Οι πρόσφατες εξελίξεις στις ασύρματες επικοινωνίες και στα ηλεκτρονικά κυκλώματα έχουν επιτρέψει την ανάπτυξη υπολογιστικών διατάξεων χαμηλού κόστους και χαμηλής κατανάλωσης ισχύος, οι οποίες ενσωματώνουν δυνατότητες μέτρησης (sensing), επεξεργασίας και ασύρματης επικοινωνίας. Οι διατάξεις αυτές, οι οποίες έχουν ιδιαίτερα μικρό μέγεθος, καλούνται κόμβοι αισθητήρες. Ένα ασύρματο δίκτυο κόμβων αισθητήρων αποτελείται από ένα πλήθος κόμβων οι οποίοι έχουν αναπτυχθεί σε κάποια περιοχή ενδιαφέροντος προκειμένου να μετρούν κάποια μεταβλητή του περιβάλλοντος. Ανάμεσα σε πολλές εφαρμογές, ο εντοπισμός και η παρακολούθηση των θέσεων πηγών οι οποίες εκπέμπουν κάποιο σήμα (π.χ. ακουστικό, ηλεκτρομαγνητικό) αποτελεί ένα πολύ ενδιαφέρον θέμα, το οποίο μάλιστα μπορεί να χρησιμοποιηθεί και ως βάση για τη μελέτη άλλων προβλημάτων τα οποία εμφανίζονται στα ασύρματα δίκτυα αισθητήρων. Οι περισσότερες από τις υπάρχουσες τεχνικές εντοπισμού θέσης μιας πηγής από μια συστοιχία αισθητήρων μπορούν να ταξινομηθούν σε δυο κατηγορίες: (α) Τις τεχνικές οι οποίες χρησιμοποιούν μετρήσεις διεύθυνσης άφιξης (Direction of Arrival, DOA) και (β) τις τεχνικές οι οποίες χρησιμοποιούν μετρήσεις διαφοράς χρόνων άφιξης (Time Difference of Arrival, TDOA). Ωστόσο, οι τεχνικές αυτές απαιτούν υψηλό ρυθμό δειγματοληψίας και ακριβή συγχρονισμό των κόμβων και δε συνάδουν έτσι με τις περιορισμένες ικανότητες των κόμβων αισθητήρων. Για τους λόγους αυτούς, το ενδιαφέρον έχει στραφεί σε μια τρίτη κατηγορία τεχνικών οι οποίες χρησιμοποιούν μετρήσεις ισχύος (Received Signal Strength, RSS). Το πρόβλημα του εντοπισμού θέσης χρησιμοποιώντας μετρήσεις ισχύος είναι ένα πρόβλημα εκτίμησης, όπου οι μετρήσεις συνδέονται με τις προς εκτίμηση παραμέτρους με μη-γραμμικό τρόπο. Στα πλαίσια της Διδακτορικής Διατριβής ασχολούμαστε αρχικά με την περίπτωση όπου επιθυμούμε να εκτιμήσουμε τη θέση και την ισχύ μιας πηγής χρησιμοποιώντας μετρήσεις ισχύος οι οποίες φθίνουν με βάση το αντίστροφο του τετραγώνου της απόστασης ανάμεσα στην πηγή και το σημείο μέτρησης. Για το πρόβλημα αυτό, προτείνουμε έναν εκτιμητή ο οποίος δίνει τις παραμέτρους της πηγής ως λύση ενός γραμμικού προβλήματος ελαχίστων τετραγώνων. Στη συνέχεια, υπολογίζουμε κατάλληλα βάρη και προτείνουμε έναν εκτιμητή ο οποίος δίνει τις παραμέτρους της πηγής ως λύση ενός προβλήματος ελαχίστων τετραγώνων με βάρη. Ακόμα, τροποποιούμε κατάλληλα τον τελευταίο εκτιμητή έτσι ώστε να είναι δυνατή η κατανεμημένη υλοποίησή του μέσω των προσαρμοστικών αλγορίθμων Least Mean Square (LMS) και Recursive Least Squares (RLS). Στη συνέχεια, εξετάζουμε την περίπτωση όπου ενδιαφερόμαστε να εκτιμήσουμε τη θέση μιας πηγής αλλά δεν έχουμε καμιά πληροφορία σχετικά με το μοντέλο εξασθένισης της ισχύος. Έτσι, υποθέτουμε πως αυτό περιγράφεται από μια άγνωστη γνησίως φθίνουσα συνάρτηση της απόστασης. Αρχικά, προσεγγίζουμε το πρόβλημα εκτίμησης κάνοντας την υπόθεση πως οι θέσεις των κόμβων αποτελούν τυχαία σημεία ομοιόμορφα κατανεμημένα στο επίπεδο. Χρησιμοποιώντας την υπόθεση αυτή, υπολογίζουμε εκτιμήσεις για τις αποστάσεις ανάμεσα στους κόμβους και την πηγή, και αναπτύσσουμε έναν αλγόριθμο εκτίμησης της θέσης της πηγής. Στη συνέχεια, προσεγγίζουμε το πρόβλημα εκτίμησης χωρίς την υπόθεση περί ομοιόμορφης κατανομής των θέσεων των κόμβων στο επίπεδο. Προτείνουμε μια κατάλληλη συνάρτηση κόστους για την περίπτωση αυτή, και δείχνουμε την ύπαρξη μιας συνθήκης υπό την οποία η βέλτιστη λύση μπορεί να υπολογιστεί. Η λύση αυτή είναι εσωτερικό σημείο ενός κυρτού πολυγώνου, το οποίο ονομάζουμε ταξινομημένο τάξης-K κελί Voronoi. Έτσι, δίνουμε αλγορίθμους υπολογισμού της λύσης αυτής, καθώς και κατανεμημένους αλγορίθμους οι οποίοι βασίζονται σε προβολές σε κυρτά σύνολα. Ακόμα, ασχολούμαστε με τις ιδιότητες των κελιών αυτών στην περίπτωση όπου οι θέσεις των κόμβων αισθητήρων είναι ομοιόμορφα κατανεμημένες στο επίπεδο και υπολογίζουμε κάποια φράγματα για το εμβαδόν τους. Τέλος, ασχολούμαστε με την περίπτωση όπου ενδιαφερόμαστε να εκτιμήσουμε τις θέσεις πολλαπλών πηγών με γνωστό μοντέλο εξασθένισης της ισχύος. Για το πρόβλημα αυτό, αρχικά προτείνουμε έναν αλγόριθμο διαδοχικής εκτίμησης και ακύρωσης της συνεισφοράς κάθε πηγής, προκειμένου να υπολογιστούν σταδιακά οι θέσεις όλων των πηγών. Ο αλγόριθμος αυτός, αποτελείται από τρία βήματα κατά τα οποία πρώτα υπολογίζεται μια προσεγγιστική θέση για την πηγή, στη συνέχεια εκτιμάται ένα σύνολο κόμβων το οποίο δέχεται μικρής έντασης παρεμβολή από τις υπόλοιπες πηγές, και τέλος επιχειρείται μια λεπτομερέστερη εκτίμηση της θέσης κάθε πηγής. Στη συνέχεια, επεκτείνοντας την τεχνική αυτή, προτείνουμε έναν επαναληπτικό αλγόριθμο εκτίμησης ο οποίος βασίζεται στον αλγόριθμο εναλλασσόμενων προβολών (Alternating Projections). Εξετάζουμε επίσης μεθόδους οι οποίες οδηγούν στη μείωση της υπολογιστικής πολυπλοκότητας του αλγορίθμου αυτού. / Technology advances in microelectronics and wireless communications have enabled the development of small-scale devices that integrate sensing, processing and short-range radio capabilities. The deployment of a large number of such devices, referred to as sensor nodes, over a territory of interest, defines the so-called wireless sensor network. Wireless sensor networks have attracted considerable attention in recent years and have motivated many new challenges, most of which require the synergy of many disciplines, including signal processing, networking and distributed algorithms. Among many other applications, source localization and tracking has been widely viewed as a canonical problem of wireless sensor networks. Furthermore, it constitutes an easily perceived problem that can be used as a vehicle to study more involved information processing and organization problems. Most of the source localization methods that have appeared in the literature can be classified into two broad categories, according to the physical variable they utilize. The algorithms of the first category utilize “time delay of arrival”(TDOA) measurements, and the algorithms of the second category use “direction of arrival” (DOA) measurements. DOA estimates are particularly useful for locating sources emitting narrowband signals, while TDOA measurements offer the increased capability of localizing sources emitting broadband signals. However, the methods of both categories impose two major requirements that render them inappropriate to be used in wireless sensor networks: (a) the analog signals at the outputs of the spatially distributed sensors should be sampled in a synchronized fashion, and (b) the sampling rate used should be high enough so as to capture the features of interest. These requirements, in turn, imply that accurate distributed synchronization methods should be implemented so as to keep the remote sensor nodes synchronized and that high frequency electronics as well as increased bandwidth are needed to transmit the acquired measurements. Due to the aforementioned limitations, source localization methods that rely upon received signal strength (RSS) measurements - originally explored for locating electromagnetic sources - have recently received revived attention. In this Thesis, we begin our study by considering the localization of an isotropic acoustic source using energy measurements from distributed sensors, in the case where the energy decays according to an inverse square law with respect to the distance. While most acoustic source localization algorithms require that distance estimates between the sensors and the source of interest are available, we propose a linear least squares criterion that does not make such an assumption. The new criterion can yield the location of the source and its transmit power in closed form. A weighted least squares cost function is also considered, and distributed implementation of the proposed estimators is studied. Numerical results indicate significant performance improvement as compared to a linear least squares based approach that utilizes energy ratios, and comparable performance to other estimators of higher computational complexity. In the sequel, we turn our attention to the case where the energy decay model is not known. For solving the localization problem in this case, we first make the assumption that the locations of the nodes near the source can be well described by a uniform distribution. Using this assumption, we derive distance estimates that are independent of both the energy decay model and the transmit power of the source. Numerical results show that these estimates lead to improved localization accuracy as compared to other model-independent approaches. In the sequel, we consider the more general case where the assumption about the uniform deployment of the sensors is not required. For this case, an optimization problem that does not require knowledge of the underlying energy decay model is proposed, and a condition under which the optimal solution can be computed is given. This condition employs a new geometric construct, called the sorted order-K Voronoi diagram. We give centralized and distributed algorithms for source localization in this setting. Finally, analytical results and simulations are used to verify the performance of the developed algorithms. The next problem we consider is the estimation of the locations of multiple acoustic sources by a network of distributed energy measuring sensors. The maximum likelihood (ML) solution to this problem is related to the optimization of a non-convex function of, usually, many variables. Thus, search-based methods of high complexity are required in order to yield an accurate solution. In order to reduce the computational complexity of the multiple source localization problem, we propose two methods. The first method proposes a sequential estimation algorithm, in which each source is localized, its contribution is cancelled, and the next source is considered. The second method makes use of an alternating projection (AP) algorithm that decomposes the original problem into a number of simpler, yet also non-convex, optimization steps. The particular form of the derived cost functions of each such optimization step indicates that, in some cases, an approximate form of these cost functions can be used. These approximate cost functions can be evaluated using considerably lower computational complexity. Thus, a low-complexity version of the AP algorithm is proposed. Extensive simulation results demonstrate that the proposed algorithm offers a performance close to that of the exact AP implementation, and in some cases, similar performance to that of the ML estimator.

Ce travail porte, de façon théorétique et pratique, sur les sujets plus pertinents autour des drones en navigation autonome et semi-autonome. Conformément à la nature multidisciplinaire des problèmes étudies, une grande diversité des techniques et théories ont été couverts dans les domaines de la robotique, l’automatique, l’informatique, la vision par ordinateur et les systèmes embarques, parmi outres.Dans le cadre de cette thèse, deux plates-formes expérimentales ont été développées afin de valider la théorie proposée pour la navigation autonome d’un drone. Le premier prototype, développé au laboratoire, est un quadrirotor spécialement conçu pour les applications extérieures. La deuxième plate-forme est composée d’un quadrirotor à bas coût du type AR.Drone fabrique par Parrot. Le véhicule est connecté sans fil à une station au sol équipé d’un système d’exploitation pour robots (ROS) et dédié à tester, d’une façon facile, rapide et sécurisé, les algorithmes de vision et les stratégies de commande proposés. Les premiers travaux développés ont été basés sur la fusion de donnés pour estimer la position du drone en utilisant des capteurs inertiels et le GPS. Deux stratégies ont été étudiées et appliquées, le Filtre de Kalman Etendu (EKF) et le filtre à Particules (PF). Les deux approches prennent en compte les mesures bruitées de la position de l’UAV, de sa vitesse et de son orientation. On a réalisé une validation numérique pour tester la performance des algorithmes. Une tâche dans le cahier de cette thèse a été de concevoir d’algorithmes de commande pour le suivi de trajectoires ou bien pour la télé-opération. Pour ce faire, on a proposé une loi de commande basée sur l’approche de Mode Glissants à deuxième ordre. Cette technique de commande permet de suivre au quadrirotor de trajectoires désirées et de réaliser l’évitement des collisions frontales si nécessaire. Etant donné que la plate-forme A.R.Drone est équipée d’un auto-pilote d’attitude, nous avons utilisé les angles désirés de roulis et de tangage comme entrées de commande. L’algorithme de commande proposé donne de la robustesse au système en boucle fermée. De plus, une nouvelle technique de vision monoculaire par ordinateur a été utilisée pour la localisation d’un drone. Les informations visuelles sont fusionnées avec les mesures inertielles du drone pour avoir une bonne estimation de sa position. Cette technique utilise l’algorithme PTAM (localisation parallèle et mapping), qui s’agit d’obtenir un nuage de points caractéristiques dans l’image par rapport à une scène qui servira comme repère. Cet algorithme n’utilise pas de cibles, de marqueurs ou de scènes bien définies. La contribution dans cette méthodologie a été de pouvoir utiliser le nuage de points disperse pour détecter possibles obstacles en face du véhicule. Avec cette information nous avons proposé un algorithme de commande pour réaliser l’évitement d’obstacles. Cette loi de commande utilise les champs de potentiel pour calculer une force de répulsion qui sera appliquée au drone. Des expériences en temps réel ont montré la bonne performance du système proposé. Les résultats antérieurs ont motivé la conception et développement d’un drone capable de réaliser en sécurité l’interaction avec les hommes et les suivre de façon autonome. Un classificateur en cascade du type Haar a été utilisé pour détecter le visage d’une personne. Une fois le visage est détecté, on utilise un filtre de Kalman (KF) pour améliorer la détection et un algorithme pour estimer la position relative du visage. Pour réguler la position du drone et la maintenir à une distance désirée du visage, on a utilisé une loi de commande linéaire. / The present document addresses, theoretically and experimentally, the most relevant topics for Unmanned Aerial Vehicles (UAVs) in autonomous and semi-autonomous navigation. According with the multidisciplinary nature of the studied problems, a wide range of techniques and theories are covered in the fields of robotics, automatic control, computer science, computer vision and embedded systems, among others. As part of this thesis, two different experimental platforms were developed in order to explore and evaluate various theories and techniques of interest for autonomous navigation. The first prototype is a quadrotor specially designed for outdoor applications and was fully developed in our lab. The second testbed is composed by a non expensive commercial quadrotor kind AR. Drone, wireless connected to a ground station equipped with the Robot Operating System (ROS), and specially intended to test computer vision algorithms and automatic control strategies in an easy, fast and safe way. In addition, this work provides a study of data fusion techniques looking to enhance the UAVs pose estimation provided by commonly used sensors. Two strategies are evaluated in particular, an Extended Kalman Filter (EKF) and a Particle Filter (PF). Both estimators are adapted for the system under consideration, taking into account noisy measurements of the UAV position, velocity and orientation. Simulations show the performance of the developed algorithms while adding noise from real GPS (Global Positioning System) measurements. Safe and accurate navigation for either autonomous trajectory tracking or haptic teleoperation of quadrotors is presented as well. A second order Sliding Mode (2-SM) control algorithm is used to track trajectories while avoiding frontal collisions in autonomous flight. The time-scale separation of the translational and rotational dynamics allows us to design position controllers by giving desired references in the roll and pitch angles, which is suitable for quadrotors equipped with an internal attitude controller. The 2-SM control allows adding robustness to the closed-loop system. A Lyapunov based analysis probes the system stability. Vision algorithms are employed to estimate the pose of the vehicle using only a monocular SLAM (Simultaneous Localization and Mapping) fused with inertial measurements. Distance to potential obstacles is detected and computed using the sparse depth map from the vision algorithm. For teleoperation tests, a haptic device is employed to feedback information to the pilot about possible collisions, by exerting opposite forces. The proposed strategies are successfully tested in real-time experiments, using a low-cost commercial quadrotor. Also, conception and development of a Micro Aerial Vehicle (MAV) able to safely interact with human users by following them autonomously, is achieved in the present work. Once a face is detected by means of a Haar cascade classifier, it is tracked applying a Kalman Filter (KF), and an estimation of the relative position with respect to the face is obtained at a high rate. A linear Proportional Derivative (PD) controller regulates the UAV’s position in order to keep a constant distance to the face, employing as well the extra available information from the embedded UAV’s sensors. Several experiments were carried out through different conditions, showing good performance even under disadvantageous scenarios like outdoor flight, being robust against illumination changes, wind perturbations, image noise and the presence of several faces on the same image. Finally, this thesis deals with the problem of implementing a safe and fast transportation system using an UAV kind quadrotor with a cable suspended load. The objective consists in transporting the load from one place to another, in a fast way and with minimum swing in the cable.

Dans la thèse nous nous occupons de l'estimation de la moyenne d'un petit domaine sous un modèle one-fold et utilisant MINQUE pour estimer les composantes de la variance, sous un modèle two-fold avec variances aléatoires, sous des plans noninformatifs et informatifs. / Doctorat en sciences, Orientation statistique / info:eu-repo/semantics/nonPublished

Ce manuscrit est dédié à l’analyse de performances en traitement d’antenne pour l’estimation des paramètres d’intérêt à l’aide d’un réseau de capteurs. Il est divisé en deux parties :– Tout d’abord, nous présentons l’étude de certaines bornes inférieures de l’erreur quadratique moyenne liées à la localisation de sources dans le contexte champ proche. Nous utilisons la borne de Cramér-Rao pour l’étude de la zone asymptotique (notamment en terme de rapport signal à bruit avec un nombre fini d’observations). Puis, nous étudions d’autres bornes inférieures de l’erreur quadratique moyenne qui permettent de prévoir le phénomène de décrochement de l’erreur quadratique moyenne des estimateurs (on cite, par exemple, la borne de McAulay-Seidman, la borne de Hammersley-Chapman-Robbins et la borne de Fourier Cramér-Rao).– Deuxièmement, nous nous concentrons sur le concept du seuil statistique de résolution limite, c’est-à-dire, la distance minimale entre deux signaux noyés dans un bruit additif qui permet une ”correcte” estimation des paramètres. Nous présentons quelques applications bien connues en traitement d’antenne avant d’étendre les concepts existants au cas de signaux multidimensionnels. Par la suite, nous étudions la validité de notre extension en utilisant un test d’hypothèses binaire. Enfin, nous appliquons notre extension à certains modèles d’observation multidimensionnels / This manuscript concerns the performance analysis in array signal processing. It can bedivided into two parts :- First, we present the study of some lower bounds on the mean square error related to the source localization in the near eld context. Using the Cramér-Rao bound, we investigate the mean square error of the maximum likelihood estimator w.r.t. the direction of arrivals in the so-called asymptotic area (i.e., for a high signal to noise ratio with a nite number of observations.) Then, using other bounds than the Cramér-Rao bound, we predict the threshold phenomena.- Secondly, we focus on the concept of the statistical resolution limit (i.e., the minimum distance between two closely spaced signals embedded in an additive noise that allows a correct resolvability/parameter estimation.) We de ne and derive the statistical resolution limit using the Cramér-Rao bound and the hypothesis test approaches for the mono-dimensional case. Then, we extend this concept to the multidimensional case. Finally, a generalized likelihood ratio test based framework for the multidimensional statistical resolution limit is given to assess the validity of the proposed extension.

CAPES / Em ensaios não destrutivos por ultrassom, o sinal obtido a partir de um sistema de aquisição de dados real podem estar contaminados por ruído e os ecos podem ter atrasos de tempo subamostrais. Em alguns casos, esses aspectos podem comprometer a informação obtida de um sinal por um sistema de aquisição. Para lidar com essas situações, podem ser utilizadas técnicas de estimativa de atraso temporal (Time Delay Estimation ou TDE) e também técnicas de reconstrução de sinais, para realizar aproximações e obter mais informações sobre o conjunto de dados. As técnicas de TDE podem ser utilizadas com diversas finalidades na defectoscopia, como por exemplo, para a localização precisa de defeitos em peças, no monitoramento da taxa de corrosão em peças, na medição da espessura de um determinado material e etc. Já os métodos de reconstrução de dados possuem uma vasta gama de aplicação, como nos NDT, no imageamento médico, em telecomunicações e etc. Em geral, a maioria das técnicas de estimativa de atraso temporal requerem um modelo de sinal com precisão elevada, caso contrário, a localização dessa estimativa pode ter sua qualidade reduzida. Neste trabalho, é proposto um esquema alternado que estima de forma conjunta, uma referência de eco e atrasos de tempo para vários ecos a partir de medições ruidosas. Além disso, reinterpretando as técnicas utilizadas a partir de uma perspectiva probabilística, estendem-se suas funcionalidades através de uma aplicação conjunta de um estimador de máxima verossimilhança (Maximum Likelihood Estimation ou MLE) e um estimador máximo a posteriori (MAP). Finalmente, através de simulações, resultados são apresentados para demonstrar a superioridade do método proposto em relação aos métodos convencionais. / Abstract (parágrafo único): In non-destructive testing (NDT) with ultrasound, the signal obtained from a real data acquisition system may be contaminated by noise and the echoes may have sub-sample time delays. In some cases, these aspects may compromise the information obtained from a signal by an acquisition system. To deal with these situations, Time Delay Estimation (TDE) techniques and signal reconstruction techniques can be used to perform approximations and also to obtain more information about the data set. TDE techniques can be used for a number of purposes in the defectoscopy, for example, for accurate location of defects in parts, monitoring the corrosion rate in pieces, measuring the thickness of a given material, and so on. Data reconstruction methods have a wide range of applications, such as NDT, medical imaging, telecommunications and so on. In general, most time delay estimation techniques require a high precision signal model, otherwise the location of this estimate may have reduced quality. In this work, an alternative scheme is proposed that jointly estimates an echo model and time delays for several echoes from noisy measurements. In addition, by reinterpreting the utilized techniques from a probabilistic perspective, its functionalities are extended through a joint application of a maximum likelihood estimator (MLE) and a maximum a posteriori (MAP) estimator. Finally, through simulations, results are presented to demonstrate the superiority of the proposed method over conventional methods.

Indiana University-Purdue University Indianapolis (IUPUI) / Joint models of longitudinal and survival outcomes have been used with increasing frequency in clinical investigations. Correct specification of fixed and random effects, as well as their functional forms is essential for practical data analysis. However, no existing methods have been developed to meet this need in a joint model setting. In this dissertation, I describe a penalized likelihood-based method with adaptive least absolute shrinkage and selection operator (ALASSO) penalty functions for model selection. By reparameterizing variance components through a Cholesky decomposition, I introduce a penalty function of group shrinkage; the penalized likelihood is approximated by Gaussian quadrature and optimized by an EM algorithm. The functional forms of the independent effects are determined through a procedure for structural discovery. Specifically, I first construct the model by penalized cubic B-spline and then decompose the B-spline to linear and nonlinear elements by spectral decomposition. The decomposition represents the model in a mixed-effects model format, and I then use the mixed-effects variable selection method to perform structural discovery. Simulation studies show excellent performance. A clinical application is described to illustrate the use of the proposed methods, and the analytical results demonstrate the usefulness of the methods.

Multiple-outcome longitudinal data are abundant in clinical investigations. For example, infections with different pathogenic organisms are often tested concurrently, and assessments are usually taken repeatedly over time. It is therefore natural to consider a multivariate modeling approach to accommodate the underlying interrelationship among the multiple longitudinally measured outcomes. This dissertation proposes a multivariate semiparametric modeling framework for such data. Relevant estimation and inference procedures as well as model selection tools are discussed within this modeling framework. The first part of this research focuses on the analytical issues concerning binary data. The second part extends the binary model to a more general situation for data from the exponential family of distributions. The proposed model accounts for the correlations across the outcomes as well as the temporal dependency among the repeated measures of each outcome within an individual. An important feature of the proposed model is the addition of a bivariate smooth function for the depiction of concurrent nonlinear and possibly interacting influences of two independent variables on each outcome. For model implementation, a general approach for parameter estimation is developed by using the maximum penalized likelihood method. For statistical inference, a likelihood-based resampling procedure is proposed to compare the bivariate nonlinear effect surfaces across the outcomes. The final part of the dissertation presents a variable selection tool to facilitate model development in practical data analysis. Using the adaptive least absolute shrinkage and selection operator (LASSO) penalty, the variable selection tool simultaneously identifies important fixed effects and random effects, determines the correlation structure of the outcomes, and selects the interaction effects in the bivariate smooth functions. Model selection and estimation are performed through a two-stage procedure based on an expectation-maximization (EM) algorithm. Simulation studies are conducted to evaluate the performance of the proposed methods. The utility of the methods is demonstrated through several clinical applications.

Indiana University-Purdue University Indianapolis (IUPUI) / Lithium ion (Li-ion) batteries have become integral parts of our lives; they are widely used in applications like handheld consumer products, automotive systems, and power tools among others. To extract maximum output from a Li-ion battery under optimal conditions it is imperative to have access to the state of the battery under every operating condition. Faults occurring in the battery when left unchecked can lead to irreversible, and under extreme conditions, catastrophic damage. In this thesis, an adaptive fault diagnosis technique is developed for Li-ion batteries. For the purpose of fault diagnosis the battery is modeled by using lumped electrical elements under the equivalent circuit paradigm. The model takes into account much of the electro-chemical phenomenon while keeping the computational effort at the minimum. The diagnosis process consists of multiple models representing the various conditions of the battery. A bank of observers is used to estimate the output of each model; the estimated output is compared with the measurement for generating residual signals. These residuals are then used in the multiple model adaptive estimation (MMAE) technique for generating probabilities and for detecting the signature faults. The effectiveness of the fault detection and identification process is also dependent on the model uncertainties caused by the battery modeling process. The diagnosis performance is compared for both the linear and nonlinear battery models. The non-linear battery model better captures the actual system dynamics and results in considerable improvement and hence robust battery fault diagnosis in real time. Furthermore, it is shown that the non-linear battery model enables precise battery condition monitoring in different degrees of over-discharge.

Indiana University-Purdue University Indianapolis (IUPUI) / Epidemiologic and clinical studies routinely collect longitudinal measures of multiple outcomes. These longitudinal outcomes can be used to establish the temporal order of relevant biological processes and their association with the onset of clinical symptoms. In the first part of this thesis, we proposed to use bivariate change point models for two longitudinal outcomes with a focus on estimating the correlation between the two change points. We adopted a Bayesian approach for parameter estimation and inference. In the second part, we considered the situation when time-to-event outcome is also collected along with multiple longitudinal biomarkers measured until the occurrence of the event or censoring. Joint models for longitudinal and time-to-event data can be used to estimate the association between the characteristics of the longitudinal measures over time and survival time. We developed a maximum-likelihood method to joint model multiple longitudinal biomarkers and a time-to-event outcome. In addition, we focused on predicting conditional survival probabilities and evaluating the predictive accuracy of multiple longitudinal biomarkers in the joint modeling framework. We assessed the performance of the proposed methods in simulation studies and applied the new methods to data sets from two cohort studies. / National Institutes of Health (NIH) Grants R01 AG019181, R24 MH080827, P30 AG10133, R01 AG09956.