abstract: Parkinson's disease is a neurodegenerative condition diagnosed on patients with clinical history and motor signs of tremor, rigidity and bradykinesia, and the estimated number of patients living with Parkinson's disease around the world is seven to ten million. Deep brain stimulation (DBS) provides substantial relief of the motor signs of Parkinson's disease patients. It is an advanced surgical technique that is used when drug therapy is no longer sufficient for Parkinson's disease patients. DBS alleviates the motor symptoms of Parkinson's disease by targeting the subthalamic nucleus using high-frequency electrical stimulation. This work proposes a behavior recognition model for patients with Parkinson's disease. In particular, an adaptive learning method is proposed to classify behavioral tasks of Parkinson's disease patients using local field potential and electrocorticography signals that are collected during DBS implantation surgeries. Unique patterns exhibited between these signals in a matched feature space would lead to distinction between motor and language behavioral tasks. Unique features are first extracted from deep brain signals in the time-frequency space using the matching pursuit decomposition algorithm. The Dirichlet process Gaussian mixture model uses the extracted features to cluster the different behavioral signal patterns, without training or any prior information. The performance of the method is then compared with other machine learning methods and the advantages of each method is discussed under different conditions. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

abstract: Composite materials are increasingly being used in aircraft, automobiles, and other applications due to their high strength to weight and stiffness to weight ratios. However, the presence of damage, such as delamination or matrix cracks, can significantly compromise the performance of these materials and result in premature failure. Structural components are often manually inspected to detect the presence of damage. This technique, known as schedule based maintenance, however, is expensive, time-consuming, and often limited to easily accessible structural elements. Therefore, there is an increased demand for robust and efficient Structural Health Monitoring (SHM) techniques that can be used for Condition Based Monitoring, which is the method in which structural components are inspected based upon damage metrics as opposed to flight hours. SHM relies on in situ frameworks for detecting early signs of damage in exposed and unexposed structural elements, offering not only reduced number of schedule based inspections, but also providing better useful life estimates. SHM frameworks require the development of different sensing technologies, algorithms, and procedures to detect, localize, quantify, characterize, as well as assess overall damage in aerospace structures so that strong estimations in the remaining useful life can be determined. The use of piezoelectric transducers along with guided Lamb waves is a method that has received considerable attention due to the weight, cost, and function of the systems based on these elements. The research in this thesis investigates the ability of Lamb waves to detect damage in feature dense anisotropic composite panels. Most current research negates the effects of experimental variability by performing tests on structurally simple isotropic plates that are used as a baseline and damaged specimen. However, in actual applications, variability cannot be negated, and therefore there is a need to research the effects of complex sample geometries, environmental operating conditions, and the effects of variability in material properties. This research is based on experiments conducted on a single blade-stiffened anisotropic composite panel that localizes delamination damage caused by impact. The overall goal was to utilize a correlative approach that used only the damage feature produced by the delamination as the damage index. This approach was adopted because it offered a simplistic way to determine the existence and location of damage without having to conduct a more complex wave propagation analysis or having to take into account the geometric complexities of the test specimen. Results showed that even in a complex structure, if the damage feature can be extracted and measured, then an appropriate damage index can be associated to it and the location of the damage can be inferred using a dense sensor array. The second experiment presented in this research studies the effects of temperature on damage detection when using one test specimen for a benchmark data set and another for damage data collection. This expands the previous experiment into exploring not only the effects of variable temperature, but also the effects of high experimental variability. Results from this work show that the damage feature in the data is not only extractable at higher temperatures, but that the data from one panel at one temperature can be directly compared to another panel at another temperature for baseline comparison due to linearity of the collected data. / Dissertation/Thesis / M.S. Aerospace Engineering 2012

Les contraintes de rapidité et de discrétion imposées à un système moderne de caractérisation du milieu océanique ont conduit au développement de la tomographie passive, définie comme un moyen discret et rapide d'estimation des paramètres d'un canal acoustique. Ce concept fait appel aux signaux existants dans le milieu et transmis par des sources d'opportunité. Les signaux d'opportunité sont inconnus à la réception mais contiennent des informations relatives aux paramètres physiques du canal défini entre la source et le récepteur. Le travail de recherche présenté dans ce mémoire est d´edié à la caractérisation des milieux sous-marins en utilisant des signaux bioacoustiques d'opportunité (sifflements à modulation fréquentielle). La méconnaissance du signal transmis, de la position et de la vitesse de la source acoustique d'opportunité rendent la tomographie passive difficile à mettre en oeuvre. La propagation dans l'environnement océanique et le mouvement inconnu de la source transforment conjointement les signaux d'opportunité enregistrés. Dans un premier temps, nous présentons de nouvelles méthodes d'estimation simultanée des paramètres environnementaux et des déformations engendrées par le mouvement dans le plan d'ambiguïté large-bande, dans un contexte d'émissions actives (le signal transmis est supposé connu). Ces méthodes, permettant de compenser les effets du mouvement dans les scénarios d'´emissions actives, sont appliquées et validées sur différents jeux de données simulées et réelles enregistrées en mer. Puis nous nous intéressons à la tomographie océanique acoustique passive sur un unique hydrophone. Dans ce contexte, le signal transmis, la position et la vitesse de la source sont entièrement inconnus. A partir des estimateurs développés pour les scénarios d'émissions actives, nous présentons une nouvelle méthodologie permettant d'estimer les paramètres environnementaux en utilisant des vocalises de mammifères marins enregistrées sur un unique hydrophone. Les informations extraites sur les signaux naturels d'opportunité sont ensuite utilisées pour estimer la position puis le vecteur vitesse de la source d'opportunité. Ces méthodes sont appliquées et validées sur différents jeux de données simulées et réelles enregistrées en mer.