Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision- making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day. In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding time-varying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The time-varying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limited-memory discrete Kalman filter (ALMF). The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the real-time information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions. It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of time-varying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a well-defined normal distribution.

Nous nous intéressons à estimer la moyenne d'une variable aléatoire de loi à queue lourde. Nous adoptons une approche plus robuste que la moyenne empirique classique communément utilisée. L'objectif est de développer des inégalités de concentration de type sous-gaussien sur l'erreur d'estimation. En d'autres termes, nous cherchons à garantir une forte concentration sous une hypothèse plus faible que la bornitude : une variance finie. Deux estimateurs de la moyenne pour une loi à support réel sont invoqués et leurs résultats de concentration sont rappelés. Plusieurs adaptations en dimension supérieure sont envisagées. L'utilisation appropriée de ces estimateurs nous permet d'introduire une nouvelle technique de minimisation du risque empirique pour des variables aléatoires à queue lourde. Quelques applications de cette technique sont développées. Nous appuyons ces résultats sur des simulations sur des jeux de données simulées. Dans un troisième temps, nous étudions un problème d'estimation multivarié dans le cadre des U-statistiques où les estimateurs précédents offrent, là aussi, une généralisation naturelle d'estimateurs présents dans la littérature. / In this thesis, we are interested in estimating the mean of heavy-tailed random variables. We focus on a robust estimation of the mean approach as an alternative to the classical empirical mean estimation. The goal is to develop sub-Gaussian concentration inequalities for the estimating error. In other words, we seek strong concentration results usually obtained for bounded random variables, in the context where the bounded condition is replaced by a finite variance condition. Two existing estimators of the mean of a real-valued random variable are invoked and their concentration results are recalled. Several new higher dimension adaptations are discussed. Using those estimators, we introduce a new version of empirical risk minimization for heavy-tailed random variables. Some applications are developed. These results are illustrated by simulations on artificial data samples. Lastly, we study the multivariate case in the U-statistics context. A natural generalization of existing estimators is offered, once again, by previous estimators.

As a classic parameter originated from the binomial distribution, the binomial pro- portion has been well studied in the literature due to its wide range of applications. In contrast, the reciprocal of the binomial proportion, also known as the inverse proportion, is often overlooked, although it plays an important role in sampling designs and clinical studies. To estimate the inverse proportion, a simple method is to apply the maximum likelihood estimation (MLE). This estimator is, however, not a valid estimator because it suffers from the zero-event problem, which occurs when there is no successful event in the trials. At first, we review a number of methods proposed to overcome the zero-event problem and discuss whether they are feasible to estimate the inverse proportion. Inspired by the Wilson (1927) and Agresti and Coull (1998), in this thesis, we focus on a family of shrinkage estimators of the inverse proportion and propose to derive the optimal estimator within this family. The shrinkage estimator overcomes the zero-event problem by including a positive shrinkage parameter, which is intrinsically related to the expected value of the resulting estimator. To find the best shrinkage parameter, the relationship between the shrinkage parameter and the estimation bias of the shrinkage estimator is investigated systematically. Note that the explicit expression of the expected value function of the estimator and the best shrinkage parameter are quite complicated to compute when the number of trials is large. Hence, we review three methods in the literature which were proposed to approximate the expected value function. And after being inspired, we propose a new approximate formula for the expected value function and derive an approximate solution of the optimal shrinkage parameter by the Taylor expansion. Because there still exist an unknown binomial proportion in the optimal shrinkage parameter, we suggest a plug-in estimator for the unknown proportion with an adaptive threshold. Finally, simulation studies are conducted to evaluate the performance of our new estimator. As baselines for comparison, we also include the Fattorini estimator, the Haldane estimator and a piecewise estimator in the simulations. According to the simulation results, the new estimator is able to achieve a better or equally good performance compared with the Fattorini estimators in most settings. Hence, our new estimator can be a reliable estimator for the inverse proportion in most practical cases

L’estimation de pose et l’interaction 3D sont les fondements de base d’un système de réalité augmentée (RA). L’objectif de cette thèse étant de traiter ces deux problématiques, nous présentons dans ce mémoire un état de l’art qui regroupe : approches, techniques et technologies relatives à l’estimation de pose et à l’interaction 3D en RA. Puis nous faisons le bilan sur les travaux menés jusqu'à aujourd’hui.A cet effet, nos contributions dans ce vaste domaine sont dans les deux parties : vision et interaction 3D. Nous avons proposé un nouveau détecteur et descripteur binaire nommé MOBIL qui effectue une comparaison binaire des moments géométriques. Par la suite nous avons proposé deux améliorations de notre descripteur. MOBIL_2B et POLAR_MOBIL.En outre, nous avons utilisé notre descripteur avec l’approche PTAM (Parallel Tracking and Mapping) afin d’assurer le recalage des objets virtuels en immersion mobile de l’utilisateur en RA.Nous avons également proposé une technique d’interaction pour la RA, appelée « Zoom-in » qui facilite la sélection et la manipulation des objets virtuels distants. Cette technique est basée sur le zoom de l’image et des objets virtuels recalé sur l’image. Les objets virtuels sont mis à la portée de l’utilisateur en gardant le recalage par rapport à la scène.Ce mémoire se termine par une conclusion générale qui fait le point sur l’essentiel de ce travail et ouvre de nouvelles perspectives. / Pose estimation and 3D interaction are the essential basis for any Augmented Reality (AR) system. We aim to treat those two fields in order to offer a pertinent AR system that allows a mobile immersion and natural interaction. In this optic, this thesis provides an overall consistent state of the art in both pose estimation and 3D interaction for AR.In addition, this thesis details our contributions that consists of MOBIL: a binary descriptor that compares geometric moments of the patch through a binary test. Two improvements of this descriptor: MOBIL_2B and POLAR_MOBIL are proposed in order to enhance its robustness.We used this descriptor with PTAM technique to ensure the user pose estimation respectively for the selection/manipulation task and the navigation task.On the other hand, we proposed a novel 3D interaction technique called “Zoom-In”, designed for augmented reality applications. This technique is based on the zoom of the captured image. It calculates the 3D transformation relative to the selected object. This technique allows user selecting and manipulating distant virtual objects by bringing them within the user arm’s reach by zooming in the captured image, and re-estimating the user pose thanks to our proposed descriptor. Finally, we present a conclusion that describes the essential of this work and provide perspective and future work.

The development of a fleet of flexible and ruggedized unmanned ground vehicles for use in autonomy and distributed sensing research has resulted in a mature platform with proven capabilities. Each Mapping Autonomous Ground Vehicle (MAGV) is capable of travel on- and off-road, speeds up to 10 mph, and its sturdy construction with a rugged suspension cushions onboard instruments from vibrations. The large battery capacity can sustain at least eight hours of hard use, including powering all onboard electronics. The MAGV is fitted with a high accuracy GPS/INS system for centimeter-accuracy localization and a powerful but compact onboard computer. The integrated wireless communications allow high-bandwidth data communication between the MAGV fleet and a base station. The platform can additionally be fitted with a wide array of sensors, including LIDAR and stereovision cameras, and is designed with ample space to allow the mounting of any future data gathering devices. The platform has already taken a central role in the development of new algorithms for map creation with modern sensing technology, and was deployed to collect data for the demonstration of the map estimation algorithms outlined in this thesis. A wavelet basis combined with a state estimator is demonstrated to be effective for approximating a global map of a given area with complex features. The recursive least squares state estimator is highly effective at rejecting transient features, such as pedestrians frequently passing through the field of view, while retaining the shape of the walls and terrain features. The ability to vary the map resolution allows the mapping station to trade detail for a faster map update processing time. In its current implementation, the global map estimator supports the acquisition and integration of data from multiple simultaneous mobile sources. Because each scan is registered using the position of the vehicle when it is recorded, there is no difference between receiving all data from a single agent, or multiple agents working cooperatively gathering data in the same area. The wavelet basis also offers several opportunities for reducing communications overhead through data compression. In particular, we have demonstrated that simple thresholding of the least significant wavelet coefficients results in a significant reduction in data size with no noticeable reduction in fidelity of the reconstructed map estimate. / Master of Science

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<p> This thesis is concerned with the properties of sequential binomial estimation. It illustrates the construction of optimal sequential binomial sampling plans for point estimation problems in which, according to custom, each loss function is taken to be a constant times the square of the error. The way such a constant affects the sizes of the constructed sampling plans is also within the scope of this thesis.</p> / Thesis / Master of Science (MSc)

The main idea of this thesis is to de ne and formulate the role of bias estimation in multitarget{multisensor scenarios as a general framework for various measurement types. After a brief introduction of the work that has been done in this thesis, three main contributions are explained in detail, which exercise the novel ideas. Starting with radar measurements, a new bias estimation method that can estimate o set and scaling biases in large network of radars is proposed. Further, Cram er{Rao Lower Bound is calculated for the bias estimation algorithm to show the theoretical accuracy that can be achieved by the proposed method. In practice, communication loss is also part of the distributed systems, which sometimes can not be avoided. A novel technique is also developed to accompany the proposed bias estimation method in this thesis to compensate for communication loss at di erent rates by the use of tracklets. Next, bearing{only measurements are considered. Biases in this type of measurement can be di cult to tackle because the measurement noise and systematic biases are normally larger than in radar measurements. In addition, target observability is sensitive to sensor{target alignment and can vary over time. In a multitarget{ multisensor bearing{only scenario with biases, a new model is proposed for the biases that is decoupled form the bearing{only measurements. These decoupled bias measurements then are used in a maximum likelihood batch estimator to estimate the biases and then be used for compensation. The thesis is then expanded by applying bias estimation algorithms into video sensor measurements. Video sensor measurements are increasingly implemented in distributed systems because of their economical bene ts. However, geo{location and geo{registration of the targets must be considered in such systems. In last part of the thesis, a new approach proposed for modeling and estimation of biases in a two video sensor platform which can be used as a standalone algorithm. The proposed algorithm can estimate the gimbal elevation and azimuth biases e ectively. It is worth noting that in all parts of the thesis, simulation results of various scenarios with di erent parameter settings are presented to support the ideas, the accuracy, mathematical modelings and proposed algorithms. These results show that the bias estimation methods that have been conducted in this thesis are viable and can handle larger biases and measurement errors than previously proposed methods. Finally, the thesis conclude with suggestions for future research in three main directions. / Thesis / Doctor of Philosophy (PhD)

Thesis (Ph.D.)--Boston University. / Effects of two independent variables -- expectations of liking or disliking other individuals, and evaluative reactions about self from these others on self concept and liking for others, were experimentally studied, as an application of Heider's theory of balanced states. Heider has postulated a tendency for individuals to reach a state in which interpersonal cognitions are balanced, or consistent, with one another. Several hypotheses were generated by applying balance theory to the relationships among these variables [TRUNCATED]

In order to identify ways to improve cost estimation, especially early in design, cost estimation needs to be viewed and represented as a process. An important activity within the cost estimation process is assessing the cost risk of a system. A decision-support tool that assesses cost risk should represent the impact of subsystem or system-level uncertainty and provide mechanisms to help select among competing designs. In order to address these problems, a generic cost estimation process was developed. It is based on an extensive review of the cost estimation literature. Also, a hierarchicial product structure, model-based approach and tool to estimate system-level cost risk was developed. This tool provides a link between cost models and cost elements for each component, mechanisms for determining the impact of risk on the cost of the design, and outputs used for selecting among alternative competing designs.