong sweep events) with regard to the particle response is also investigated. / Ph. D.

A very short and disagreeable audible and tactile response from a vehicle driveline may be excited when the throttle is abruptly applied or released, or when the clutch is rapidly engaged. The condition is most noticeable in low gear and in slow moving traffic, when other background engine and road noise levels are low. This phenomenon is known as clonk and is often associated with the first cycle of shuffle response, which is a low frequency longitudinal vehicle movement excited by throttle demand. It is often reported that clonk may coincide with each cycle of the shuffle response, and multiple clonks may then occur. The problem is aggravated by backlash and wear in the drivetrain, and it conveys a perception of low quality to the customer. Hitherto, reported investigations do not reveal or discuss the mechanism and causal factors of clonk in a quantitative manner, which would relate the engine impulsive torque to the elastic response of the driveline components, and in particular to the noise radiating surfaces. Crucially, neither have the issues of sensitivity, variability and non-linearity been addressed and published. It is also of fundamental importance that clonk is seen as a total system response to impulsive torque, in the presence of distributed lash at the vibro-elastic impact sites. In this thesis, the drivetrain is defined as the torque path from the engine flywheel to the road wheels. The drivetrain is a lightly damped and highly non-linear dynamic system. There are many impact and noise emitting locations in the driveline that contribute to clonk, when the system is subjected to shock torque loading. This thesis examines the clonk energy paths, from the initial impact to many driveline lash locations, and to the various noise radiating surfaces. Both experimental and theoretical methods are applied to this complex system. Structural and acoustic dynamics are considered, as well as the very important frequency couplings between elastic structures and acoustic volumes. Preliminary road tests had indicated that the clonk phenomenon was a, very short transient impact event between lubricated contacts and having a high frequency characteristic. This indicated that a multi-body dynamics simulation of the driveline, in conjunction with a high frequency elasto-acoustic coupling analysis, would be required. In addition, advanced methods of signal analysis would be required to handle the frequency content of the very short clonk time histories. These are the main novelties of this thesis. There were many successful outcomes from the investigation, including quantitative agreement between the numerical and experimental investigations. From the experimental work, it was established that vehicle clonk could be accurately reproduced on a driveline rig and also on a vehicle chassis dynamometer, under controlled test conditions. It then enabled Design of Experiments to be conducted and the principal causal factors to be identified. The experimental input and output data was also used to verify the mathematical simulation. The high frequency FE analysis of the structures and acoustic cavities were used to predict the dynamic modal response to a shock input. The excellent correlation between model and empirical data that was achieved, clearly established the clonk mechanism in mathematical physics terms. Localised impact of meshing gears under impulsive loads were found to be responsible for high frequency structural wave propagation, some of which coupled with the acoustics modes of cavities, when the speed of wave propagation reached supersonic levels. This finding, although previously surmised, has been shown in the thesis and constitutes a major contribution to knowledge.

The research work presented in this thesis describes the development and testing of a new data analysis methodology for the automated near real-time detection and approximate location of pipe bursts and other events which induce similar abnormal pressure/flow variations (e.g., unauthorised consumptions, equipment failures, etc.) in Water Distribution Systems (WDSs). This methodology makes synergistic use of several self-learning Artificial Intelligence (AI) and statistical/geostatistical techniques for the analysis of the stream of data (i.e., signals) collected and communicated on-line by the hydraulic sensors deployed in a WDS. These techniques include: (i) wavelets for the de-noising of the recorded pressure/flow signals, (ii) Artificial Neural Networks (ANNs) for the short-term forecasting of future pressure/flow signal values, (iii) Evolutionary Algorithms (EAs) for the selection of optimal ANN input structure and parameters sets, (iv) Statistical Process Control (SPC) techniques for the short and long term analysis of the burst/other event-induced pressure/flow variations, (v) Bayesian Inference Systems (BISs) for inferring the probability of a burst/other event occurrence and raising the detection alarms, and (vi) geostatistical techniques for determining the approximate location of a detected burst/other event. The results of applying the new methodology to the pressure/flow data from several District Metered Areas (DMAs) in the United Kingdom (UK) with real-life bursts/other events and simulated (i.e., engineered) burst events are also reported in this thesis. The results obtained illustrate that the developed methodology allowed detecting the aforementioned events in a fast and reliable manner and also successfully determining their approximate location within a DMA. The results obtained additionally show the potential of the methodology presented here to yield substantial improvements to the state-of-the-art in near real-time WDS incident management by enabling the water companies to save water, energy, money, achieve higher levels of operational efficiency and improve their customer service. The new data analysis methodology developed and tested as part of the research work presented in this thesis has been patented (International Application Number: PCT/GB2010/000961).

This thesis provides an analysis of systemic risk in the US banking sector. We use conditional value at risk (∆CoVaR), marginal expected shortfall (MES) and cross-quantilogram (CQ) to statistically measure tail-dependence in return series of individual institutions and the system as a whole. Wavelet multireso- lution analysis is used to study systemic risk in the time-frequency domain. De- composition of returns on different scales allows us to isolate cycles of 2-8 days, 8-32 days and 32-64 days and analyze co-movement patterns which would oth- erwise stay hidden. Empirical results demonstrate that filtering out short-term noise from the return series improves the forecast power of ∆CoVaR. Eventu- ally, we investigate the connection between statistical measures of systemic risk and fundamental characteristics of institutions (size, leverage, market to book ratio) and conclude that size is the most robust determinant of systemic risk.

O objetivo deste estudo foi investigar os padrões de distribuição de energia e as propriedades espectrais dos principais músculos de membro inferior de diabéticos neuropatas durante a marcha, utilizando a análise de wavelet. Foram coletados dados de EMG de superfície (bipolar) dos músculos tibial anterior, vasto lateral e gastrocnêmio medial no ciclo da marcha em 21 pacientes diabéticos diagnosticados com neuropatia periférica, e 21 indivíduos não- diabéticos. A energia do sinal e freqüência foram comparados entre os grupos no ciclo da marcha e em cada faixa de freqüência (7-542Hz), utilizando testes t. A Análise de Componentes Principais foi utilizada para avaliar as diferenças entre os padrões eletromiográficos de diabéticos e não-diabéticos. Os indivíduos diabéticos exibiram menores energias nas menores frequências para todos os músculos, e energias mais altas nas maiores frequências nos músculos extensores do membro inferior. Os pacientes também apresentaram menor energia de gastrocnêmio medial e uma maior energia de vasto lateral comparado aos não diabéticos, e este último achado sugere uma estratégia para compensar o déficit dos extensores de tornozelo para impulsionar o corpo na marcha. Os resultados mostram, de maneira geral, uma mudança na estratégia neuromuscular dos pacientes diabéticos, sugerindo que os principais músculos extensores do membro inferior adaptam a sua resposta a fim de produzir a energia necessária para realizar essa tarefa, a do andar / The aim of this study was to investigate lower limb muscle\'s energy patterns and spectral properties of diabetic neuropathic individuals during gait cycle using wavelet approach. Bipolar surface EMG of tibialis anterior, vastus lateralis and gastrocnemius medialis were acquired in the whole gait cycle in 21 diabetic patients already diagnosed with peripheral neuropathy, and 21 non-diabetic individuals. The signal´s energy and frequency were compared between groups in the whole gait cycle and in each frequency band (7-542Hz) using t tests. Principal component analysis was used to assess differences between diabetic and non-diabetic EMG patterns. The diabetic individuals displayed lower energies in lower frequency bands for all muscles and higher energies in higher frequency bands in the extensors\' muscles. They also showed lower energy of gastrocnemius and a higher energy of vastus, and this last finding suggests a strategy to compensate the ankle extensor deficit to propel the body forward. The overall results suggest a change in the neuromuscular strategy of diabetic patients, suggesting that the main extensor muscles of the lower limb adapt their response to produce the energy necessary to accomplish the walking task

A avaliação perceptivo-auditiva tem papel fundamental no estudo e na avaliação da voz, no entanto, por ser subjetiva está sujeita a imprecisões e variações. Por outro lado, a análise acústica permite a reprodutibilidade de resultados, porém precisa ser aprimorada, pois não analisa com precisão vozes com disfonias mais intensas e com ondas caóticas. Assim, elaborar medidas que proporcionem conhecimentos confiáveis em relação à função vocal resulta de uma necessidade antiga dentro desta linha de pesquisa e atuação clínica. Neste contexto, o uso da inteligência artificial, como as redes neurais artificiais, indica ser uma abordagem promissora. Objetivo: Validar um sistema automático utilizando redes neurais artificiais para a avaliação de vozes rugosas e soprosas. Materiais e métodos: Foram selecionadas 150 vozes, desde neutras até com presença em grau intenso de rugosidade e/ou soprosidade, do banco de dados da Clínica de Fonoaudiologia da Faculdade de Odontologia de Bauru (FOB/USP). Dessas vozes, 23 foram excluídas por não responderem aos critérios de inclusão na amostra, assim utilizaram-se 123 vozes. Procedimentos: avaliação perceptivo-auditiva pela escala visual analógica de 100 mm e pela escala numérica de quatro pontos; extração de características do sinal de voz por meio da Transformada Wavelet Packet e dos parâmetros acústicos: jitter, shimmer, amplitude da derivada e amplitude do pitch; e validação do classificador por meio da parametrização, treino, teste e avaliação das redes neurais artificiais. Resultados: Na avaliação perceptivo-auditiva encontrou-se, por meio do teste Coeficiente de Correlação Intraclasse (CCI), concordâncias inter e intrajuiz excelentes, com p = 0,85 na concordância interjuízes e p variando de 0,87 a 0,93 nas concordâncias intrajuiz. Em relação ao desempenho da rede neural artificial, na discriminação da soprosidade e da rugosidade e dos seus respectivos graus, encontrou-se o melhor desempenho para a soprosidade no subconjunto composto pelo jitter, amplitude do pitch e frequência fundamental, no qual obteve-se taxa de acerto de 74%, concordância excelente com a avaliação perceptivo-auditiva da escala visual analógica (0,80 no CCI) e erro médio de 9 mm. Para a rugosidade, o melhor subconjunto foi composto pela Transformada Wavelet Packet com 1 nível de decomposição, jitter, shimmer, amplitude do pitch e frequência fundamental, no qual obteve-se 73% de acerto, concordância excelente (0,84 no CCI), e erro médio de 10 mm. Conclusão: O uso da inteligência artificial baseado em redes neurais artificiais na identificação, e graduação da rugosidade e da soprosidade, apresentou confiabilidade excelente (CCI > 0,80), com resultados semelhantes a concordância interjuízes. Dessa forma, a rede neural artificial revela-se como uma metodologia promissora de avaliação vocal, tendo sua maior vantagem a objetividade na avaliação. / The auditory-perceptual evaluation is fundamental in the study and analysis of voice. This evaluation, however, is subjective and tends to be imprecise and variable. On the other hand, acoustic analysis allows reproducing results, although these results must be refined since the analysis is not precise enough for intense dysphonia or chaotic waves. Therefore, the will to develop measurements allowing reliable knowledge related to vocal function is not new on this research and clinical actuation field. In this context, the use of artificial intelligence such as neural networks seems to be a promising research field. Objective: to validate an automatic system using artificial neural networks for evaluation of vocal roughness and breathiness. Methods: One hundred fifty (150) voices were selected from from Clínica de Fonoaudiologia da Faculdade de Odontologia de Bauru (FOB/USP) database. These voices presented variation from neutral to intense roughness and/or breathiness. Twenty-three of them were excluded since they did not match inclusion criteria. Thus, 123 voices were used for analysis. The procedures include use of auditoryperception based on two scales: visual analog scale of 100 mm and four points numerical scale. Additionally, the characteristics of voice signals were extracted by Wavelet Packet Transform and by analysis of acoustic parameters: jitter, shimmer, derivative amplitude and pitch amplitude. Validation of classifying system was carried out by parameterization, training, test and evaluation of artificial neural networks. Results: In the auditory-perceptual evaluation, excellent interrater (p=0.85) and intrarater (0.87<p<0.93) agreement were obtained by means of Intraclass Correlation Coefficient (ICC) testing. The artificial neural network performance has achieved the best results for breathiness in the subset composed by parameters jitter, pitch amplitude and fundamental frequency. In this case, the neural network obtained a rate of 74%, demonstrating excellent concordance with auditory-perceptual evaluation for visual analog scale (0.80 ICC) and mean error of 9 mm. As for roughness evaluation, the best subset is composed by Wavelet Packet Transform with 1 resolution level, jitter, shimmer, pitch amplitude and fundamental frequency. For this case, a 73% rate was achieved (0.84 ICC) and mean error of 10 mm was obtained. Conclusion: The use of artificial neural networks for roughness and breathiness evaluation present high reliability (ICC&gt;0.80), with results similar to interrater agreement. Thus, the artificial neural network reveals a promising method for vocal evaluation, bringing objective analysis as a strong advantage.

"In this work an innovative wavelet packet based sifting process for signal decomposition has been developed and its application for health monitoring of time-varying structures is presented. With the proposed sifting process, a signal can be decomposed into its mono-frequency components by examining the energy content in the wavelet packet components of a signal, and imposing certain decomposition criteria. The method is illustrated for simulation data of a linear three degree-of-freedom spring-mass-damper system and the results are compared with those obtained using the empirical mode decomposition (EMD) method. Both methods provide good approximations, as compared with the exact solution for modal responses from a conventional modal analysis. Incorporated with the classical Hilbert transform, the proposed sifting process may be effectively used for structural health monitoring by monitoring instantaneous modal parameters of the structure for both, cases of abrupt structural stiffness loss and progressive stiffness degradation. The effectiveness of this method for practical application is evaluated by applying the methodology for experimental data and the results obtained matched with the field observations. The proposed methodology has shown better results in a comparison study which is done to evaluate performance of the proposed approach with other available SHM techniques, namely EMD technique and Continuous Wavelet Transform (CWT) method, for cases characterized by different damage scenarios and noise conditions."