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A methodology to measure a characteristic time scale (duration) of peaks in pressure and velocity data is presented. This methodology is based on the use of the Morlet and Paul wavelets. Detailed descriptions of these wavelets and their implementation procedures are given. The results show that similar time scales or durations can be measured using either Morlet or Paul wavelets. To obtain consistent results data windowing might need to be applied. Using the Paul wavelet, durations of events measured in different wind tunnel simulations are obtained and discussed. / Master of Science

Um tópico importante na análise de seqüências biológicas é a busca de genes, ou seja, a identificação de regiões codificantes de proteínas. Esta identificação permite a posterior procura de significado, descrição ou categorização biológica do organismo analisado. Atualmente, vários métodos combinam reconhecimento de padrões com conhecimento coletado de conjuntos de treinamento ou de comparações com banco de dados genômicos. Entretanto, a acurácia desses métodos está ainda longe do satisfatório. Novos métodos de processamento de seqüências de DNA e de identificação de genes podem ser criados através da busca por conteúdo (search-by-content). O padrão periódico de DNA em regiões codificantes de proteína, denominada periodicidade de três bases, vem sendo considerado uma propriedade dessas regiões. As técnicas de processamento digital de sinais fornecem uma base robusta para a identificação de regiões com periodicidade de três bases. Nesta dissertação, são apresentados um \\pipeline, os conceitos básicos da identificação genômica, e métodos de processamento digital de sinais utilizados para a identificação de regiões codificantes de proteínas. Introduzimos um novo método para a identificação dessas regiões, baseado na transformada proposta, denominada Transformada Modificada de Morlet. Apresentamos vários resultados experimentais obtidos a partir de seqüências de DNA sintéticas e reais. As principais contribuições do trabalho consistem no desenvolvimento de um pipeline para projetos genoma e na criação de um método de identificação de regiões codificantes onde a periodicidade de três bases seja latente. O método apresenta desempenho superior e vantagens importantes em comparação ao método tradicional baseado na transformada de Fourier de tempo reduzido. / An important topic in biological sequences analysis is gene finding, i.e. the identification of protein coding regions. This identification allows the posterior research for meaning, description or biological categorization of the analyzed organism. Currently, several methods combine pattern recognition with knowledge collected from training datasets or from comparison with genomic databases. Nonetheless, the accuracy of these methods is still far from satisfactory. New methods of DNA sequences processing and genes identification can be created through search-by-content such sequences. The periodic pattern of DNA in protein coding regions, called three-base periodicity, has been considered proper of coding regions. Digital signal processing techniques supply a strong basis for regions identification with three-base periodicity. In this work, we present a bioinformatics pipeline, basic concepts of the genomic identification and digital signal processing methods used for protein coding regions identification. We introduce a new method for identification of these regions, based on a newly proposed transform, called Modified Morlet Transform. We present some obtained experimental results from synthetic and real DNA sequences. The main contributions consist of the bioinformatics pipeline development for genoma projects and the creation of a method for protein coding regions identification where the three-base periodicity is latent. The method presents superior performance and important advantages in comparison to traditional method based on the short time Fourier transform.

Τα συστήματα διεπαφών ανθρώπινου εγκεφάλου-υπολογιστή (BCIs: Brain-Computer Interfaces) απαιτούν την πραγματικού χρόνου, αποτελεσματική επεξεργασία των μετρήσεων των ηλεκτροεγκεφαλογραφικών (ΗΕΓ) σημάτων του χρήστη τους, προκειμένου να μεταφράσουν τις νοητικές διεργασίες/προθέσεις του σε σήματα ελέγχου εξωτερικών διατάξεων ή συστημάτων. Στο πλαίσιο της εργασίας αυτής μελετήθηκε το θεωρητικό υπόβαθρο του προβλήματος και αναλύθηκαν συνοπτικά οι κυριότερες τεχνικές που χρησιμοποιούνται σήμερα. Επιπρόσθετα, παρουσιάστηκε μία μέθοδος ταξινόμησης των νοητικών προθέσεων της αριστερής και δεξιάς κίνησης των χεριών ενός χρήστη η οποία εφαρμόστηκε σε πραγματικά ιατρικά δεδομένα. Η εξαγωγή των χαρακτηριστικών που διαφοροποιούνται μεταξύ των δύο καταστάσεων βασίστηκε σε πληροφορίες του πεδίου χρόνου-συχνότητας, οι οποίες αντλούνται με το φιλτράρισμα των ακατέργαστων ΗΕΓ δεδομένων και με τη βοήθεια των αιτιατών κυματιδίων Morlet, ενώ για την επακόλουθη ταξινόμηση των χαρακτηριστικών αναπτύχθηκαν και συγκρίθηκαν δύο αξιόπιστες μέθοδοι. Η πρώτη αφορά στη δημιουργία πιθανοθεωρητικών προτύπων κανονικής κατανομής για κάθε κατηγορία πρόθεσης κίνησης, με την τελική απόφαση ταξινόμησης να λαμβάνεται με εφαρμογή του απλού ταξινομητή του Bayes, ενώ η δεύτερη δημιουργεί ένα πρότυπο ταξινόμησης με βάση το θεωρητικό πλαίσιο των Μηχανών Διανυσμάτων Υποστήριξης (SVM). Στόχος του προβλήματος της δυαδικής ταξινόμησης είναι να αποφασίζεται σε ποια από τις δύο κατηγορίες ανήκει μία δεδομένη νοητική πρόθεση όσο το δυνατόν ταχύτερα και αξιόπιστα, έτσι ώστε ο σχεδιαζόμενος αλγόριθμος να εξυπηρετήσει ένα πλαίσιο ανατροφοδότησης της τελικής απόφασης στο χρήστη σε συνθήκες πραγματικού χρόνου. / Brain-Computer Interfaces (BCIs) demand the efficient processing of EEG data in order to translate one's thought or wish into a control signal that can be applied as input to external devices. Here we present a method to classify left from right hand movements, by extracting features from the data with Morlet wavelets and classifying with two different models, SVMs and Naive Bayes Classifier.

Um tópico importante na análise de seqüências biológicas é a busca de genes, ou seja, a identificação de regiões codificantes de proteínas. Esta identificação permite a posterior procura de significado, descrição ou categorização biológica do organismo analisado. Atualmente, vários métodos combinam reconhecimento de padrões com conhecimento coletado de conjuntos de treinamento ou de comparações com banco de dados genômicos. Entretanto, a acurácia desses métodos está ainda longe do satisfatório. Novos métodos de processamento de seqüências de DNA e de identificação de genes podem ser criados através da busca por conteúdo (search-by-content). O padrão periódico de DNA em regiões codificantes de proteína, denominada periodicidade de três bases, vem sendo considerado uma propriedade dessas regiões. As técnicas de processamento digital de sinais fornecem uma base robusta para a identificação de regiões com periodicidade de três bases. Nesta dissertação, são apresentados um \\pipeline, os conceitos básicos da identificação genômica, e métodos de processamento digital de sinais utilizados para a identificação de regiões codificantes de proteínas. Introduzimos um novo método para a identificação dessas regiões, baseado na transformada proposta, denominada Transformada Modificada de Morlet. Apresentamos vários resultados experimentais obtidos a partir de seqüências de DNA sintéticas e reais. As principais contribuições do trabalho consistem no desenvolvimento de um pipeline para projetos genoma e na criação de um método de identificação de regiões codificantes onde a periodicidade de três bases seja latente. O método apresenta desempenho superior e vantagens importantes em comparação ao método tradicional baseado na transformada de Fourier de tempo reduzido. / An important topic in biological sequences analysis is gene finding, i.e. the identification of protein coding regions. This identification allows the posterior research for meaning, description or biological categorization of the analyzed organism. Currently, several methods combine pattern recognition with knowledge collected from training datasets or from comparison with genomic databases. Nonetheless, the accuracy of these methods is still far from satisfactory. New methods of DNA sequences processing and genes identification can be created through search-by-content such sequences. The periodic pattern of DNA in protein coding regions, called three-base periodicity, has been considered proper of coding regions. Digital signal processing techniques supply a strong basis for regions identification with three-base periodicity. In this work, we present a bioinformatics pipeline, basic concepts of the genomic identification and digital signal processing methods used for protein coding regions identification. We introduce a new method for identification of these regions, based on a newly proposed transform, called Modified Morlet Transform. We present some obtained experimental results from synthetic and real DNA sequences. The main contributions consist of the bioinformatics pipeline development for genoma projects and the creation of a method for protein coding regions identification where the three-base periodicity is latent. The method presents superior performance and important advantages in comparison to traditional method based on the short time Fourier transform.

An experimental investigation of the interaction of water waves with flexible structures acting as breakwaters was carried out. Wave profiles, mapped out by water level measuring transducers, were studied to provide information on the performance of different breakwater models. A new signal analysis procedure for determining reflection coefficients based on wavelet theory was developed and compared to a conventional method. The reliability of using wavelet analysis to separate a partial standing wave into incident and reflected wave components was verified with a numerical example. It was also verified by the small variance in the estimates of the incident wave height from independent experimental measurements. Different geometries of rigid and flexible structures were constructed and examined. Reflection, transmission and energy loss coefficients were obtained over them. The influence of various properties of the models, such as the width and the internal pressure, on the effectiveness in reflecting or absorbing the incident wave energy was determined. Various factors which affect the performance of the breakwater, including the water depth, the wave length and the wave amplitude, were measured and documented. Suspended and bottom-mounted models were considered. The flow field over and near a hemi-cylindrical breakwater model was also examined using a flow visualization technique. An overall comparison among the models has also been provided. The results showed that the rectangular models, rigid and flexible, are the most effective structures to dissipate wave energy. The flow visualization technique indicated that the flow conforms with the circular geometry of a hemi-cylindrical breakwater model, yielding no flow separation. / Ph. D.

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)

La première partie de ce mémoire présente une nouvelle vision de l'estimation de mouvement, et donc de la compression, dans les séquences<br />vidéo. D'une part, nous avons choisi d'aborder l'estimation de mouvement à partir de familles d'ondelettes redondantes adaptées à différentes<br />transformations, dont, plus particulièrement, la vitesse. Ces familles, très peu connues, ont déjà été étudiées dans le cadre de la poursuite de<br />cibles. D'autre part, les standards de compression actuels comme MPEG4 prennent en compte une compression objet mais ne calculent toujours que de<br />simples vecteurs de mouvements de ``blocs''. Il nous a paru intéressant de chercher à mettre en oeuvre ces familles d'ondelettes car 1)<br />elle sont construites pour le calcul de paramètres sur plusieurs types de mouvement (rotation, vitesse, accélération) et 2) nous<br />pensons qu'une approche de l'estimation basée sur l'identification de trajectoires d'objets dans une scène est une solution intéressante pour les<br />méthodes futures de compression. En effet nous pensons que l'analyse et la compréhension des mouvements dans une scène est une voie pour des méthodes<br />de compression ``contextuelles'' performantes.<br /><br /><br /><br />La seconde partie présente deux développements concernant la segmentation non-supervisée dans une approche bayésienne. Le premier, destiné à réduire<br />les temps de calcul dans la segmentation de séquences vidéo, est basé sur une mise en oeuvre itérative, simple, de la segmentation. Il nous a aussi<br />permis de mettre une estimation de mouvement basée sur une segmentation ``région'' (voire objet). Le second est destiné à diminuer les temps de<br />segmentation d'images fixes en réalisant la segmentation dans le domaine des ondelettes. Ces deux développements sont basés sur une approche par<br />estimation bayésienne utilisant un modèle de champ aléatoire de Potts-Markov (PMRF) pour les étiquettes des pixels, dans le domaine direct, et pour<br />les coefficients d'ondelettes. Il utilise aussi un algorithme itératif de type MCMC (Markov Chain Monte Carlo) avec échantillonneur de Gibbs.<br />L'approche initiale, directe, utilise un modèle de Potts avec voisinage d'ordre un. Nous avons développé le modèle de Potts pour l'adapter à des<br />voisinages convenant aux orientations privilégiées des sous-bandes d'ondelettes. Ces réalisations apportent, à notre connaissance, des approches<br />nouvelles dans les méthodes de segmentation<br />non-supervisées.