O presente trabalho trata do desenvolvimento de um sistema de sensoriamento de orientação utilizando sensores inerciais de baixo custo, de tecnologia MicroElectroMechanical Systems, MEMS, que apresentam altas taxas de ruído. Assim, é realizada a filtragem e fusão dos dados dos sensores para obtenção de uma estimativa confiável, com a aplicação do filtro de Kalman estendido. O sistema é utilizado para a navegação e controle em um veículo aquático de superfície autônomo. No desenvolvimento do trabalho são investigados os princípios da navegação inercial, da representação da orientação e os sistemas de coordenadas envolvidos, apresentando o método por ângulos de Euler, quatérnios e DCM e o procedimento de atualização conforme a variação da orientação. O sistema desenvolvido foi testado em bancada e em um barco com formato de trimarã construído no Laboratório de Controle e Eletrônica de Potência, na Escola de Engenharia de São Carlos, mostrando os resultados dos testes realizados navegando em uma represa, obtendo resultados satisfatórios para essa aplicação. É mostrado também o comportamento dinâmico dos veículos aquáticos de superfície através do estudo da dinâmica de corpos rígidos. / This work describes the development of an orientation sensing system composed of low cost inertial sensors with MicroElectroMechanical Systems (MEMS) technology, which presents high noise levels. Thus, filtering and sensor\'s measurements fusion is done in order to achieve a reliable estimation, trough an extended Kalman filter. The system is used for navigation and control of an autonomous aquatic surface vehicle. In this work, the principles of inertial navigation, orientation representation as well as the coordinate frames involved are investigated, presenting the methods trough Euler angles, quaternions and DCM, and the update proceeding according to the orientation changes. The developed system was tested in the lab and on a trimaran shaped vessel navigating on a dam, wich was developed in the Control and Power Electronics Laboratory at the São Carlos School of Engineering, achieving satisfactory results for this application. It is also shown the dynamic behavior of the surface aquatic vehicles, using rigid-body dynamics.

As primeiras mídias sociais da internet surgiram há pouco mais de duas décadas, segunda metade dos anos 90. Em comparação com a evolução humana, isso seria algo como um milésimo de segundo de sua existência. Neste período, vários estudos procuram entender o comportamento e o agrupamento dos seres humanos nesta nova forma de comunicação. Teorias sobre formas de analisar as pessoas neste meio e como elas se agrupam e criam novos modos de comunicação e propagação de suas ideias florescem e iluminam este desconhecido caminho a ser criado e percorrido. Os métodos de identificação do comportamento humano criados antes das mídias sociais ganham uma nova forma de serem utilizados. Estudos sobre o \"eu\" (Belk, 1988), tribalismo (Cova, B., 1997), etnografia (Danzig, 1985), netnografia (Kozinets, 1998) e filtragem colaborativa (Golberg, Nichols, Oki e Terry, 1992) entram em cena para colocar uma luz no estudo das relações humanas no mundo digital. A internet revolucionou o modo de as pessoas interagirem e a evolução constante da tecnologia vem incessantemente gerando profundas implicações para o marketing. A rede mundial passou a ser um canal global pelo qual as empresas podem divulgar e vender seus produtos. No entanto, mesmo oferecendo um enorme potencial para as empresas, a internet aumentou a complexidade de identificar os clientes. Os usuários presentes nas mídias sociais estão menos interessados nos produtos e valorizam mais as identidades e os laços sociais gerados em torno de seus assuntos de interesse. Estas tribos eletrônicas ultrapassam as fronteiras geográficas e independem de raça, sexo e aspectos culturais de seus integrantes. Este trabalho apresenta um método para identificar tribos nas mídias sociais. O método foi aplicado na identificação da tribo de MMA (MixedMartialArts, em tradução livre, Artes Marciais Mistas) no Twitter. A validação foi realizada usando a plataforma de anúncios do Twitter, enviando durante 72 horas uma publicidade para mais de 600 mil usuários, divididos em grupo de controle e segmentações do Twitter e do método proposto DNA. O estudo comparou os resultados obtidos pelo método proposto DNA com os resultados do grupo de controle e da segmentação realizada pelo Twitter. Os resultados obtidos apontaram o aumento de interações dos usuários identificados como pertencentes a tribo de MMA, validando o método. / The first Internet social media emerged just over two decades ago, at the second half of 90\'s. Compared to human evolution, this would be something like a millisecond of its existence. In this period, several studies try to understand the behavior and grouping of human beings in this new form of communication. Theories about ways of analyzing people in this environment and how they group themselves and create new ways of communication and propagation their ideas flourish and illuminate this unknown pathway to be created and traveled. Methods of identifying human behavior created before social media receive a new way of being used. Studies on the \"self\" (Belk, 1988), tribalism (Cova, B., 1997), ethnography (Danzig, 1985), netnography (Kozinets, 1998) and collaborative filtering (Golberg, Nichols, Oki and Terry, 1992) come on the scene to shed light on the study of human relations in the digital world. The Internet has revolutionized people\'s way of interacting and the constant evolution of technology generates profound implications for the marketing. The worldwide network has become a global channel through which companies can disclose and sell their products. However, while offering tremendous potential to businesses, the Internet has increased the complexity of identifying customers. Users present in social media are less interested in products and value more the identities and social ties generated around their subjects of interest. These electronic tribes transcend the geographical borders and are independent of race, sex and cultural aspects of its members. This paper presents a method to identify tribes in social media. The method was applied in the identification of the MMA (Mixed Martial Arts) tribe on Twitter. The validation was done using the Twitter ads platform, sending 72 hours of advertisement for more than 600 thousand users, divided in control group and segmentations of Twitter and the proposed method. The study compared the results obtained by the proposed method with that of the control group and the segmentation created by Twitter. The obtained results pointed out the increase of interactions of the users identified as belonging to the MMA tribe validating the method.

Plants can be colonized by complex communities of endophytic fungi. This thesis presents two studies, both of which investigate biotic filtering in endophytic fungal communities. Chapter 1. Endophytic fungi can be acquired horizontally via propagules produced in the environment such as in plant litters of various species. Given that litters from different plant species harbor distinct endophytic fungal communities and that endophytic fungi may be dispersal-limited, the structure of the endophytic fungal community of a given plant may be determined by proximity to particular inoculum sources. Community assembly may also be affected by biotic filtering by the plant. Therefore, a plant may be able to select particular fungal taxa from among the available pool. In that case, the structure of the endophytic fungal community in the plant could be somewhat independent of the structure of the inoculum community. We tested the hypothesis that biotic filtering of endophytic fungal communities occurs in Bromus tectorum by exposing it to a variety of inoculum sources including litters from several co-occurring plant species. The inoculum sources differed significantly from each other in the structures of the communities of endophytic fungi they harbored. We characterized the structures of the resulting leaf and root endophytic fungal communities in Bromus tectorum using high-throughput sequencing. All tested inoculum sources successfully produced complex communities of endophytic fungi in Bromus tectorum. There was significantly more variation in the structures of the communities of endophytic fungi among the inoculum sources than in the resultant endophytic fungal communities in the leaves and roots of Bromus tectorum. These results suggest that biotic filtering by Bromus tectorum played a significant role in the assembly of the endophytic fungal communities in tissues of Bromus tectorum. Because endophytic fungi influence plant fitness, it is reasonable to expect there to be selective pressure to develop a uniform, desirable endophytic fungal community even from disparate inoculum sources via a process known as biotic filtering. Chapter 2. Frequently one finds that different plant species harbor communities that are distinct. However, the nature of this interspecific variation is not clear. We characterized the endophytic fungal communities in six plant species from the eastern Great Basin in central Utah. Four of the species are arbuscular mycorrhizal (two in the Poaceae and two in the Asteraceae), while the other two species are nonmycorrhizal (one in the Brassicaceae and one in the Amaranthaceae). Our evidence suggests that both host mycorrhizal status and phylogenic relatedness independently influence endophytic fungal community structure.

Mobile devices, such as smartphones and wearable devices, have become a popular platform to deliver recommendations and interact with users. To learn the decision rule of assigning recommendations, i.e. policy, neither one homogeneous policy for all users nor completely heterogeneous policy for each user is appropriate. Many attempts have been made to learn a policy for making recommendations using observational mobile health (mHealth) data. The majority of them focuses on a homogeneous policy, that is a one-fit-to-all policy for all users. It is a fair starting point for mHealth study, but it ignores the underlying user heterogeneity. Users with similar behavior pattern may have unobservable underlying heterogeneity. To solve this problem, we develop a personalized learning framework that models both population and personalized effect simultaneously. In the first part of this dissertation, we address the personalized policy learning problem using longitudinal mHealth application usage data. Personalized policy represents a paradigm shift from developing a single policy that may prescribe personalized decisions by tailoring. Specifically, we aim to develop the best policy, one per user, based on estimating random effects under generalized linear mixed model. With many random effects, we consider new estimation method and penalized objective to circumvent high-dimensional integrals for marginal likelihood approximation. We establish consistency and optimality of our method with endogenous application usage. We apply our method to develop personalized prompt schedules in 294 application users, with a goal to maximize the prompt response rate given past application usage and other contextual factors. We found the best push schedule given the same covariates varied among the users, thus calling for personalized policies. Using the estimated personalized policies would have achieved a mean prompt response rate of 23% in these users at 16 weeks or later: this is a remarkable improvement on the observed rate (11%), while the literature suggests 3%-15% user engagement at 3 months after download. The proposed method compares favorably to existing estimation methods including using the R function glmer in a simulation study. In the second part of this dissertation, we aim to solve a practical problem in the mHealth area. Low response rate has been a major issue that blocks researchers from collecting high quality mHealth data. Therefore, developing a prompting system is important to keep user engagement and increase response rate. We aim to learn personalized prompting time for users in order to gain a high response rate. An extension of the personalized learning algorithm is applied on the Intellicare data that incorporates penalties of the population effect parameters and personalized effect parameters into learning the personalized decision rule of sending prompts. The number of personalized policy parameters increases with sample size. Since there is a large number of users in the Intellicare data, it is challenging to estimate such high dimensional parameters. To solve the computational issue, we employ a bagging method that first bootstraps subsamples and then ensembles parameters learned from each subsample. The analysis of Intellicare data shows that sending prompts at a personalized hour helps achieve a higher response rate compared to a one-fit-to-all prompting hour.

Cette thèse porte sur le contrôle non destructif par ultrasons et la détection de défauts dans des matériaux complexes. Elle apporte des améliorations à l’imagerie Total Focusing Method(TFM) lorsque les images sont altérées par un haut niveau de bruit. Trois points essentiels sont abordés : l’optimisation de l’acquisition de la matrice des réponses impulsionnelles K(t) avec des sources virtuelles ou des émissions codées en amplitude ; la séparation des sous-espaces vectoriels associés au signal et au bruit avec la Décomposition de l’Opérateur de Retournement Temporel (DORT) ; et la formation d’image dans le domaine temporel avec TFM après le débruitage des signaux. La thèse s’intéresse au bruit cohérent lié à la structure hétérogène d’un acier à gros grains, puis au bruit électronique incohérent introduit par la chaîne d’acquisition des signaux dans le cas d’un matériau viscoélastique très atténuant. Ce travail s’intéresse aussi aux artefacts d’imagerie engendrés par les ondes de surface se propageant le long d’un capteur multiéléments au contact. Les valeurs singulières associées à ces modes guidés sont modélisées pour faciliter l’interprétation de la décomposition de la matrice de transfert Kˆ(f)et filtrer les artefacts d’imagerie. Lorsque la zone d’intérêt est éloignée de l’axe central du capteur,une approche alternative à la rétro-propagation de vecteurs singuliers est proposée pour améliorer la qualité des images formées dans le domaine fréquentiel. Elle consiste à combinerla méthode DORT avec l’imagerie topologique. Après filtrage du bruit et des ondes de surface,les images TFM sont comparées avec celles calculées par rétro-propagation ou par imagerie topologique. Ensuite, ce travail s’intéresse à la détection dans un tube en polyéthylène dont l’atténuation viscoélastique fait apparaître un fort bruit électronique sur les images TFM. Pour enregistrer la matrice K(t) en augmentant la profondeur de pénétration des ultrasons, deux pseudo-codages de Hadamard sont développés, et les gains apportés sont justifiés théoriquement et expérimentalement. Un modèle théorique des valeurs singulières associées au bruit est ensuite proposé pour faciliter l’extraction de la réponse du défaut dans la matrice Kˆ(f). Enfin, la thèse introduit une méthode de filtrage pour Plane Wave Imaging (PWI) offrant de bonnes performances dans les matériaux complexes car elle cumule les avantages de sources virtuelles (utilisées dans l’acier) et des émissions codées (utilisées dans le polyéthylène) / This thesis is related to ultrasonic non-destructive testing and detection of defects in complex materials. Improvements of the Total Focusing Method (TFM) when images are corrupted bya high noise level are proposed. Three main points are developed : the optimization of the acquisition of the impulse response matrix K(t) using virtual sources or spatial coding ; the separation of subspaces associated with the signal and the noise using the decomposition of the time reversal operator (DORT) ; and the image formation in the time domain with TFM after the signal denoising. Two different types of noise are considered : the coherent noise linked to the heterogeneous structure of a coarse-grained steel, and the incoherent electronic noise introduced by the signal acquisition system in the case of a high attenuating viscoelastic material.The study also focuses on imaging artifacts generated by surface waves which propagate alonga contact array probe. The singular values associated with these guided modes are modeled tofacilitate the interpretation of the decomposition of the transfer matrix ˆK( f ), and to filter theartifacts. When the region of interest is far from the probe axis, an alternative approach to singular vector back-propagation is proposed in order to improve the quality of images formedin the frequency domain. This approach consists in combining the DORT method with the topologica limaging. After the noise and surface waves filtering, the TFM images are comparedwith those calculated by the singular vector back-propagation or by the topological imaging.Then, this work focuses on the detection in a polyethylene pipe of high viscoelastic attenuation introducing unwanted noise in the TFM images. To record the K(t) matrix while increasingthe ultrasonic penetration depth, two Hadamard pseudo-codes are developed, and the gainsare theoretically and experimentally justified. A theoretical model of the singular values associated with the noise is then proposed to facilitate the defect response extraction from thetransfer matrix ˆK( f ). Finally, a filtering procedure for Plane Wave Imaging (PWI) is proposed,which combines the advantages of virtual sources (used in the coarse-grained steel) and coded transmissions (used in the polyethylene), thus giving excellent performances in complex materials

The aim of this effort is to implement a nonlinear flight control architecture, specifically flight path control via command filtered backstepping, for use in AME UAS's Fury® 1500 unmanned flying wing aircraft. Backstepping is a recursive, control-effort minimizing, constructive design procedure that interlaces the choice of a Lyapunov function with the design of feedback control. It allows the use of certain plant states to act as intermediate, virtual controls, for others breaking complex high order systems into a sequence of simpler lower-order design tasks. Work herein is a simplified implementation based on publications by Farrell, Sharma, and Polycarpou. Online approximation is not applied, however command filtering along with two variants of control allocation is. This minimal approach was done to mitigate risk, as adaptation could be added in future work to this baseline. Command filtering assures that control inputs generated meet magnitude, rate, and bandwidth constraints for states and actuators as well as provides command derivatives that reduce computation. Two different forms of control allocation were implemented, the simplest a least-squares pseudo-inverse and the second an optimal quadratic programming method. Two Simulink based simulations successfully flew AME's Fury® 1500 UAS: a nominal case with fully operational actuators and a failure case with an actuator stuck at -10°. Coordinated flight for both cases with outer loop tracking was achieved for a demanding autopilot task of simultaneously varying heading and flight-path angle commands, ±60° and ±10° respectively, for a constant airspeed command of 135 ft/s. Command signals were generated were achievable due to the command filter implementation.

Robot localization is one of the most important subjects in the Robotics science. It is an interesting and complicated topic. There are many algorithms to solve the problem of localization. Each localization system has its own set of features, and based on them, a solution will be chosen. In my thesis, I want to present a solution to find the best estimate for a robot position in certain space for which a map is available. The thesis started with an elementary introduction to the probability and the Gaussian theories. Simple and advanced practical examples are presented to illustrate each concept related to localization. Extended Kalman Filter is chosen to be the main algorithm to find the best estimate of the robot position. It was presented through two chapters with many examples. All these examples were simulated in Matlab in this thesis in order to give the readers and future students a clear and complete introduction to Kalman Filter. Fortunately, I applied this algorithm on a robot that I have built its base from scratch. MCECS-Bot was a project started in Winter 2012 and it was assigned to me from my adviser, Dr. Marek Perkowski. This robot consists of the base with four Mecanum wheels, the waist based on four linear actuators, an arm, neck and head. The base is equipped with many sensors, which are bumper switches, encoders, sonars, LRF and Kinect. Additional devices can provide extra information as backup sensors, which are a tablet and a camera. The ultimate goal of this thesis is to have the MCECS-Bot as an open source system accessed by many future classes, capstone projects and graduate thesis students for education purposes. A well-known MRPT software system was used to present the results of the Extended Kalman Filter (EKF). These results are simply the robot positions estimated by EKF. They are demonstrated on the base floor of the FAB building of PSU. In parallel, simulated results to all different solutions derived in this thesis are presented using Matlab. A future students will have a ready platform and a good start to continue developing this system.

Résumé indisponible. / Résumé indisponible.

Exome sequencing, the process of sequencing the set of all known exons simultaneously using next-generation sequencing technology, has dramatically changed the landscape of genetic research and genetic testing. The incredible volume of data produced by these experiments creates challenges in: 1) annotating the affects of observed variants, 2) filtering to remove noise, 3) identifying plausible disease-causing variants, and 4) validating experimental results. Here we will present a series of bioinformatic tools and techniques intended to address these challenges with exome sequencing and associated validation experiments. First, we will present the Automated Sequence Analysis Pipeline (ASAP), a tool for the efficient and automated management, detection and annotation of Sanger sequencing-based genetic testing and variant validation. This pipeline is extended to annotate exome-sequencing derived variants. Exome sequencing experiments produce a great number of variants that do not cause a patient's disease. One of the biggest challenges in exome sequencing experiments is sorting through these false positives to discover the true disease-causing variants. We have developed several techniques to aid in the reduction of these errors. The techniques described include: 1) the construction of a catalog of systematic errors by reprocessing thousands of publically available exomes, 2) a tool for the filtering of variants based on family structure and disease assumptions, and 3) a tool for discovering regions of autozygosity from the exomes of several affected patients in consanguineous pedigrees. Classes of variants that are undiscoverable using current analysis techniques gives rise to false negatives in exome sequencing experiments. We will present a tool, the Retrotransposon Insertion Detector for Exomes (RIDE) that uses the characteristic anomalies present in sequence alignments to detect the insertion of repetitive elements. The process of identifying a the cause of a patient's disease using exome sequencing data has been equated to finding a needle in a stack of needles. Only through the proper annotation of variants and the reduction of the error rates associated with exome sequencing experiments can this task be achieved in an efficient manner.

This thesis reviews algorithms that have been developed for classifying lidar data and identifies a progressive morphological filter for evaluation and improvement. Two potential weaknesses evaluated include the row-column order bias and grid orientation bias. Four different row-column orderings were developed to test for bias associated with the order choice. Moreover, a method rotating the filter grid to a series of angles was developed for testing bias associated with grid orientation. Measures of success of the improvements include Type I and II errors, where results are compared with a hand-produced "truth" dataset. Two datasets, one urban, the other rural, were selected for testing the modified filters. The results are presented and discussed for each algorithm. It was found that the four row-column orders all classified the dataset exactly the same. After the erosion and dilation functions were completed, the same surface profiles and elevations were produced regardless of row-column ordering. The filter windows used by the algorithm were found to create a rectangular area where the minimum and maximum values within that area were always selected. Therefore, it was found that the row-column orders did not create a bias in the classification. However, grid orientation was found to greatly affect results. Misclassification problems occurred at ridgelines, mounds, and along roads with ditches and steep slopes running along them. Grid angles running parallel to these objects were found to avoid these errors. Buildings also created errors, but were minimized with grid angles crossing them at 45 degrees. The selected angle directions significantly affect the classification results in all cases. Therefore, the grid orientation bias was verified. Two new methods of combining the results from the various angles have been developed. The first method used the best two classifying angles to combine the results. Best results were found in datasets with terrain objects positioned in similar directions for this method. The Multiple Angle method used all of the angle classifications to combine the results. This method performed best on datasets with terrain objects oriented in numerous directions. More accurate terrain models and better overall classification results have been generated using these methods.