Wireless sensor networks form an emerging class of computing devices capable of observing the world with an unprecedented resolution, and promise to provide a revolutionary instrument for environmental monitoring. Such a network is composed of a collection of battery-operated wireless sensors, or sensor nodes, each of which is equipped with sensing, processing and wireless communication capabilities. Thanks to advances in microelectronics and wireless technologies, wireless sensors are small in size, and can be deployed at low cost over different kinds of environments in order to monitor both over space and time the variations of physical quantities such as temperature, humidity, light, or sound. <p><p>In environmental monitoring studies, many applications are expected to run unattended for months or years. Sensor nodes are however constrained by limited resources, particularly in terms of energy. Since communication is one order of magnitude more energy-consuming than processing, the design of data collection schemes that limit the amount of transmitted data is therefore recognized as a central issue for wireless sensor networks.<p><p>An efficient way to address this challenge is to approximate, by means of mathematical models, the evolution of the measurements taken by sensors over space and/or time. Indeed, whenever a mathematical model may be used in place of the true measurements, significant gains in communications may be obtained by only transmitting the parameters of the model instead of the set of real measurements. Since in most cases there is little or no a priori information about the variations taken by sensor measurements, the models must be identified in an automated manner. This calls for the use of machine learning techniques, which allow to model the variations of future measurements on the basis of past measurements.<p><p>This thesis brings two main contributions to the use of learning techniques in a sensor network. First, we propose an approach which combines time series prediction and model selection for reducing the amount of communication. The rationale of this approach, called adaptive model selection, is to let the sensors determine in an automated manner a prediction model that does not only fits their measurements, but that also reduces the amount of transmitted data. <p><p>The second main contribution is the design of a distributed approach for modeling sensed data, based on the principal component analysis (PCA). The proposed method allows to transform along a routing tree the measurements taken in such a way that (i) most of the variability in the measurements is retained, and (ii) the network load sustained by sensor nodes is reduced and more evenly distributed, which in turn extends the overall network lifetime. The framework can be seen as a truly distributed approach for the principal component analysis, and finds applications not only for approximated data collection tasks, but also for event detection or recognition tasks. <p><p>/<p><p>Les réseaux de capteurs sans fil forment une nouvelle famille de systèmes informatiques permettant d'observer le monde avec une résolution sans précédent. En particulier, ces systèmes promettent de révolutionner le domaine de l'étude environnementale. Un tel réseau est composé d'un ensemble de capteurs sans fil, ou unités sensorielles, capables de collecter, traiter, et transmettre de l'information. Grâce aux avancées dans les domaines de la microélectronique et des technologies sans fil, ces systèmes sont à la fois peu volumineux et peu coûteux. Ceci permet leurs deploiements dans différents types d'environnements, afin d'observer l'évolution dans le temps et l'espace de quantités physiques telles que la température, l'humidité, la lumière ou le son.<p><p>Dans le domaine de l'étude environnementale, les systèmes de prise de mesures doivent souvent fonctionner de manière autonome pendant plusieurs mois ou plusieurs années. Les capteurs sans fil ont cependant des ressources limitées, particulièrement en terme d'énergie. Les communications radios étant d'un ordre de grandeur plus coûteuses en énergie que l'utilisation du processeur, la conception de méthodes de collecte de données limitant la transmission de données est devenue l'un des principaux défis soulevés par cette technologie. <p><p>Ce défi peut être abordé de manière efficace par l'utilisation de modèles mathématiques modélisant l'évolution spatiotemporelle des mesures prises par les capteurs. En effet, si un tel modèle peut être utilisé à la place des mesures, d'importants gains en communications peuvent être obtenus en utilisant les paramètres du modèle comme substitut des mesures. Cependant, dans la majorité des cas, peu ou aucune information sur la nature des mesures prises par les capteurs ne sont disponibles, et donc aucun modèle ne peut être a priori défini. Dans ces cas, les techniques issues du domaine de l'apprentissage machine sont particulièrement appropriées. Ces techniques ont pour but de créer ces modèles de façon autonome, en anticipant les mesures à venir sur la base des mesures passées. <p><p>Dans cette thèse, deux contributions sont principalement apportées permettant l'applica-tion de techniques d'apprentissage machine dans le domaine des réseaux de capteurs sans fil. Premièrement, nous proposons une approche qui combine la prédiction de série temporelle avec la sélection de modèles afin de réduire la communication. La logique de cette approche, appelée sélection de modèle adaptive, est de permettre aux unités sensorielles de determiner de manière autonome un modèle de prédiction qui anticipe correctement leurs mesures, tout en réduisant l'utilisation de leur radio.<p><p>Deuxièmement, nous avons conçu une méthode permettant de modéliser de façon distribuée les mesures collectées, qui se base sur l'analyse en composantes principales (ACP). La méthode permet de transformer les mesures le long d'un arbre de routage, de façon à ce que (i) la majeure partie des variations dans les mesures des capteurs soient conservées, et (ii) la charge réseau soit réduite et mieux distribuée, ce qui permet d'augmenter également la durée de vie du réseau. L'approche proposée permet de véritablement distribuer l'ACP, et peut être utilisée pour des applications impliquant la collecte de données, mais également pour la détection ou la classification d'événements. <p> / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Indiana University-Purdue University Indianapolis (IUPUI) / An excess of bone resorption over bone formation leads to osteoporosis, resulting in a reduction of bone mass and an increase in the risk of bone fracture. Anabolic and anti-resorptive drugs are currently available for treatment, however, none of these drugs are able to both promote osteoblastogenesis and reduce osteoclastogenesis. This thesis focused on the role of eukaryotic translation initiation factor 2 alpha (eIF2alpha), which regulates efficiency of translational initiation. The elevation of phosphorylated eIF2alpha was reported to stimulate osteoblastogenesis, but its effects on osteoclastogenesis have not been well understood. Using synthetic chemical agents such as salubrinal and guanabenz that are known to inhibit the de-phosphorylation of eIF2alpha, the role of phosphorylation of eIF2alpha in osteoclastogenesis was investigated in this thesis. The questions addressed herein were: Does the elevation of phosphorylated eIF2alpha (p-eIF2alpha) by salubrinal and guanabenz alter osteoclastogenesis? If so, what regulatory mechanism mediates the process? It was hypothesized that p-eIF2alpha could attenuate the development of osteoclast by regulating the transcription factor(s) amd microRNA(s) involved in osteoclastogenesis. To test this hypothesis, we conducted in vitro and in silico analysis of the responses of RAW 264.7 pre-osteoclast cells to salubrinal and guanabenz. First, the in vitro results revealed that the elevated level of phosphorylated eIF2alpha inhibited the proliferation, differentiation, and maturation of RAW264.7 cells and downregulated the expression of NFATc1, a master transcription factor of osteoclastogenesis. Silencing eIF2alpha by RNA interference suppressed the downregulation of NFATc1, suggesting the involvement of eIF2alpha in regulation of NFATc1. Second, the in silico results using genome-wide expression data and custom-made Matlab programs predicted a set of stimulatory and inhibitory regulator genes as well as microRNAs, which were potentially involved in the regulation of NFATc1. RNA interference experiments indicated that the genes such as Zfyve21 and Ddit4 were primary candidates as an inhibitor of NFATc1. In summary, the results showed that the elevation of p-eIF2alpha by salubrinal and guanabenz leads to attenuation of osteoclastogenesis through the downregulation of NFATc1. The regulatory mechanism is mediated by eIF2alpha signaling, but other signaling pathways are likely to be involved. Together with the previous data showing the stimulatory role of p-eIF2alpha in osteoblastogenesis, the results herein suggest that eIF2alpha-mediated signaling could provide a novel therapeutic target for treatment of osteoporosis by promoting bone formation and reducing bone resorption.

Indiana University-Purdue University Indianapolis (IUPUI) / In general, profiling sensors are low-cost crude imagers that typically utilize a sparse detector array, whereas traditional cameras employ a dense focal-plane array. Profiling sensors are of particular interest in applications that require classification of a sensed object into broad categories, such as human, animal, or vehicle. However, profiling sensors have many other applications in which reliable classification of a crude silhouette or profile produced by the sensor is of value. The notion of a profiling sensor was first realized by a Near-Infrared (N-IR), retro-reflective prototype consisting of a vertical column of sparse detectors. Alternative arrangements of detectors have been implemented in which a subset of the detectors have been offset from the vertical column and placed at arbitrary locations along the anticipated path of the objects of interest. All prior work with the N-IR, retro-reflective profiling sensors has consisted of wired detectors. This thesis surveys prior work and advances this work with a wireless profiling sensor prototype in which each detector is a wireless sensor node and the aggregation of these nodes comprises a profiling sensor’s field of view. In this novel approach, a base station pre-processes the data collected from the sensor nodes, including data realignment, prior to its classification through a back-propagation neural network. Such a wireless detector configuration advances deployment options for N-IR, retro-reflective profiling sensors.