This dissertation has been motivated by the increasing application of sensing technologies in structural health monitoring. Many wireless sensor techniques exist for structural health monitoring while a challenge faced is the finite lifetime of batteries. The objective of this dissertation is to develop passive wireless technology to provide early warning of conditions that damage the structure. In this dissertation, sensing mechanism is proposed based on time and frequency domain characteristics of magneto-inductive (MI) waves. Experimental results are also presented to demonstrate the sensing mechanism. MI waves are predominantly magnetic waves that are supported in periodic arrays of magnetically coupled resonators and propagate within a narrow frequency band around the resonant frequency. The array is to be embedded in a structure and different types of transducers can be integrated for different sensing applications. With the onset of structure defect, the transducer introduces an impedance discontinuity that generates reflected MI waves along the array, which are monitored and processed by Smoothed Wigner-Ville distribution (WVD) to extract time-of-flight for frequency components in the narrow passband. The transmission and reflection coefficients of MI waves are also investigated based on the lumped-element circuit model of the array. Based on MI waves travel time, amplitude and group velocity, the position and severity of structure defect are decided. The sensing mechanisms for different distribution of defects are proposed. The validity of the sensing mechanism is examined in experiments. The guided wave testing is implemented in one-dimensional square-shaped printed spiral resonators with Q-factor of 161 at 13.6 MHz. It demonstrates that low MI waves propagation loss is achieved with value of 0.098 dB per element at mid-band with center-to-center distance of half an inch. A pitch-catch measurement system is built to capture traveling MI signal in resonant element and extract group velocity, and a pulse-echo measurement system is designed to monitor reflected MI signal and locate structure discontinuity. In both measurement systems, MI waves are excited with wide bandwidth voltage pulse, and a digitizer is attached to sense the MI signal in a specific resonant element circuit. A baseline signal is obtained from the healthy state to use as reference and comparison with the test case using pitch-catch system. The test signal subtracted from baseline signal infers the structure damage information with time and frequency domain characteristics. It can offer an effective method to estimate the structure discontinuity location, severity and type of damage. The experimental results are consistent with the theoretical predictions. At the end, future directions for the research to integrate with other technologies are suggested. / text

<p>This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.</p><p>In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.</p><p>In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.</p><p>Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.</p><p>We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.</p> / Dissertation

No description available.

No description available.

Afin de satisfaire les exigences de sécurité requises dans l’aviation civile, la stratégie adoptée consiste à multiplier les chaînes de mesure pour une même information. Il est aujourd’hui recommandé d’introduire une chaîne de mesure dissemblable (reposant sur un principe physique différent) afin d’augmenter le niveau de sécurité. Dans cette optique, Thales mène des travaux sur le développement d’un anémomètre laser Doppler embarqué sur aéronef. Ce capteur, composé de quatre axes LiDAR (Light Detection And Ranging) répartis autour de l’avion, permet d’estimer la vitesse air par l’analyse de la réflexion de l’onde laser émise sur les particules présentes dans l’air.L’objectif de ces travaux est de concevoir une chaîne de traitement du signal LiDAR adaptée à un capteur sur avion. Cette chaîne, basée sur une représentation temps-fréquence, inclut des étapes de détection du signal utile optimisée pour les conditions de faible ensemencement en particules, de sélection des aérosols utiles dans un nuage et d’estimation robuste afin de contrôler la qualité de la mesure. Cette chaîne de traitement, évaluée lors d’une campagne d’essais réalisée à l’observatoire du Pic du Midi, apporte un gain de performances élevé dans les situations critiques. L’architecture du système a été le second axe d’étude. Une méthode d’estimation du vecteur vitesse à partir des estimations effectuées sur chaque axe LiDAR et d’un modèle aérodynamique de l’avion permet de compenser les perturbations observées à proximité de ce dernier. Puis, une procédure d’optimisation de l’architecture est proposée afin d’améliorer les performances du capteur. Les performances de la chaîne de traitement présentée devront être évaluées en conditions réelles, lors d’essais en vol, afin de sonder une grande variété de conditions atmosphériques et d’évaluer le gain apporté et les faiblesses éventuelles du traitement proposé en fonction de ces conditions. / The method use to respect security requirements in civil aviation consists in multiplying measuring chains for the same information. Now, it is recommended to add a dissimilar measuring chain, based on a different physical principle, in order to improve security level. Thus, Thales works on the development of a laser Doppler anemometer embedded on aircraft. This sensor is composed by four LiDAR (Light Detection And Ranging) axis distributed around the aircraft and air speed is estimated by the analysis of the reflection of the emitted laser wave on particles.This thesis objective is to design a LiDAR signal processing chain adapted to an aircraft sensor. The process is based on a time-frequency representation and it includes methods for signal detection in low concentrated air mass, useful particles selection in clouds and robust estimation to control measure reliability. The process has been evaluated during a test campaign realized at the Pic du Midi observatory. Its performances are greatly improved, especially in critical situations.The system architecture has also been studied. An estimation method designed from estimations performed on different LiDAR axis and an aerodynamic model of an aircraft is proposed in order to compensate for air mass perturbations close to the aircraft. Then, an optimization process is presented to improve sensor performances.The signal processing chain will have to be evaluated by flight tests, to explore a large atmospheric conditions variety and to quantify its strengths and weaknesses depending on conditions.

[pt] Uma das principais etapas na exploração de petróleo é a definição de um modelo geológico que justifique a existência de uma acumulação de hidrocarbonetos. Ferramentas que possam aumentar o grau de precisão deste modelo são foco de constante estudo na indústria do petróleo. Neste contexto, recentemente, Partyka et al. apresentaram uma nova técnica que utiliza a decomposição espectral dos dados sísmicos para refinar o modelo geológico em termos de definição de espessura de camadas. Nesta pesquisa, essa técnica é estudada e testada, e é proposta a utilização da transformada S, desenvolvida por Stockwell et al., para localizar as componentes de freqüência no domínio do tempo. Os testes realizados com dados sintéticos apontam o uso da técnica de Partyka et al. para fins mais qualitativos, já que, quando os modelos dos testes são perturbados, as análises quantitativas ficam comprometidas. A transformada S mostrou bons resultados na localização das componentes de freqüência no domínio do tempo; no entanto, ela acarreta a suavização do espectro de amplitudes. Ao final deste trabalho é apresentado um exemplo da utilização da técnica em dados reais tridimensionais. / [en] One of the main steps in oil exploration is the definition of a geological model that can justify the existence of a hydrocarbon accumulation. Tools that can improve the precision of this geological model are a constant goal of the oil industry research. In this context, Partyka et al. have recently presented a new technique that uses spectral seismic data decomposition in order to improve the model's accuracy in terms of the thickness definition of geological layers. In the present research, this technique is studied and tested, and a use for the S transform is proposed to locate the frequency components in the time domain. The S transform was recently developed by Stockwell et al. The tests performed with synthetic data indicate that the technique developed by Partyka et al. provide a better qualitative response, because, when the models in the tests are disturbed, qualitative analyses are compromised. The S transform showed good results in locating the frequency components in the time domain, but it smoothes the amplitude spectrum. At the end of this work, an example of the use of this technique with real three-dimensional data is presented.

The radar transmitter identification problem involves the identification of a specific radar transmitter based on a received pulse. The radar transmitters are of identical make and model. This makes the problem challenging since the differences between radars of identical make and model will be solely due to component tolerances and variation. Radar pulses also vary in time and frequency which means that the problem is non-stationary. Because of this fact, time-frequency representations such as shift-invariant quadratic time-frequency representations (Cohen’s class) and wavelets were used. A model for a radar transmitter was developed. This consisted of an analytical solution to a pulse-forming network and a linear model of an oscillator. Three signal classification algorithms were developed. A signal classifier was developed that used a radially Gaussian Cohen’s class transform. This time-frequency representation was refined to increase the classification accuracy. The classification was performed with a support vector machine classifier. The second signal classifier used a wavelet packet transform to calculate the feature values. The classification was performed using a support vector machine. The third signal classifier also used the wavelet packet transform to calculate the feature values but used a Universum type classifier for classification. This classifier uses signals from the same domain to increase the classification accuracy. The classifiers were compared against each other on a cubic and exponential chirp test problem and the radar transmitter model. The classifier based on the Cohen’s class transform achieved the best classification accuracy. The classifier based on the wavelet packet transform achieved excellent results on an Electroencephalography (EEG) test dataset. The complexity of the wavelet packet classifier is significantly lower than the Cohen’s class classifier. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / unrestricted

No description available.

Nos travaux de recherches présentés dans ce manuscrit ont pour objectif, l'optimisation des performances des algorithmes de suivi des formants. Pour ce faire, nous avons commencé par l'analyse des différentes techniques existantes utilisées dans le suivi automatique des formants. Cette analyse nous a permis de constater que l'estimation automatique des formants reste délicate malgré l'emploi de diverses techniques complexes. Vue la non disponibilité des bases de données de référence en langue arabe, nous avons élaboré un corpus phonétiquement équilibré en langue arabe tout en élaborant un étiquetage manuel phonétique et formantique. Ensuite, nous avons présenté nos deux nouvelles approches de suivi de formants dont la première est basée sur l'estimation des crêtes de Fourier (maxima de spectrogramme) ou des crêtes d'ondelettes (maxima de scalogramme) en utilisant comme contrainte de suivi le calcul de centre de gravité de la combinaison des fréquences candidates pour chaque formant, tandis que la deuxième approche de suivi est basée sur la programmation dynamique combinée avec le filtrage de Kalman. Finalement, nous avons fait une étude exploratrice en utilisant notre corpus étiqueté manuellement comme référence pour évaluer quantitativement nos deux nouvelles approches par rapport à d'autres méthodes automatiques de suivi de formants. Nous avons testé la première approche par détection des crêtes ondelette, utilisant le calcul de centre de gravité, sur des signaux synthétiques ensuite sur des signaux réels de notre corpus étiqueté en testant trois types d'ondelettes complexes (CMOR, SHAN et FBSP). Suite à ces différents tests, il apparaît que le suivi de formants et la résolution des scalogrammes donnés par les ondelettes CMOR et FBSP sont meilleurs qu'avec l'ondelette SHAN. Afin d'évaluer quantitativement nos deux approches, nous avons calculé la différence moyenne absolue et l'écart type normalisée. Nous avons fait plusieurs tests avec différents locuteurs (masculins et féminins) sur les différentes voyelles longues et courtes et la parole continue en prenant les signaux étiquetés issus de la base élaborée comme référence. Les résultats de suivi ont été ensuite comparés à ceux de la méthode par crêtes de Fourier en utilisant le calcul de centre de gravité, de l'analyse LPC combinée à des bancs de filtres de Mustafa Kamran et de l'analyse LPC dans le logiciel Praat. D'après les résultats obtenus sur les voyelles /a/ et /A/, nous avons constaté que le suivi fait par la méthode ondelette avec CMOR est globalement meilleur que celui des autres méthodes Praat et Fourier. Cette méthode donne donc un suivi de formants (F1, F2 et F3) pertinent et plus proche de suivi référence. Les résultats des méthodes Fourier et ondelette sont très proches dans certains cas puisque toutes les deux présentent moins d'erreurs que la méthode Praat pour les cinq locuteurs masculins ce qui n'est pas le cas pour les autres voyelles où il y a des erreurs qui se présentent parfois sur F2 et parfois sur F3. D'après les résultats obtenus sur la parole continue, nous avons constaté que dans le cas des locuteurs masculins, les résultats des deux nouvelles approches sont notamment meilleurs que ceux de la méthode LPC de Mustafa Kamran et ceux de Praat même si elles présentent souvent quelques erreurs sur F3. Elles sont aussi très proches de la méthode par détection de crêtes de Fourier utilisant le calcul de centre de gravité. Les résultats obtenus dans le cas des locutrices féminins confirment la tendance observée sur les locuteurs / Our research work presented in this thesis aims the optimization of the performance of formant tracking algorithms. We began by analyzing different existing techniques used in the automatic formant tracking. This analysis showed that the automatic formant estimation remains difficult despite the use of complex techniques. For the non-availability of database as reference in Arabic, we have developed a phonetically balanced corpus in Arabic while developing a manual phonetic and formant tracking labeling. Then we presented our two new automatic formant tracking approaches which are based on the estimation of Fourier ridges (local maxima of spectrogram) or wavelet ridges (local maxima of scalogram) using as a tracking constraint the calculation of center of gravity of a set of candidate frequencies for each formant, while the second tracking approach is based on dynamic programming combined with Kalman filtering. Finally, we made an exploratory study using manually labeled corpus as a reference to quantify our two new approaches compared to other automatic formant tracking methods. We tested the first approach based on wavelet ridges detection, using the calculation of the center of gravity on synthetic signals and then on real signals issued from our database by testing three types of complex wavelets (CMOR, SHAN and FBSP). Following these tests, it appears that formant tracking and scalogram resolution given by CMOR and FBSP wavelets are better than the SHAN wavelet. To quantitatively evaluate our two approaches, we calculated the absolute difference average and standard deviation. We made several tests with different speakers (male and female) on various long and short vowels and continuous speech signals issued from our database using it as a reference. The formant tracking results are compared to those of Fourier ridges method calculating the center of gravity, LPC analysis combined with filter banks method of Kamran.M and LPC analysis integrated in Praat software. According to the results of the vowels / a / and / A /, we found that formant tracking by the method with wavelet CMOR is generally better than other methods. Therefore, this method provides a correct formant tracking (F1, F2 and F3) and closer to the reference. The results of Fourier and wavelet methods are very similar in some cases since both have fewer errors than the method Praat. These results are proven for the five male speakers which is not the case for the other vowels where there are some errors which are present sometimes in F2 and sometimes in F3. According to the results obtained on continuous speech, we found that in the case of male speakers, the result of both approaches are particularly better than those of Kamran.M method and those of Praat even if they are often few errors in F3. They are also very close to the Fourier ridges method using the calculation of center of gravity. The results obtained in the case of female speakers confirm the trend observed over the male speakers

Cette thèse propose de nouvelles méthodes d'analyse d'enregistrements cérébraux intra-crâniens (potentiels de champs locaux), qui pallie les lacunes de la méthode temps-fréquence standard d'analyse des perturbations spectrales événementielles : le calcul d'une moyenne sur les enregistrements et l'emploi de l'activité dans la période pré-stimulus. La première méthode proposée repose sur la détection de sous-ensembles d'électrodes dont l'activité présente des synchronisations cooccurrentes en un même point du plan temps-fréquence, à l'aide de modèles bayésiens de mélange gaussiens. Les sous-ensembles d'électrodes pertinents sont validés par une mesure de stabilité calculée entre les résultats obtenus sur les différents enregistrements. Pour la seconde méthode proposée, le constat qu'un bruit blanc dans le domaine temporel se transforme en bruit ricien dans le domaine de l'amplitude d'une transformée temps-fréquence a permis de mettre au point une segmentation du signal de chaque enregistrement dans chaque bande de fréquence en deux niveaux possibles, haut ou bas, à l'aide de modèles bayésiens de mélange ricien à deux composantes. À partir de ces deux niveaux, une analyse statistique permet de détecter des régions temps-fréquence plus ou moins actives. Pour développer le modèle bayésien de mélange ricien, de nouveaux algorithmes d'inférence bayésienne variationnelle ont été créés pour les distributions de Rice et de mélange ricien. Les performances des nouvelles méthodes ont été évaluées sur des données artificielles et sur des données expérimentales enregistrées sur des singes. Il ressort que les nouvelles méthodes génèrent moins de faux-positifs et sont plus robustes à l'absence de données dans la période pré-stimulus / This thesis promotes new methods to analyze intracranial cerebral signals (local field potentials), which overcome limitations of the standard time-frequency method of event-related spectral perturbations analysis: averaging over the trials and relying on the activity in the pre-stimulus period. The first proposed method is based on the detection of sub-networks of electrodes whose activity presents cooccurring synchronisations at a same point of the time-frequency plan, using bayesian gaussian mixture models. The relevant sub-networks are validated with a stability measure computed over the results obtained from different trials. For the second proposed method, the fact that a white noise in the temporal domain is transformed into a rician noise in the amplitude domain of a time-frequency transform made possible the development of a segmentation of the signal in each frequency band of each trial into two possible levels, a high one and a low one, using bayesian rician mixture models with two components. From these two levels, a statistical analysis can detect time-frequency regions more or less active. To develop the bayesian rician mixture model, new algorithms of variational bayesian inference have been created for the Rice distribution and the rician mixture distribution. Performances of the new methods have been evaluated on artificial data and experimental data recorded on monkeys. It appears that the new methods generate less false positive results and are more robust to a lack of data in the pre-stimulus period