Thèse de doctorat : Mécanique : Paris 12 : 2006. / Titre provenant de l'écran-titre. Pagination : 146 f. Bibliogr. f. 135-146.

Thèse de doctorat : Sciences du vivant. Aspects moléculaires et cellulaires de la biologie. Histologie moléculaire : Strasbourg 1 : 2007. / Thèse soutenue sur un ensemble de travaux. Titre provenant de l'écran-titre. Bibliogr. 41 p.

Studien behandlar Stratford, London och syftar till att utifrån olika perspektiv analysera den pågående stadsförnyelsens betydelse för Stratford och dess invånare samt vilken del de Olympiska Spelen haft i stadsförnyelsen. För att undersöka detta fokuserar uppsatsen på att studera konsekvenserna av stadsförnyelsen för faktorer som bostads- och arbetsmöjligheter i området. Det undersöks utifrån frågeställningarna: hur har stadsförnyelsen i Stratford påverkat Stratford och dess invånare, med betoning på arbete och bostad samt på vilka sätt har OS haft betydelse för stadsförnyelsen i Stratford. Studien relaterar till teorier kring gentrifiering och globala städer. Uppsatsen har baserats på semistrukturerade intervjuer, litteraturstudier av dokument från bland annat London Borough of Newham och London Legacy development Corporation samt observation. Huvudsakliga resultat är att stadsförnyelsen inneburit fler bostads- och arbetsmöjligheter men då området till stor del utvecklas för en medelklass indikerar resultatet att de fattigare lokalinvånarna på sikt kommer att tvingas bort från området som är på väg att gentrifieras.

Thesis (Ph.D)-University of Bath, 2006. / Title appears on item as: Undergraduate dissertation: Information retrieval and gathering: An experimental prototype for Mac OS X. Includes bibliographical references.

Thèse (M.Sc.)--Université Laval, 2004. / Bibliogr.: f. 87-95. Publié aussi en version électronique.

Texte remanié de: Habil.-Schr.--Kiel--Mathematisch-Naturwissaftlichen Fakultät, 1966. / Bibliogr. p. 216-226.

With the present need for on the move networking, innovative technologies strive to establish a technological basis for managing secure and reliable systems in a highly interconnected information enabled world, and prevent reliance on a fixed networking infrastructure, hence the implementation of ad-hoc networks. There are numerous applications where ad-hoc networks are deployed including military, tele-health and mobile education. As such the need for security is imperative. Not much research work has been invested in the area of trust in ad hoc networks which proves to be a challenging subject relative to the characteristics of these types of networks. The objective of this thesis was to develop a model for trust computation between the nodes of the network. Eventually, the confidence level for each node was quantified, which lead to a better constancy among the nodes. Therefore, communication was trust worthy, and malicious nodes were punished and secluded from the network.

The recent advancement in computational and communication systems has led to the introduction of high-performing neural networks and high-speed wireless vehicular communication networks. As a result, new technologies such as cooperative perception and cognition have emerged, addressing the inherent limitations of sensory devices by providing solutions for the detection of partially occluded targets and expanding the sensing range. However, designing a reliable cooperative cognition or perception system requires addressing the challenges caused by limited network resources and discrepancies between the data shared by different sources. We examine the requirements, limitations, and performance of different cooperative perception techniques, and present an in-depth analysis of the notion of Deep Feature Sharing (DFS). We explore different cooperative object detection designs and evaluate their performance in terms of average precision. We use the Volony dataset for our experimental study. The results confirm that the DFS methods are significantly less sensitive to the localization error caused by GPS noise. Furthermore, the results attest that detection gain of DFS methods caused by adding more cooperative participants in the scenes is comparable to raw information sharing technique while DFS enables flexibility in design toward satisfying communication requirements. Furthermore, in the environments where there is noise in GPS positioning estimates, cooperative perception performance will decrease. To alleviate the performance decrease we introduce a method to estimate the relative positioning of cooperative vehicles by comparing feature maps extracted from LIDAR observations of the cooperative vehicles. The results show that GPS positioning estimates of all participating vehicles will be improved as the number of cooperative vehicles increases in the scene.

Driven by the ever-increasing data volume through the Internet, the per-port speed of network devices reached 400 Gbps, and high-end switches are capable of processing 25.6 Tbps of network traffic. To improve the efficiency and security of the network, network traffic measurement becomes more important than ever. For fast and accurate traffic measurement, managing an accurate working set of active flows (WSAF) at line rates is a key challenge. WSAF is usually located in high-speed but expensive memories, such as TCAM or SRAM, and thus their capacity is quite limited. To scale up the per-flow measurement, we pursue three thrusts. In the first thrust, we propose to use In-DRAM WSAF and put a compact data structure (i.e., sketch) called FlowRegulator before WSAF to compensate for DRAM's slow access time. Per our results, FlowRegulator can substantially reduce massive influxes to WSAF without compromising measurement accuracy. In the second thrust, we integrate our sketch into a network system and propose an SDN-based WLAN monitoring and management framework called RFlow+, which can overcome the limitations of existing traffic measurement solutions (e.g., OpenFlow and sFlow), such as a limited view, incomplete flow statistics, and poor trade-off between measurement accuracy and CPU/network overheads. In the third thrust, we introduce a novel sampling scheme to deal with the poor trade-off that is provided by the standard simple random sampling (SRS). Even though SRS has been widely used in practice because of its simplicity, it provides non-uniform sampling rates for different flows, because it samples packets over an aggregated data flow. Starting with a simple idea that "independent per-flow packet sampling provides the most accurate estimation of each flow," we introduce a new concept of per-flow systematic sampling, aiming to provide the same sampling rate across all flows. In addition, we provide a concrete sampling method called SketchFlow, which approximates the idea of the per-flow systematic sampling using a sketch saturation event.

In this Dissertation, we have investigated the underlying theories of probabilistic models for application in large scale machine learning tasks. First, we introduce the maximum probability theorem and its consequences. We present a theoretical framework of probabilistic learning derived from the Maximum Probability (MP) Theorem. In this probabilistic framework, a model is defined as an event in the probability space, and a model or the associated event - either the true underlying model or the parameterized model - have a quantified probability measure. This quantification of a model's probability measure is derived from the MP Theorem, where we have shown that an event's probability measure has an upper-bound given its conditional distribution on an arbitrary random variable. Through this alternative framework, the notion of model parameters is encompassed in the definition of the model or the associated event. Therefore, this framework deviates from the conventional approach of assuming a prior on the model parameters. Instead, the regularizing effects of assuming prior over parameters are imposed through maximizing probabilities of models or according to information theory, minimizing the information content of a model. The probability of a model in MP framework is invariant to reparameterization and is solely dependent on the model's likelihood function. Also, rather than maximizing the posterior in a conventional Bayesian setting, the objective function in our alternative framework is defined as the probability of set operations (e.g. intersection) on the event of the true underlying model and the event of the model at hand. The MP framework adds clarity to probabilistic learning through solidifying the definition of probabilistic models, quantifying their probabilities, and providing a visual understanding of objective functions. Furthermore, we discuss Finite "K"onvolutional Neural Networks (FKNN) as a step towards constructing a discrete counterpart to Convolutional Neural Networks (CNN). In FKNNs, the linear and non-linear components of the network are naturally derived and justified in terms of Bayes' Theorem. The building blocks of our network are classifiers operating on the domain of categorical distributions. This property enables the composition of Bayesian classifiers to construct more expressive models. The resulting composite model consists of linear and non-linear components, which are remarkably similar to modern CNNs and their variations, yet the roles of parameters, variables, and layers are less ambiguous from a statistical perspective. Parameters and variables represent categorical distributions in FKNNs, providing the potential for usage of statistical and information-theoretical methods. We further introduce two methods of parameter initialization, inspired by the natural parameterization of categorical distribution and the Jeffreys priors. Finally, we transform some well-known CNN architectures for image classification task into their FKNN counterparts and compare their performance. Experimental results show that the FKNNs and their corresponding CNN architecture exhibit comparable performances. The functional similarity of CNNs and FKNNs, the empirical results, and the explicit connection of FKNNs and Bayes' rule encourage the investigation of finite-state probabilistic models.