Sabe-se que folgas e impactos de partes internas reduzem o desempenho da transmissão, do sistema de controle e a qualidade global do mecanismo. Folgas causam instabilidade, mas impactos também geram ruídos estruturais que perturbam medições e causam ruído acústico que reduz o conforto. Sinais de movimento são frequentemente usados para realimentação e podem ser perturbados por ruídos de impacto. Para avaliar este problema, um procedimento capaz de detectar e quantificar a severidade e a frequência de ocorrência de impactos é proposto. Desta forma, o desempenho de diferentes sistemas em relação à sua robustez ao impacto pode ser comparado. Sinais de vibração na estrutura do sistema são medidos e o sinal é processado digitalmente e decomposto em múltiplas escalas pela Transformada de Wavelet. As wavelets Harmônicas e de Daubechies são comparadas. Escalas que correspondem aos níveis de detalhamento característicos dos impactos são extraídas do sinal de vibração e a severidade do impacto é avaliada. Após o processamento do sinal e a retirada do ruído, o sinal original é disponibilizado para posterior processamento e controle. Foi estudado o ruído de impacto em caixas de direção conhecido como knock noise. Ao final, tentou-se comparar a técnica de Transformada de Wavelet com a técnica de Emissão Acústica. Na sessão Transformada de Wavelets é feito um breve estudo de alguns conceitos necessários para o entendimento de wavelets e é explicada a técnica utilizada para a detecção dos ruídos de impacto. Na sessão Emissão Acústica é explicada a técnica utilizada para comparar com a técnica de wavelets. Na sessão Direção Hidráulica é explicado o mecanismo de direção estudado. Na sessão Resultados, são mostrados os sinais de ruídos de impacto detectados pela Transformada de Wavelet e Emissão Acústica e o método adotado para mensurar a severidade dos impactos e a frequência de ocorrência dos mesmos. / It is well known that backlash and impact of inner parts reduce the performance of transmission, control systems and overall quality of mechanism. Clearance causes instability, but impact also generates structural borne noise that disrupts measurements and cause acoustic noise that reduces the comfort. Motion signals are often used for feedback and can be disrupted by impact noise. In order to overcome this problem a procedure able to detect and quantify the severity and rate of occurrence of impacts is proposed. Thence performance of different systems in regard to their robustness to impact can be compared. A procedure to detect and evaluate impacts of inner parts of mechanical mechanism is presented. Therefore, vibration signals on the structure of the system are measured and the signal is digitally processed and decomposed into multiscales by the Wavelet transform. Both Daubechies and Harmonic Wavelet are compared. Scales that correspond to the impact response are extracted from the overall vibration signal, and the impact severity is rated. After signal processing and de-noising, the original signal without the impact is also available for further processing and control. The analysis technique was applied to study the impact noise in steering boxes known as knock noise. Finally, it was tried to compare the technique of wavelet transform with the Acoustic Emission. First, a brief study is presented of some concepts necessary to understand wavelets and Wavelet Transform technique is explained and the approach used for detection of impact noise is presented. In the following section the technique of Acoustic Emission is explained and how it was compared to the wavelet technique. The Hydraulic Steering System is explained in the next section. In the final section the Results of the study are show, the signal with impact noise, the impact detected by the Wavelet Transform and Acoustic Emission and the method used to measure the severity of impacts and to measure the frequency of their occurrence.

Digital images are widely used in computer applications. Uncompressed digital images require considerable storage capacity and transmission bandwidth. Efficient image compression solutions are becoming more critical with the recent growth of data intensive, multimedia-based web applications. This thesis studies image compression with wavelet transforms. As a necessary background, the basic concepts of graphical image storage and currently used compression algorithms are discussed. The mathematical properties of several types of wavelets, including Haar, Daubechies, and biorthogonal spline wavelets are covered and the Enbedded Zerotree Wavelet (EZW) coding algorithm is introduced. The last part of the thesis analyzes the compression results to compare the wavelet types.

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores - Major Energia. Faculdade de Engenharia. Universidade do Porto. 2008

Under most conditions, meiotic recombination is essential for ensuring that organisms adapt to ever changing biotic and abiotic conditions and, as such, it shapes evolutionary change within and between species. The interplay between selection and recombination plays a role shaping levels diversity within populations. Remarkably, recombination is itself an evolving trait that varies at many levels: between distant species of eukaryotes, between closely related species and among populations (and individuals) of the same species. Recombination rates also vary across genomes. Most of the causes and mechanisms of this plasticity in recombination rates and distribution are not clearly understood. Also, our understanding of how this variability in recombination rates influences levels of diversity within populations and across genomes is incomplete. Here, I present a study combining molecular genetics with bioinformatic techniques to characterize recombination landscapes in Drosophila melanogaster. I present a model that accounts for a significant fraction of the variation in crossover rates across the genome of Drosophila melanogaster. Our predictive model suggests that crossover distribution is influenced by both meiosis-specific chromatin dynamics and very local constitutively open chromatin associated with DNA motifs that prevent nucleosome stabilization. I also present a novel method for genomic scans to identify recent events of adaptation in using nucleotide diversity data. In addition, I characterized variability in recombination rates in different populations of D. melanogaster and detected that the highest degree of variability in recombination rates across the genome is associated with intermediate genomic scales, and that this intermediate scale also plays a major role in explaining differences in recombination among populations. Our report is the first linking variation in recombination rates across genomes (genomic) and among populations (evolutionary), possibly suggesting a common mechanistic/genomic cause. Finally, I present preliminary data of the first large-scale project to study the effects of multiple environmental conditions in recombination rates at genome-wide level. In conclusion, these studies provide a new framework to investigate variation in recombination rates and to understand the genomic causes and evolutionary consequences.

This study focuses on implementing a log analysis strategy that combines a neural network algorithm and wavelet transform. Wavelet transform allows us to extract the important hidden information and features of the original time series log data and offers a precise framework for the analysis of input information. While neural network algorithm constitutes a powerfulnonlinear function approximation which can provide detection and prediction functions. The combination of the two techniques is based on the idea of using wavelet transform to denoise the log data by decomposing it into a set of coefficients, then feed the denoised data into a neural network. The experimental outputs reveal that this strategy can have a better ability to identify the patterns among problems in a log dataset, and make predictions with a better accuracy. This strategy can help the platform maintainers to adopt corresponding actions to eliminate risks before the occurrence of serious damages.

A wavelet is a function which is used to construct a specific type of orthonormal basis. We are interested in using C*-algebras and Hilbert C*-modules to study wavelets. A Hilbert C*-module is a generalisation of a Hilbert space for which the inner product takes its values in a C*-algebra instead of the complex numbers. We study wavelets in an arbitrary Hilbert space and construct some Hilbert C*-modules over a group C*-algebra which will be used to study the properties of wavelets. We study wavelets by constructing Hilbert C*-modules over C*-algebras generated by groups of translations. We shall examine how this construction works in both the Fourier and non-Fourier domains. We also make use of Hilbert C*-modules over the space of essentially bounded functions on tori. We shall use the Hilbert C*-modules mentioned above to study wavelet and scaling filters, the fast wavelet transform, and the cascade algorithm. We shall furthermore use Hilbert C*-modules over matrix C*-algebras to study multiwavelets.

This thesis examines the application of the techniques of Fourier spectrogram and wavelet analysis to a low power embedded microprocessor application in a novel railway and rollingstock monitoring system. The safe and cost effective operation of freight railways is limited by the dynamic performance of wagons running on track. A monitoring system has been proposed comprising of low cost wireless sensing devices, dubbed “Health Cards”, to be installed on every wagon in the fleet. When marshalled into a train, the devices would sense accelerations and communicate via radio network to a master system in the locomotive. The integrated system would provide online information for decision support systems. Data throughput was heavily restricted by the network architecture, so significant signal analysis was required at the device level. An electronics engineering team at Central Queensland University developed a prototype Health Card, incorporating a 27MHz microcontroller and four dual axis accelerometers. A sensing arrangement and online analysis algorithms were required to detect and categorise dynamic events while operating within the constraints of the system. Time-frequency analysis reveals the time varying frequency content of signals, making it suitable to detect and characterise transient events. With efficient algorithms such as the Fast Fourier Transform, and Fast Wavelet Transform, time-frequency analysis methods can be implemented on a low power, embedded microcontroller. This thesis examines the application of time-frequency analysis techniques to wagon body acceleration signals, for the purpose of detecting poor dynamic performance of the wagon-track system. The Fourier spectrogram is implemented on the Health Card prototype and demonstrated in the laboratory. The research and algorithms provide a foundation for ongoing development as resources become available for system testing and validation.

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.

Le principal objectif de ce travail est l’étude de l’influence de la pesanteur sur la mécanique respiratoire et le contrôle de la respiration, ainsi que sur les interactions cardio-respiratoires pendant les différents stades du sommeil. Le chapitre introductif présente le contexte général et les objectifs de la thèse. Des sections abordant le sommeil, la respiration, et l’interaction cardio-respiratoire y sont présentées, résumant l’état actuel des connaissances sur les effets de la pesanteur sur chacun de ces systèmes. Dans le deuxième chapitre, l’expérience “Sleep and Breathing in microgravity”, qui constitue la source des données à la base de ce travail, est présentée en détail. L’étude des signaux de longue durée requiert avant tout de disposer d’outils performants d’analyse des signaux. La première partie de la thèse présente en détail deux algorithmes : un algorithme de détection automatique d’événements respiratoires (inspiration / expiration) basé sur des réseaux neuronaux artificiels, et un algorithme de quantification de l’amplitude et de la phase de l’arythmie sinusale pendant le sommeil, utilisant la méthode des ondelettes. La validation de chaque algorithme est présentée, et leur performance évaluée. Cette partie inclut aussi des courtes introductions théoriques aux réseaux de neurones artificiels ainsi qu’aux méthodes d’analyse temps–fréquence (Fourier et ondelettes). Une approche similaire à celle utilisée pour la détection automatique d’événements respiratoires a été appliquée à la détection d’événements dans des signaux de vitesse du sang dans l’artère cérébrale moyenne, mesures obtenues par Doppler transcrânien. Ceci est le sujet de la thèse annexe. Ces deux algorithmes ont été appliqués aux données expérimentales pour extraire des informations physiologiques quant à l’impact de la pesanteur sur la mécanique respiratoire et l’interaction cardio-respiratoire. Ceci constitue la deuxième partie de la thèse. Un chapitre est consacré aux effets de l’apesanteur sur la mécanique respiratoire pendant le sommeil. Ce chapitre a mis en évidence, pour tous les stades de sommeil, une augmentation de la contribution abdominale en microgravité, suivi d’un retour progressif vers des valeurs observées avant le vol. L’augmentation initiale était attendue, mais l’adaptation progressive observée ne peut pas être expliquée par un effet purement mécanique, et nous suggère la présence d’un mécanisme d’adaptation central. Un deuxième chapitre présente les résultats comparant l’arythmie sinusale pendant le sommeil avant le vol, en apesanteur et après le retour sur terre. Le rythme cardiaque pendant le sommeil dans l’espace présente une moindre variabilité. Les différences NREM–REM observées sur terre pour les influences vagales et sympathiques sont accentuées dans l’espace. Aucun changement significatif n’est présent pour le gain et la différence de phase entre les les signaux cardiaque et respiratoire en comparant le sommeil sur terre et en apesanteur. La dissertation termine par une discussion générale du travail effectué, incluant les prin- cipales conclusions ainsi que les perspectives qui en découlent.

Life would be much easier if there were no typing involved in preparing a document, typing an email, paying online bills, entering credit card details, booking flights, hotels or car rentals online. Imagine a system that would recognize speech and convert it into another form to do these functions automatically. The fact that most people can speak faster than they can type gives a good reason to have a speech recognizer. This thesis concentrates on developing a speaker independent, speech recognizer using Wavelet Packet Transform. Speech corpus in the form of phonemes is collected from an American male and an Indian Female. The subjects chosen for phoneme recognition vary in a number of factors like the accent, gender, age, microphone used to record speech, environment in which phonemes are recorded, etc. These factors increase the complexity of speech recognition. We also assume that the emotions of the speakers are the same and the speakers are stationary while recording phonemes.