Given the increasing competition in the software industry and the critical consequences of software errors, it has become important for companies to achieve high levels of software quality. While cost reduction and timeliness of projects continue to be important measures, software companies are placing increasing attention on identifying the user needs and better defining software quality from a customer perspective. Software quality goes beyond just correcting the defects that arise from any deviations from the functional requirements. System engineers also have to focus on a large number of quality requirements such as security, availability, reliability, maintainability, performance and temporal correctness requirements. The fulfillment of these run-time observable quality requirements is important for customer satisfaction and project success. Generating early forecasts of potential quality problems can have significant benefits to quality improvement. One approach to better software quality is to improve the overall development cycle in order to prevent the introduction of defects and improve run-time quality factors. Many methods and techniques are available which can be used to forecast quality of an ongoing project such as statistical models, opinion polls, survey methods etc. These methods have known strengths and weaknesses and accurate forecasting is still a major issue. This research utilized a novel approach using prediction markets, which has proved useful in a variety of situations. In a prediction market for software quality, individual estimates from diverse project stakeholders such as project managers, developers, testers, and users were collected at various points in time during the project. Analogous to the financial futures markets, a security (or contract) was defined that represents the quality requirements and various stakeholders traded the securities using the prevailing market price and their private information. The equilibrium market price represents the best aggregate of diverse opinions. Among many software quality factors, this research focused on predicting the software correctness. The goal of the study was to evaluate if a suitably designed prediction market would generate a more accurate estimate of software quality than a survey method which polls subjects. Data were collected using a live software project in three stages: viz., the requirements phase, an early release phase and a final release phase. The efficacy of the market was tested with results from prediction markets by (i) comparing the market outcomes to final project outcome, and (ii) by comparing market outcomes to results of opinion poll. Analysis of data suggests that predictions generated using the prediction market are significantly different from those generated using polls at early release and final release stages. The prediction market estimates were also closer to the actual probability estimates for quality compared to the polls. Overall, the results suggest that suitably designed prediction markets provide better forecasts of potential quality problems than polls.

Estimation fusion, or data fusion for estimation, is the problem of how to best utilize useful information contained in multiple data sets for the purpose of estimating an unknown quantity — a parameter or a process. Estimation fusion with constraints gives rise to challenging theoretical problems given the observations from multiple geometrically dispersed sensors: Under dimensionality constraints, how to preprocess data at each local sensor to achieve the best estimation accuracy at the fusion center? Under communication bandwidth constraints, how to quantize local sensor data to minimize the estimation error at the fusion center? Under constraints on storage, how to optimally update state estimates at the fusion center with out-of-sequence measurements? Under constraints on storage, how to apply the out-of-sequence measurements (OOSM) update algorithm to multi-sensor multi-target tracking in clutter? The present work is devoted to the above topics by applying the best linear unbiased estimation (BLUE) fusion. We propose optimal data compression by reducing sensor data from a higher dimension to a lower dimension with minimal or no performance loss at the fusion center. For single-sensor and some particular multiple-sensor systems, we obtain the explicit optimal compression rule. For a multisensor system with a general dimensionality requirement, we propose the Gauss-Seidel iterative algorithm to search for the optimal compression rule. Another way to accomplish sensor data compression is to find an optimal sensor quantizer. Using BLUE fusion rules, we develop optimal sensor data quantization schemes according to the bit rate constraints in communication between each sensor and the fusion center. For a dynamic system, how to perform the state estimation and sensor quantization update simultaneously is also established, along with a closed form of a recursion for a linear system with additive white Gaussian noise. A globally optimal OOSM update algorithm and a constrained optimal update algorithm are derived to solve one-lag as well as multi-lag OOSM update problems. In order to extend the OOSM update algorithms to multisensor multitarget tracking in clutter, we also study the performance of OOSM update associated with the Probabilistic Data Association (PDA) algorithm.

The main contribution of this thesis is the introduction of Bayesian quantile regression for hidden Markov models, especially when we have to deal with extreme quantile regression analysis, as there is a limited research to inference conditional quantiles for hidden Markov models, under a Bayesian approach. The first objective is to compare Bayesian extreme quantile regression and the classical extreme quantile regression, with the help of simulated data generated by three specific models, which only differ in the error term’s distribution. It is also investigated if and how the error term’s distribution affects Bayesian extreme quantile regression, in terms of parameter and confidence intervals estimation. Bayesian extreme quantile regression is performed by implementing a Metropolis-Hastings algorithm to update our parameters, while the classical extreme quantile regression is performed by using linear programming. Moreover, the same analysis and comparison is performed on a real data set. The results provide strong evidence that our method can be improved, by combining MCMC algorithms and linear programming, in order to obtain better parameter and confidence intervals estimation. After improving our method for Bayesian extreme quantile regression, we extend it by including hidden Markov models. First, we assume a discrete time finite state-space hidden Markov model, where the distribution associated with each hidden state is a) a Normal distribution and b) an asymmetric Laplace distribution. Our aim is to explore the number of hidden states that describe the extreme quantiles of our data sets and check whether a different distribution associated with each hidden state can affect our estimation. Additionally, we also explore whether there are structural changes (breakpoints), by using break-point hidden Markov models. In order to perform this analysis we implement two new MCMC algorithms. The first one updates the parameters and the hidden states by using a Forward-Backward algorithm and Gibbs sampling (when a Normal distribution is assumed), and the second one uses a Forward-Backward algorithm and a mixture of Gibbs and Metropolis-Hastings sampling (when an asymmetric Laplace distribution is assumed). Finally, we consider hidden Markov models, where the hidden state (latent variables) are continuous. For this case of the discrete-time continuous state-space hidden Markov model we implement a method that uses linear programming and the Kalman filter (and Kalman smoother). Our methods are used in order to analyze real interest rates by assuming hidden states, which represent different financial regimes. We show that our methods work very well in terms of parameter estimation and also in hidden state and break-point estimation, which is very useful for the real life applications of those methods.

In this study a new wind gust estimate (WGE) method proposed by Brasseur (2001) is implemented in a limited area climate model (RCA, Rossby Centre regional Atmospheric model). The WGE method assumes that wind gusts develop when air parcels higher up in the boundary layer deflect down to the surface by turbulent eddies. The method also gives an interval of possible gusts by estimating an upper and lower bound of a bounding interval. Two separate storms (December 3-4, 1999 and January 8-9, 2005) and a three month period (November 1, 2004 - January 31, 2005) are simulated with RCA. The results are compared to direct observations and to gridded analysis (MESAN). The result is highly dependent on how well the meteorological fields are represented in RCA. Since the storm of December 1999 was not well captured by RCA, the wind gusts were consequently not correctly estimated. The storm of January 2005 was well captured by the RCA and the wind gusts relatively well described. Both the storm of January 2005 and the simulation over a three month period give rather good estimated gusts over sea areas, while over land there is an obvious overestimation of the calculated gusts. A correction to the estimated gust is necessary in order to make the parameterisation useful. Such a correction is tested in this study. It shows significant improvement over most land areas and also gives a certain underestimation in other areas. / Sammanfattning av ”Beräkning av byiga vindar i RCA” En ny metod (WGE-metoden) för att bestämma byvindar har i den här studien implementerats i en regional klimatmodell (RCA, Rossby Centre regional Atmospheric model). WGE-metoden utgår från att vindbyar genereras när luftpaket högre upp i gränsskiktet förs ner till marken av stora turbulenta virvlar. Ett intervall av möjliga byvindar erhålls genom att en övre och undre gräns för detta intervall beräknas. Två stormar (3-4 december 1999 och 8-9 januari 2005) och en tremånaders period (1 november 2004 – 31 januari 2005) har simulerats, och resultaten har jämförts med mätdata och MESAN. Resultatet är till stor del beroende av hur väl de meteorologiska fälten representeras av RCA. Stormen i december 1999 simulerades dåligt av RCA, vilket medförde att byvinden inte heller simulerades korrekt. Både stormen januari 2005 och simuleringen över tre månader ger en tämligen korrekt byvind över hav, samtidigt som man över land får kraftiga överskattningar av den beräknade byvinden. För att byvind-parametriseringen ska vara användbar krävs korrigeringar för att komma till rätta med överskattningen över land. En korrigering testades i den här studien med resultatet att ett förbättrat resultat över land samtidigt leder till en viss underskattning av byvinden i andra områden.

<p>Various engineering systems have become rapidly automated and intelligent as sensing, communication, and computing technologies have been increasingly advanced. The dynamical behaviors of such systems have also become complicated as they need to meet requirements on performance and safety in various operating conditions. Due to the heterogeneity in its behaviors for different operating modes, it is not appropriate to use a single dynamical model to describe its dynamics, which motivates the development of the stochastic hybrid system (SHS). The SHS is defined as a dynamical system which contains interacting time-evolving continuous state and event-driven discrete state (also called a mode) with uncertainties. Due to its flexibility and effectiveness, the SHS has been widely used for modeling complex engineering systems in many applications such as air traffic control, sensor networks, biological systems, and etc.</p><p>One of the key research areas related to the SHS is the inference or estimation of the states of the SHS, which is also known as the hybrid state estimation. This task is very challenging because both the continuous and discrete states need to be inferred from noisy measurements generated from mixed time-evolving and event-driven behavior of the SHS. This becomes even more difficult when the dynamical behavior or measurement contains nonlinearity, which is the case in many engineering systems that can be modeled as the SHS.</p><p>This research aims to 1) propose a stochastic nonlinear hybrid system model and develop novel nonlinear hybrid state estimation algorithms that can deal with the aforementioned challenges, and 2) apply them to safety-critical applications in air traffic control systems such as aircraft tracking and estimated time of arrival prediction, and unmanned aircraft system traffic management.</p>

Estimation of the covariance matrix of asset returns is a key component of portfolio optimization. Inherent in any estimation technique is the capacity to inaccurately reflect current market conditions. Typical of Markowitz portfolio optimization theory, which we use as the basis for our analysis, is to assume that asset returns are stationary. This assumption inevitably causes an optimized portfolio to fail during a market crash since estimates of covariance matrices of asset returns no longer reflect current conditions. We use the market crash of 2008 to exemplify this fact. A current industry-standard benchmark for estimation is the Ledoit covariance matrix, which attempts to adjust a portfolio’s aggressiveness during varying market conditions. We test this technique against the CALM (Covariance Adjustment for Liability Management Method), which incorporates forward-looking signals for market volatility to reduce portfolio variance, and assess under certain criteria how well each model performs during recent market crash. We show that CALM should be preferred against the sample convariance matrix and Ledoit covariance matrix under some reasonable weight constraints.

Préfigurant la prochaine grande étape dans le domaine de la navigation, la géolocalisation intra-muros est un domaine de recherche très actif depuis quelques années. Alors que la géolocalisation est entrée dans le quotidien de nombreux professionnels et particuliers avec, notamment, le guidage routier assisté, les besoins d'étendre les applications à l'intérieur se font de plus en plus pressants. Cependant, les systèmes existants se heurtent à des contraintes techniques bien supérieures à celles rencontrées à l'extérieur, la faute, notamment, à la propagation chaotique des ondes électromagnétiques dans les environnements confinés et inhomogènes. Nous proposons dans ce manuscrit une approche statistique du problème de géolocalisation d'un mobile à l'intérieur d'un bâtiment utilisant les ondes WiFi environnantes. Ce manuscrit s'articule autour de deux questions centrales : celle de la détermination des cartes de propagation des ondes WiFi dans un bâtiment donné et celle de la construction d'estimateurs des positions du mobile à l'aide de ces cartes de propagation. Le cadre statistique utilisé dans cette thèse afin de répondre à ces questions est celui des modèles de Markov cachés. Nous proposons notamment, dans un cadre paramétrique, une méthode d'inférence permettant l'estimation en ligne des cartes de propagation, sur la base des informations relevées par le mobile. Dans un cadre non-paramétrique, nous avons étudié la possibilité d'estimer les cartes de propagation considérées comme simple fonction régulière sur l'environnement à géolocaliser. Nos résultats sur l'estimation non paramétrique dans les modèles de Markov cachés permettent d'exhiber un estimateur des fonctions de propagation dont la consistance est établie dans un cadre général. La dernière partie du manuscrit porte sur l'estimation de l'arbre de contextes dans les modèles de Markov cachés à longueur variable. / Foreshadowing the next big step in the field of navigation, indoor geolocation has been a very active field of research in the last few years. While geolocation entered the life of many individuals and professionals, particularly through assisted navigation systems on roads, needs to extend the applications inside the buildings are more and more present. However, existing systems face many more technical constraints than those encountered outside, including the chaotic propagation of electromagnetic waves in confined and inhomogeneous environments. In this manuscript, we propose a statistical approach to the problem of geolocation of a mobile device inside a building, using the WiFi surrounding waves. This manuscript focuses on two central issues: the determination of WiFi wave propagation maps inside a building and the construction of estimators of the mobile's positions using these propagation maps. The statistical framework used in this thesis to answer these questions is that of hidden Markov models. We propose, in a parametric framework, an inference method for the online estimation of the propagation maps, on the basis of the informations reported by the mobile. In a nonparametric framework, we investigated the possibility of estimating the propagation maps considered as a single regular function on the environment that we wish to geolocate. Our results on the nonparametric estimation in hidden Markov models make it possible to produce estimators of the propagation functions whose consistency is established in a general framework. The last part of the manuscript deals with the estimation of the context tree in variable length hidden Markov models.

Ce travail de thèse traite de la problématique d’estimation des défauts et de l’étathybride pour une classe de systèmes linéaires à commutations. L’objectif est de développerune méthode afin de synthétiser un observateur et un estimateur dédiésrespectivement à l’estimation de l’état hybride et des défauts. Après la présentationd’un état de l’art sur les techniques d’estimation, de stabilité et de diagnosticpour les systèmes linéaires à commutations, la thèse est scindée en deux parties.La première partie propose une méthode d’estimation de l’état continu et desdéfauts dans le cas où l’état discret du système est connu. En se basant sur unetransformation de coordonnées qui découple un sous-ensemble de l’état du systèmedes défauts, nous avons synthétisé dans un premier temps un observateur hybridepour estimer l’état continu du système, et dans un second temps, un estimateurpermettant la reconstruction des défauts. L’estimateur de défauts proposé dépendde la dérivée de la sortie du système. Pour cette raison, un différenciateur robusteet exact basé sur des techniques des modes glissants est utilisé. Dans la secondepartie de ce mémoire, l’état discret du système est supposé inconnu. Une approchebasée sur des méthodes algébriques est proposée afin d’estimer les instants decommutation entre les différents sous-systèmes. Par la suite, l’estimation de l’étathybride (état continu et état discret) et des défauts est considérée dans le cas oùl’état discret du système est inconnu. Ce dernier est reconstruit en se basant surles instant de commutation estimé et sur une séquence de commutation connue.L’état continu du système est estimé en se basant sur une méthode de placementde pôles permettant d’améliorer les performances de la phase transitoire. Enfin, enexploitant des résultats trouvés dans la première partie, l’estimation des défautsest considérée en estimant la sortie du système avec un différenciateur algébrique.Ce différenciateur donne des résultats plus intéressants vis-à-vis du bruit par rapportau différenciateur basé sur les techniques des modes glissants utilisé dans lapremière partie. / This work deals with the problem of estimation of fault and hybrid state for a classof switched linear systems. The objective is to develop a method to synthesize anobserver and an estimator dedicated respectively to the estimation of the hybridstate and the faults. After presenting a state of the art for estimation, stabilityand diagnostic techniques for switched linear systems, the report is divided intotwo parts. The first part proposes a method for estimating the continuous stateand the faults in the case where the discrete state of the system is known. Basedon a coordinate transformation which decouples a subset of the state of the systemof faults, we first synthesized a hybrid observer to estimate the continuous stateof the system and, in a second step, an estimator allowing the reconstructionof faults. The proposed fault estimator depends on the derivative of the systemoutput. For this reason, a robust and accurate differentiator based on sliding modetechniques is used. In the second part of this paper, the discrete state of the systemis assumed unknown. An algebraic approach is proposed to estimate the switchingtimes between the different subsystems. Thereafter, the estimation of the hybridstate (continuous and discrete state) and of the faults is considered in the casewhere the discrete state of the system is unknown. The latter is reconstructedfrom the estimated switching times and on a known switching sequence. Thecontinuous state of the system is estimated using a pole placement method allowingimprove the performances of the transient phase. Finally, by exploiting the resultsfound in the first part, the estimation of the faults is considered by estimatingthe output of the system with an algebraic differentiator. This differentiator givesmore interesting results at the noise compared to the differentiator based on thesliding mode techniques used in the first part.

Autonomous aerial, terrestrial, and marine vehicles provide a platform for several applications including cargo transport, information gathering, surveillance, reconnaissance, and search-and-rescue. To enable such applications, two main technical problems are commonly addressed.On the one hand, the motion-planning problem addresses optimal motion to a destination: an application example is the delivery of a package in the shortest time with least fuel. Solutions to this problem often assume that all relevant information about the environment is available, possibly with some uncertainty. On the other hand, the information gathering problem addresses the maximization of some metric of information about the environment: application examples include such as surveillance and environmental monitoring. Solutions to the motion-planning problem in vehicular autonomy assume that information about the environment is available from three sources: (1) the vehicle’s own onboard sensors, (2) stationary sensor installations (e.g. ground radar stations), and (3) other information gathering vehicles, i.e., mobile sensors, especially with the recent emphasis on collaborative teams of autonomous vehicles with heterogeneous capabilities. Each source typically processes the raw sensor data via estimation algorithms. These estimates are then available to a decision making system such as a motion- planning algorithm. The motion-planner may use some or all of the estimates provided. There is an underlying assumption of “separation� between the motion-planning algorithm and the information about environment. This separation is common in linear feedback control systems, where estimation algorithms are designed independent of control laws, and control laws are designed with the assumption that the estimated state is the true state. In the case of motion-planning, there is no reason to believe that such a separation between the motion-planning algorithm and the sources of estimated environment information will lead to optimal motion plans, even if the motion planner and the estimators are themselves optimal. The goal of this dissertation is to investigate whether the removal of this separation, via interactive motion-planning and sensing, can significantly improve the optimality of motion- planning. The major contribution of this work is interactive planning and sensing. We consider the problem of planning the path of a vehicle, which we refer to as the actor, to traverse a threat field with minimum threat exposure. The threat field is an unknown, time- variant, and strictly positive scalar field defined on a compact 2D spatial domain – the actor’s workspace. The threat field is estimated by a network of mobile sensors that can measure the threat field pointwise. All measurements are noisy. The objective is to determine a path for the actor to reach a desired goal with minimum risk, which is a measure sensitive not only to the threat exposure itself, but also to the uncertainty therein. A novelty of this problem setup is that the actor can communicate with the sensor network and request that the sensors position themselves in a procedure we call sensor reconfiguration such that the actor’s risk is minimized. This work continues with a foundation in motion planning in time-varying fields where waiting is a control input. Waiting is examined in the context of finding an optimal path with considerations for the cost of exposure to a threat field, the cost of movement, and the cost of waiting. For example, an application where waiting may be beneficial in motion-planning is the delivery of a package where adverse weather may pose a risk to the safety of a UAV and its cargo. In such scenarios, an optimal plan may include “waiting until the storm passes.� Results on computational efficiency and optimality of considering waiting in path- planning algorithms are presented. In addition, the relationship of waiting in a time- varying field represented with varying levels of resolution, or multiresolution is studied. Interactive planning and sensing is further developed for the case of time-varying environments. This proposed extension allows for the evaluation of different mission windows, finite sensor network reconfiguration durations, finite planning durations, and varying number of available sensors. Finally, the proposed method considers the effect of waiting in the path planner under the interactive planning and sensing for time-varying fields framework. Future work considers various extensions of the proposed interactive planning and sensing framework including: generalizing the environment using Gaussian processes, sensor reconfiguration costs, multiresolution implementations, nonlinear parameters, decentralized sensor networks and an application to aerial payload delivery by parafoil.

This thesis intends to advance educational research by providing exploratory insights about the roles of, and relationships between, the actions, language, and gestures of college and elementary-aged students surrounding measurement estimation. To the best of my knowledge, prior research has examined the role of speech and gestures as they relate to areas of mathematics such as algebra and geometry, however, this work has not been extended to the area of measurement. Similarly, language and gesture have been explored but the three-way interplay between actions during problem-solving, and the language and gestures observed during explanations after problem solving has not been investigated in mathematics. To actualize the findings from this research in practice, this thesis uses the findings from two studies on behavior during measurement tasks to propose text and image support for an elementary-aged measurement game, EstimateIT!, to support students as they practice how to measure objects and develop conceptual skills through embodied game play. Specifically, this thesis intends to provide 1) a synthesis of the work on gestures in mathematics as well as the research methods used to study gestures, 2) a coding guide to analyze the gestures of mathematics learners, as well as their actions and language, 3) an application of the coding guide to explore the behavior of college and elementary students during measurement estimation tasks, and 4) proposals for action-guiding support for EstimateIT! to help elementary students develop and reinforce an understanding of measurement during gameplay based on the more mature strategies demonstrated by college students as they complete similar tasks.