The application of the linear prediction Model for speech waveform analysis to context-independent automatic speaker recognition is explored, primarily in terns of the parametric sensitivity of the model. Feature vectors to characterize speakers are formed from linear prediction speech parameters computed as inverse filter coefficients, reflection coefficients or cepstral coefficients, and also power spectrum parameters via Fast Fourier Transform coefficients. The comparative performance of these parameters is investigated in speaker recognition experiments. The stability of the linear prediction parameters is tested over a range of model order from p=6 to p=30. Two independent speech databases are used to substantiate the experimental results. The quality of the automatic recognition technique is assessed in a novel experiment based on a direct performance comparison with the human skill of aural recognition. Correlation is sought between the performance of the aural and automatic recognition methods, for each of the four parameter sets. Although the recognition accuracy of the automatic system is superior to that of the direct aural technique, the error distributions are highly variable. The performance of the automatic system is shown to be empirically based and unlike the intuitive human process. An extended preamble to the description of the experiments reviews the current art of automatic speaker recognition, with a critical consideration of the performance of linear prediction techniques. As supported by our experimental results, it is concluded that success in the laboratory rests upon a rather fragile foundation. Application to problems beyond the controlled laboratory environment is seen, therefore, to be still more precarious.

Radio Frequency Machine Learning (RFML) is the application of ML techniques to solve problems in the RF domain as an alternative to traditional digital-signal processing (DSP) techniques. Notable among these are the tasks of specific emitter identification (SEI), determining source identity of a received RF signal, and automated modulation classification (AMC), determining the modulation scheme of a received RF transmission. Both tasks have a number of algorithms that are effective on simulated data, but struggle to generalize to data collected in the real-world, partially due to the lack of available datasets upon which to train models and understand their limitations. This thesis covers the practical considerations for systems that can create high-quality datasets for RFML tasks, how variances from real-world effects in these datasets affect RFML algorithm performance, and how well models developed from these datasets are able to generalize and adapt across different receiver hardware platforms. Moreover, this thesis presents a proof-of-concept system for large-scale and efficient data generation, proven through the design and implementation of a custom platform capable of coordinating transmissions from nearly a hundred Software-Defined Radios (SDRs). This platform was used to rapidly perform experiments in both RFML performance sensitivity analysis and successful transfer between SDRs of trained models for both SEI and AMC algorithms. / Master of Science / Radio Frequency Machine Learning (RFML) is the application of machine learning techniques to solve problems having to do with radio signals as an alternative to traditional signal processing techniques. Notable among these are the tasks of specific emitter identification (SEI), determining source identity of a received signal, and automated modulation classification (AMC), determining the data encoding format of a received RF transmission. Both tasks have practical limitations related to the real-world collection of RF training data. This thesis presents a proof-of-concept for large-scale, efficient data generation and management, as proven through the design and construction of a custom platform capable of coordinating transmissions from nearly a hundred radios. This platform was used to rapidly perform experiments in both RFML performance sensitivity analysis and successful cross-radio transfer of trained behaviors.

O presente trabalho descreve a aplicação de um método de ajuste de modelo, com base em resultados experimentais obtidos em túnel de vento, a uma aeronave com sistema de aumento de estabilidade longitudinal (LSAS). O estudo inclui uma revisão de métodos para ajuste de modelos, o desenvolvimento do modelo matemático da aeronave e uma descrição dos ensaios em túnel de vento da aeronave com o LSAS. O sistema automático de controle é composto de (1) um sistema de aquisição de dados, que processa o sinal do sensor e envia um sinal de comando para o atuador; (2) um potenciômetro, usado como sensor de ângulo de arfagem; e (3) um servo motor, usado como atuador do canard. O modelo de aeronave é baseado no Grumman X-29, que tem asa de enflechamento negativo e canard. Sua margem de estabilidade estática pode ser ajustada mudando a posição do centro de rotação que, por sua vez, coincide com a posição do centro de gravidade da aeronave através de balanceamento do peso. O ajuste do modelo matemático do avião é conduzido, no ambiente Matlab/Simulink, com a modificação dos parâmetros das derivadas de estabilidade da aeronave, do filtro digital e da dinâmica do sensor e do atuador. O objetivo é obter uma correlação ótima entre resultados teóricos e experimentais. O método da análise da sensibilidade paramétrica é escolhido para o ajuste do modelo. Numa primeira fase do estudo, a comparação entre resultados experimentais e numéricos é feita com base nas freqüências e razões de amortecimento da variação do ângulo de arfagem em resposta a uma entrada do tipo impulso de deflexão do canard. Numa segunda fase a comparação é baseada diretamente na resposta no tempo do ângulo de arfagem numérico e experimental para a mesma entrada impulso do canard. Três posições do centro de gravidade são analisadas, uma em que a aeronave é estaticamente estável e duas em que ela é instável. Os resultados mostram grande variação dos parâmetros ajustados indicando a necessidade de aperfeiçoamento na implementação da metodologia utilizada. / The present work describes the application of a model updating method, based on experimental wind tunnel data to an aircraft longitudinal stability augmentation system (LSAS). The study includes a revision of model updating methods, the development of the aircraft mathematical model and the description of a previously conducted, aircraft LSAS wind tunnel testing. The LSAS is comprised by (1) a data acquisition system, which processes the sensor signal and sends the control command to the actuator; (2) a potentiometer, used as a pitch angle sensor; and (3) a servo motor, used to actuate canard deflection. The aircraft model is based on the Grumman X-29, which has canard and forward swept wing. Its static stability margin can be adjusted by changing the center of rotation position which, in turn, coincides with the aircraft center of gravity position through weight balance. The airplane mathematical model updating is carried out, in the Matlab/Simulink environment, by adjusting model parameters for aircraft stability derivatives, digital filter, sensor and servo dynamics. The objective is to obtain an optimal correlation between numerical and experimental results. The parametric sensitivity analysis method is chosen for model updating. In a first phase of the study the comparison between theoretical and experimental results is based on frequencies and damping ratios for aircraft pitch angle response to an impulse canard deflection input. In a second phase the comparison is based directly on experimental and numerical pitch angle time response to the same impulse canard deflection input. Three center of gravity positions are analyzed, one for which the aircraft is statically stable and two for which it is unstable. Results show large variations among adjusted parameters indicating the need for improvements in the implementation of the adopted methodology.

O presente trabalho descreve a aplicação de um método de ajuste de modelo, com base em resultados experimentais obtidos em túnel de vento, a uma aeronave com sistema de aumento de estabilidade longitudinal (LSAS). O estudo inclui uma revisão de métodos para ajuste de modelos, o desenvolvimento do modelo matemático da aeronave e uma descrição dos ensaios em túnel de vento da aeronave com o LSAS. O sistema automático de controle é composto de (1) um sistema de aquisição de dados, que processa o sinal do sensor e envia um sinal de comando para o atuador; (2) um potenciômetro, usado como sensor de ângulo de arfagem; e (3) um servo motor, usado como atuador do canard. O modelo de aeronave é baseado no Grumman X-29, que tem asa de enflechamento negativo e canard. Sua margem de estabilidade estática pode ser ajustada mudando a posição do centro de rotação que, por sua vez, coincide com a posição do centro de gravidade da aeronave através de balanceamento do peso. O ajuste do modelo matemático do avião é conduzido, no ambiente Matlab/Simulink, com a modificação dos parâmetros das derivadas de estabilidade da aeronave, do filtro digital e da dinâmica do sensor e do atuador. O objetivo é obter uma correlação ótima entre resultados teóricos e experimentais. O método da análise da sensibilidade paramétrica é escolhido para o ajuste do modelo. Numa primeira fase do estudo, a comparação entre resultados experimentais e numéricos é feita com base nas freqüências e razões de amortecimento da variação do ângulo de arfagem em resposta a uma entrada do tipo impulso de deflexão do canard. Numa segunda fase a comparação é baseada diretamente na resposta no tempo do ângulo de arfagem numérico e experimental para a mesma entrada impulso do canard. Três posições do centro de gravidade são analisadas, uma em que a aeronave é estaticamente estável e duas em que ela é instável. Os resultados mostram grande variação dos parâmetros ajustados indicando a necessidade de aperfeiçoamento na implementação da metodologia utilizada. / The present work describes the application of a model updating method, based on experimental wind tunnel data to an aircraft longitudinal stability augmentation system (LSAS). The study includes a revision of model updating methods, the development of the aircraft mathematical model and the description of a previously conducted, aircraft LSAS wind tunnel testing. The LSAS is comprised by (1) a data acquisition system, which processes the sensor signal and sends the control command to the actuator; (2) a potentiometer, used as a pitch angle sensor; and (3) a servo motor, used to actuate canard deflection. The aircraft model is based on the Grumman X-29, which has canard and forward swept wing. Its static stability margin can be adjusted by changing the center of rotation position which, in turn, coincides with the aircraft center of gravity position through weight balance. The airplane mathematical model updating is carried out, in the Matlab/Simulink environment, by adjusting model parameters for aircraft stability derivatives, digital filter, sensor and servo dynamics. The objective is to obtain an optimal correlation between numerical and experimental results. The parametric sensitivity analysis method is chosen for model updating. In a first phase of the study the comparison between theoretical and experimental results is based on frequencies and damping ratios for aircraft pitch angle response to an impulse canard deflection input. In a second phase the comparison is based directly on experimental and numerical pitch angle time response to the same impulse canard deflection input. Three center of gravity positions are analyzed, one for which the aircraft is statically stable and two for which it is unstable. Results show large variations among adjusted parameters indicating the need for improvements in the implementation of the adopted methodology.

Ce travail de thèse a pour finalité de présenter différentes méthodologies pour l’analyse de réseaux à courant continue haut tension (HVDC). La théorie fondamentale de l‘analyse de sensitivité s’applique sur l’analyse des systèmes électriques de puissance dans les différentes étapes de la production et la transmission de l’énergie. Des outils de bases concernant cette théorie sont devenus populaires et très utilisés.En pratique, les facteurs de participation représentent un exemple de ces outils, utilisés depuis longtemps. Ils ont été proposés par (Perez-Arriaga et al., 1982). Le but des facteurs de participation est la réduction des modèles des systèmes ayant une dynamique particulière comme celle des systèmes électromécaniques. Aussi, les facteurs de participation sont utilisés pour comprendre les relations entre les variables d’état et les valeurs propres des systèmes. Nous présentons la théorie fondamentale de la sensitivité analytique dans laquelle les facteurs de participation sont basses. Pour une bonne compréhension de ces éléments de base de cette théorie, quelques exemples sont présentés. Nous présentons une formulation différente pour l’analyse par sensitivité paramétrique (Barrera Gallegos et al., 2016).Enfin, les deux méthodologies sur des exemples des réseaux HVDC. Notre comparaison permet d’exposer les limites de l’utilisation des facteurs de participation sur des réseaux HVDC.En conclusion, la nouvelle méthodologie est plus générale comparée aux facteurs de participation. Egalement, la nouvelle méthodologie, par sensitivité paramétrique, nous donne plus d’informations sur les caractéristiques dynamiques des réseaux HVDC. / The work presented in this thesis presents different methodology for parametric sensitivity of high voltage dc networks(HVDC).The fundamental theory of modal analysis has been applied for analysis of the power electrical systems in its different stages of production and transmission of energy. Tools derived from these fundamentals have become popular with its use. Among the tools used in dynamic analysis, participation factors have been used for a long time. Proposed by (Perez-Arriaga et al., 1982), they give a metric for relating states and eigenvalues of a system. The objective of the participation factors is to analyze systems with particular dynamics such as electromechanical systems. The participation factors is a tool that helps in the reduction of systems. Firstly, we present the fundaments of the sensitivity analysis upon which the participation factors are based on. The principle is illustrated with several examples.We propose a new formulation for sensitivity analysis using parametric sensitivity (Barrera Gallegos et al., 2016).In the latter, the application of participation factors and parametric sensitivity analysis is performed using HVDC networks. This comparison exposes the limitation of the participation factors for the general analysis of HVDC grids.In conclusion, the new methodology is a better and general alternative compared to traditional participation factors employed for analysis of HVDC grids. In addition, the new technique of parametric sensitivity produces several novel information related to the dynamic characteristics of the HVDC grid.

L'objectif principal de ce travail a été d'apporter une nouvelle contribution quant à l'approche de contrôle optimal pendant la phase d'identification. Il s'agissait de trouver la commande à appliquer pendant l'expérience qui permet d'optimiser un critère qui est fonction des sensibilités des sorties par rapport aux paramètres du modèle à identifier. Cette approche couplant contrôleur prédictif sous contraintes et estimateur a résolu en ligne le problème d'identification à chaque instant en utilisant l'observateur. En ce sens, c'est une approche permettant d'automatiser et d'optimiser le design d'expérience, tout en réalisant conjointement l'identification d'un paramètre du modèle spécifié. L'aspect temps réel a été pris en compte dans la formulation de la solution apportée. Dans ce contexte, nous avons introduit deux stratégies de commande pour l'identification optimale. La première était basée sur un modèle de prédiction non linéaire et la seconde sur un modèle linéaire temps variant. Si le temps devient un paramètre critique pour l'implémentation de l'approche, cette dernière vise à réduire le temps alloué à l'optimisation. L'approche d'identification optimale en ligne a été appliquée à deux problèmes concrets du Génie des Procédés (réaction de saponification et cuisson de peintures). Ces exemples ont permis de vérifier en simulation, l'efficacité et la faisabilité de cette approche. / The main aim of this work is to give a new approach of optimal control during the phase of identification. The question is how to tune the control action to be applied during the experiment optimize a criterion which is function of the sensitivities of the mesure with respect to the parameters of the model to be identified. This approach coupling constrained predictive controller and estimator solves on line the problem of identification by using the observer. In that sense, it is an approach allowing an optimal and automatic design of experiment, while performing at the same time the identification of one parameter of the specified model. The real time aspect was taken into account in the formulation of the solution. In this context, we introduced two strategies for optimal identification : the first one is based on a nonlinear model of prediction and the second one is based on a linear time varying model that may be used if the real time aspect becomes a critical parameter for the implementation of the approach. This approach of on line optimal identification was applied on two concrete problems in Chemical Engineering. These examples show the performance and the feasibility of this approach.

Ce mémoire de thèse est formé de quatre chapitres. Un premier chapitre présente les différentes notions et outils mathématiques utilisés dans le corps de la thèse ainsi qu’une description des résultats principaux que nous avons obtenus. Le second chapitre présente une généralisation d’un résultat obtenu par Rousselet-Chénais en 1990 qui décrit la sensibilité des sous-espaces propres d’opérateurs compacts auto-adjoints. Rousselet-Chénais se sont limités aux sous-espaces propres de dimension 1 et nous avons étendu leur résultat aux dimensions supérieures. Nous avons appliqué nos résultats à la Décomposition par Projection Orthogonale (POD) dans le cas de variation paramétrique, temporelle ou spatiale (Gappy-POD). Le troisième chapitre traite de l’estimation du flot optique avec des énergies quadratiques ou linéaires à l’infini. On montre des résultats mathématiques de convergence de la méthode de Décomposition Progressive Généralisée (PGD) dans le cas des énergies quadratiques. Notre démonstration est basée sur la décomposition de Brézis-Lieb via la convergence presque-partout de la suite gradient PGD. Une étude numérique détaillée est faite sur différents type d’images : sur les équations de transport de scalaire passif, dont le champ de déplacement est solution des équations de Navier-Stokes. Ces équations présentent un défi pour l’estimation du flot optique à cause du faible gradient dans plusieurs régions de l’image. Nous avons appliqué notre méthode aux séquences d’images IRM pour l’estimation du mouvement des organes abdominaux. La PGD a présenté une supériorité à la fois au niveau du temps de calcul (même en 2D) et au niveau de la représentation correcte des mouvements estimés. La diffusion locale des méthodes classiques (Horn & Schunck, par exemple) ralentit leur convergence contrairement à la PGD qui est une méthode plus globale par nature. Le dernier chapitre traite de l’application de la méthode PGD dans le cas d’équations elliptiques variationnelles dont l’énergie présente tous les défis aux méthodes variationnelles classiques : manque de convexité, manque de coercivité et manque du caractère borné de l’énergie. Nous démontrons des résultats de convergence, pour la topologie faible, des suites PGD (lorsqu’elles sont bien définies) vers deux solutions extrémales sur la variété de Nehari. Plusieurs questions mathématiques concernant la PGD restent ouvertes dans ce chapitre. Ces questions font partie de nos perspectives de recherche. / This thesis is formed of four chapters. The first one presents the mathematical notions and tools used in this thesis and gives a description of the main results obtained within. The second chapter presents our generalization of a result obtained by Rousselet-Chenais in 1990 which describes the sensitivity of eigensubspaces for self-adjoint compact operators. Rousselet-Chenais were limited to sensitivity for specific subspaces of dimension 1, we have extended their result to higher dimensions. We applied our results to the Proper Orthogonal Decomposition (POD) in the case of parametric, temporal and spatial variations (Gappy- POD). The third chapter discusses the optical flow estimate with quadratic or linear energies at infinity. Mathematical results of convergence are shown for the method Progressive Generalized Decomposition (PGD) in the case of quadratic energies. Our proof is based on the decomposition of Brézis-lieb via the convergence almost everywhere of the PGD sequence gradients. A detailed numerical study is made on different types of images : on the passive scalar transport equations, whose displacement fields are solutions of the Navier-Stokes equations. These equations present a challenge for optical flow estimates because of the presence of low gradient regions in the image. We applied our method to the MRI image sequences to estimate the movement of the abdominal organs. PGD presented a superiority in both computing time level (even in 2D) and accuracy representation of the estimated motion. The local diffusion of standard methods (Horn Schunck, for example) limits the convergence rate, in contrast to the PGD which is a more global approach by construction. The last chapter deals with the application of PGD method in the case of variational elliptic equations whose energy present all challenges to classical variational methods : lack of convexity, lack of coercivity and lack of boundedness. We prove convergence results for the weak topology, the PGD sequences converge (when they are well defined) to two extremal solutions on the Nehari manifold. Several mathematical questions about PGD remain open in this chapter. These questions are part of our research perspectives.