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Les acquisitions de télédétection ont des caractéristiques complémentaires en termes de résolution spatiale et temporelle et peuvent mesurer différents aspects de la couverture neigeuse (propriétés physiques de surface, type de neige, etc.). En combinant plusieurs acquisitions, il devrait être possible d'obtenir un suivi précis et continu de la neige. Cependant, cet objectif se heurte à la complexité du traitement des images satellites et à la confusion possible entre les différents matériaux observés. Plus particulièrement, l’accès à l’information fractionnelle, c’est-à-dire à la proportion de neige dans chaque pixel, nécessite de retrouver la proportion de l’ensemble des matériaux qui se trouvent dans celui-ci. Ces proportions sont accessibles via des méthodes d’inversions ou démélange spectral se basant sur la résolution spectrale des images obtenues. Le défi général est alors d’arriver à exploiter correctement les différentes informations de natures différentes qui nous sont apportées par les différentes acquisitions afin de produire des cartes d’enneigement précises. Les objectifs de la thèse sont alors au nombre de trois et peuvent se résumer par trois grandes interrogations qui permettent de traiter les différents points évoqués:- Quelles sont les limitations actuelles de l’état de l'art pour l’observation spatiale optique de la neige ?- Comment exploiter les séries temporelles pour s’adapter à la variabilité spectrale des matériaux ?- Est-il possible de généraliser la fusion de données pour une acquisition multimodale à partir de capteurs optiques ?Une étude complète des différents produits de neige issus du satellite MODIS est ainsi proposée, permettant l’identification des nombreuses limitations dont la principale est le haut taux d’erreurs lors de la reconstitution de la fraction (environ 30%). Parmi ces résultats sont notamment identifiés des problèmes liés aux méthodes de démélange face à la variabilité spectrale des matériaux. Face à ces limitations nous avons exploité les séries temporelles MODIS pour proposer une nouvelle approche d’estimation des endmembers, étape critique du démélange spectral. La faible évolution temporelle du milieu (hors neige) est alors utilisée pour contraindre l’estimation des endmembers non seulement sur l’image d’intérêt, mais également sur les images des jours précédents. L’efficacité de cette approche bien que démontrée ici reste sujette aux limitations de résolution spatiale intrinsèques au capteur. Des expérimentations sur la fusion de donnée, à même de pouvoir améliorer la qualité des images, ont par conséquent été réalisées. Devant les limitations de ces méthodes dans le cas des capteurs multispectraux utilisés, une nouvelle approche de fusion a été proposée. Via la formulation d’un nouveau modèle et sa résolution, la fusion entre des capteurs optiques de tous types peut être réalisée sans considération de recouvrement spectral. Les différentes expérimentations sur l’estimation de cartes de neige montrent un intérêt certain d’une meilleure résolution spatiale pour isoler les zones enneigées. Ce travail montre ainsi les nouvelles possibilités de développement pour l’observation de la neige, mais également les évolutions de l’utilisation combinée des images satellites pour l’observation de la Terre en général. / Remote sensing acquisitions have complementary characteristics in terms of spatial and temporal resolution and can measure different aspects of snow cover (e.g., surface physical properties and snow type). By combining several acquisitions, it should be possible to obtain a precise and continuous monitoring of the snow. However, this task has to face the complexity of processing satellite images and the possible confusion between different materials observed. In particular, the estimation of fractional information, i.e., the amount of snow in each pixel, requires to know the proportion of the materials present in a scene. These proportions can be obtained performing spectral unmixing. The challenge is then to effectively exploit the information of different natures that are provided by the multiple acquisitions in order to produce accurate snow maps.Three main objectives are addressed by this thesis and can be summarized by the three following questions:- What are the current limitations of state-of-the-art techniques for the estimation of snow cover extent from optical observations?- How to exploit a time series for coping with the spectral variability of materials?- How can we take advantage of multimodal acquisitions from optical sensors for estimating snow cover maps?A complete study of the various snow products from the MODIS satellite is proposed. It allows the identification of numerous limitations, the main one being the high rate of errors during the estimation of the snow fraction (approximately 30%).The experimental analysis allowed to highlight the sensitivity of the spectral unmixing methods against the spectral variability of materials.Given these limitations, we have exploited the MODIS time series to propose a new endmembers estimation approach, addressing a critical step in spectral unmixing. The low temporal evolution of the medium (except snow) is then used to constrain the estimation of the endmembers not only on the image of interest, but also on images of the previous days. The effectiveness of this approach, although demonstrated here, remains limited by the spatial resolution of the sensor.Data fusion has been considered aiming at taking advantage of multiple acquisitions with different characteristics in term of resolution available on the same scene. Given the limitations of the actual methods in the case of multispectral sensors, a new fusion approach has been proposed. Through the formulation of a new model and its resolution, the fusion between optical sensors of all types can be achieved without consideration of their characteristics. The various experiments on the estimation of snow maps show a clear interest of a better spatial resolution to isolate the snow covered areas. The improvement in spectral resolution will improve future approaches based on spectral unmixing.This work explores the new possibilities of development for the observation of snow, but also for the combined use of the satellite images for the observation of the Earth in general.

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.

This thesis presents the unified bias analysis of subspace-based DOA estimation algorithms in terms of physical parameters such as source separation, signal coherence, number of senors and snapshots. The analysis reveals the direct relationship between the performance of the DOA algorithms and signal measurement conditions. Insights into different algorithms are provided. Based upon previous first-order subspace perturbations, second-order subspace perturbations are developed which provide basis for bias analysis and unification. Simulations verifying the theoretical bias analysis are presented.

Bibliography: p. 231-232. / xvi, 232 p. : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / The aim of this thesis is detecting and tracking objects at large ranges, when no target features are visible, with imaging type sensors. A system which estimates the optical flow of the scene in a parallel architecture is developed. The architecture is similar to that of an artifical neural network. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1994

<p>This thesis describes the use of nuclear magnetic resonance (NMR) spectrometry as an analytical tool. The theory of NMR spectroscopy in general and quantitative NMR spectrometry (qNMR) in particular is described and the instrumental properties and parameter setups for qNMR measurements are discussed. Examples of qNMR are presented by impurity determination of pharmaceutical compounds and analysis of urine samples from rats fed with either water or a drug (metabolic profiling). The instrumental parameter setup of qNMR and traditional data pre-treatments are examined. Spectral smoothing by convolution with a triangular function, which is an unusual application in this context, was shown to be successful regarding the sensitivity and robustness of the method in paper II. In addition, papers III and IV comprise the field of peak alignment, especially designed for ¹H-NMR spectra of urine samples. This is an important preprocessing tool when multivariate analysis is to be applied. A novel peak alignment method was developed and compared to the traditional bucketing approach and a conceptually different alignment method.</p><p>Univariate, multivariate, linear and nonlinear data analyses were applied to qNMR data. In papers I–II, calibration models were created to examine the potential of qNMR for these applications. The data analysis in papers III–VI was mainly explorative. The potential of data fusion and data correlation was examined in order to increase the possibilities of analysing the highly complex samples from metabolic profiling (papers V–VI). Data from LC/MS analysis of the same samples were used with the ¹H-NMR data in different ways. Correlation analyses between the¹H-NMR data and the drug metabolites identified from the LC/MS data were also performed. In this process, data fusion proved to be a valuable tool.</p>

The research theme of this dissertation is the multiple-vehicles cooperative perception (or cooperative perception) applied in the context of intelligent vehicle systems. The general methodology of the presented works in this dissertation is to realize multiple-intelligent vehicles cooperative perception, which aims at providing better vehicle perception result compared with single vehicle perception (or non-cooperative perception). Instead of focusing our research works on the absolute performance of cooperative perception, we focus on the general mechanisms which enable the realization of cooperative localization and cooperative mapping (and moving objects detection), considering that localization and mapping are two underlying tasks for an intelligent vehicle system. We also exploit the possibility to realize certain augmented reality effect with the help of basic cooperative perception functionalities; we name this kind of practice as cooperative augmented reality. Naturally, the contributions of the presented works consist in three aspects: cooperative localization, cooperative local mapping and moving objects detection, and cooperative augmented reality.

This thesis describes the use of nuclear magnetic resonance (NMR) spectrometry as an analytical tool. The theory of NMR spectroscopy in general and quantitative NMR spectrometry (qNMR) in particular is described and the instrumental properties and parameter setups for qNMR measurements are discussed. Examples of qNMR are presented by impurity determination of pharmaceutical compounds and analysis of urine samples from rats fed with either water or a drug (metabolic profiling). The instrumental parameter setup of qNMR and traditional data pre-treatments are examined. Spectral smoothing by convolution with a triangular function, which is an unusual application in this context, was shown to be successful regarding the sensitivity and robustness of the method in paper II. In addition, papers III and IV comprise the field of peak alignment, especially designed for 1H-NMR spectra of urine samples. This is an important preprocessing tool when multivariate analysis is to be applied. A novel peak alignment method was developed and compared to the traditional bucketing approach and a conceptually different alignment method. Univariate, multivariate, linear and nonlinear data analyses were applied to qNMR data. In papers I–II, calibration models were created to examine the potential of qNMR for these applications. The data analysis in papers III–VI was mainly explorative. The potential of data fusion and data correlation was examined in order to increase the possibilities of analysing the highly complex samples from metabolic profiling (papers V–VI). Data from LC/MS analysis of the same samples were used with the 1H-NMR data in different ways. Correlation analyses between the 1H-NMR data and the drug metabolites identified from the LC/MS data were also performed. In this process, data fusion proved to be a valuable tool.

Advances in information technology produce large sets of data for decision makers. In both military and civilian efforts to achieve decision superiority, decision makers have to act agilely with proper, adequate and relevant information available. Information fusion is a process aimed to support decision makers’ situation awareness. This involves a process of combining data and information from disparate sources with prior information or knowledge to obtain an improved state estimate about an agent or other relevant phenomena. The important issue in decision making is not only assessing the current situation but also envisioning how a situation may evolve. In this work we focus on the prediction part of decision making called predictive situation awareness. We introduce new methodology where simulations and plan recognition are tools for achieving improved predictive situation awareness. Plan recognition is the term given to the process of inferring an agent’s intentions from a set of actions and is intended to support decision making. Beside its main task that is to support decision makers’ predictive situation awareness, plan recognition could also be used for coordination of actions and for developing computer-game agents that possess cognitive ability to recognize other agents’ behaviour. Successful plan recognition is heavily dependent on the data that is supplied. Therefore we introduce a bridge between plan recognition and sensor management where results of our plan recognition are reused to the control of, to give focus of attention to, the sensors that are expected to acquire the most important/relevant information. Our methodologies include knowledge representation, embedded stochastic simulations, microeconomics, imprecise knowledge and statistical inference issues. / QC 20100922

The mental workload for fighter pilots in modern air combat is extremely high. The pilot has to make fast dynamic decisions under high uncertainty and high time pressure. This is hard to perform in close encounters, but gets even harder when operating beyond visual range when the sensors of an aircraft become the pilot's eyes and ears. Although sensors provide good estimates for position and speed of an opponent, there is a big loss in the assessment of a situation. Important tactical events or situations can occur without the pilot noticing, which can change the outcome of a mission completely. This makes the development of an automated situation assessment system very important for future fighter aircraft. This Master Thesis investigates the possibilities to design and implement an automated situation assessment system in a fighter aircraft. A Fuzzy-Bayesian hybrid technique is used in order to cope with the stochastic environment and making the development of the tactical situations library as clear and simple as possible.