Biological processes in living organisms consist of a vast number of different molecular networks and interactions, which are complex and often hidden from our understanding. This work is focused on recovery of such details for two quite distant examples: acclimation to extreme freezing tolerance in Siberian spruce (Picea obovata) and detection of proteins associated with prostate cancer. The first biological system in the study, upon P. obovata, is interesting by this species ability to adapt and sustain extremely low temperatures, such as -60⁰C or below. Despite decades of investigations, the essential features and mechanisms of the amazing ability of this species still remains unclear. To enhance knowledge about extreme freezing tolerance, the metabolome and proteome of P. obovata’s needles were collected during the tree’s acclimation period, ranging from mid August to January, and have been analyzed. The second system within this study is the plasma proteome analysis of high risk prostate cancer (PCa) patients, with and without bone metastases. PCa is one of the most common cancers among Swedish men, which can abruptly develop into an aggressive, lethal disease. The diagnostic tools, including PSA-tests, are insufficient in predicting the disease’s aggressiveness and novel prognostic markers are urgently required. Both biological systems have been analyzed following similar steps: by two-dimensional difference gel electrophoresis (2D-DIGE) techniques, followed by protein identification using mass spectrometry (MS) analysis and multivariate methods. Data processing has been utilized for searching for proteins that serve as unique indicators for characterizing the status of the systems. In addition, the gas chromatography-mass spectrometry (GC-MS) study of the metabolic content of P.obovata’s needles, from the extended observation period, has been performed. The studies of both systems, combined with thorough statistical analysis of experimental outcomes, have resulted in novel insights and features for both P. obovata and prostate cancer. In particular, it has been shown that dehydrins, Hsp70s, AAA+ ATPases, lipocalin and several proteins involved in cellular metabolism etc., can be uniquely associated with acclimation to extreme freezing in conifers. Metabolomic analysis of P. obovata needles has revealed systematic metabolic changes in carbohydrate and lipid metabolism. Substantial increase of raffinose, accumulation of desaturated fatty acids, sugar acids, sugar alcohols, amino acids and polyamines that may act as compatible solutes or cryoprotectants have all been observed during the acclimation process. Relevant proteins for prostate cancer progression and aggressiveness have been identified in the plasma proteome study, for patients with and without bone metastasis. Proteins associated with lipid transport, coagulation, inflammation and immune response have been found among them.

Recently, autonomous vehicles have received worldwide attentions from academic research, automotive industry and the general public. In order to achieve a higher level of automation, one of the most fundamental requirements of autonomous vehicles is the capability to respond to internal and external changes in a safe, timely and appropriate manner. Situational awareness and decision making are two crucial enabling technologies for safe operation of autonomous vehicles. This thesis presents a solution for improving the automation level of autonomous vehicles in both situational awareness and decision making aspects by utilising additional domain knowledge such as constraints and influence on a moving object caused by environment and interaction between different moving objects. This includes two specific sub-systems, model based target tracking in environmental perception module and motion planning in path planning module. In the first part, a rigorous Bayesian framework is developed for pooling road constraint information and sensor measurement data of a ground vehicle to provide better situational awareness. Consequently, a new multiple targets tracking (MTT) strategy is proposed for solving target tracking problems with nonlinear dynamic systems and additional state constraints. Besides road constraint information, a vehicle movement is generally affected by its surrounding environment known as interaction information. A novel dynamic modelling approach is then proposed by considering the interaction information as virtual force which is constructed by involving the target state, desired dynamics and interaction information. The proposed modelling approach is then accommodated in the proposed MTT strategy for incorporating different types of domain knowledge in a comprehensive manner. In the second part, a new path planning strategy for autonomous vehicles operating in partially known dynamic environment is suggested. The proposed MTT technique is utilized to provide accurate on-board tracking information with associated level of uncertainty. Based on the tracking information, a path planning strategy is developed to generate collision free paths by not only predicting the future states of the moving objects but also taking into account the propagation of the associated estimation uncertainty within a given horizon. To cope with a dynamic and uncertain road environment, the strategy is implemented in a receding horizon fashion.

An autonomous vehicle is a complex system that requires a good perception of the surrounding environment to operate safely. One part of that is multiple object tracking, which is an essential component in camera-based perception whose responsibility is to estimate object motion from a sequence of images. This requires an association problem to be solved where newly estimated object positions are mapped to previously predicted trajectories, for which different solution strategies exist.  In this work, a multiple hypothesis tracking algorithm is implemented. The purpose is to demonstrate that measurement associations are improved compared to less compute-intensive alternatives. It was shown that the implemented algorithm performed 13 percent better than an intersection over union tracker when evaluated using a standard evaluation metric. Furthermore, this work also investigates the usage of abstraction layers to accelerate time-critical parallel operations on the GPU. It was found that the execution time of the tracking algorithm could be reduced by 42 percent by replacing four functions with implementations written in the purely functional array language Futhark. Finally, it was shown that a GPU code abstraction layer can reduce the knowledge barrier required to write efficient CUDA kernels.

Indoor localization remains an open problem in geolocation research, and once this is solved the localization enables counting and tracking of building occupants. This information is vital in an emergency, enables occupancy-optimized heating or cooling, and assists smart buildings in tailoring services for occupants. Unfortunately, two prevalent technologies---GPS and cellular-based positioning---perform poorly indoors due to attenuation and multipath from the building. To address this issue, the research community devised many alternatives for indoor localization (e.g., beacons, RFID tags, Wi-Fi fingerprinting, and UWB to cite just a few examples). A drawback with most is the requirement for those being located to carry a properly-configured device at all times. An alternative based on computer vision techniques poses significant privacy concerns due to cameras recording building occupants. By contrast, ILoViT research makes novel use of accelerometers already present in some buildings. These sensors were originally intended to monitor structural health or to study structural dynamics. The key idea is that when a person's footstep-generated floor vibrations can be detected and located then it becomes possible to locate persons moving within a building. Vibration propagation in buildings has complexities not encountered by acoustic or radio wave propagation in air; thus, conventional localization algorithms are inadequate. ILoVIT algorithms account for these conditions and have been demonstrated in a public building to provide sub-meter accuracy. Localization provides the foundation for counting and tracking, but providing these additional capabilities confronts new challenges. In particular, how does one determine the correct association of footsteps to the person making them? The ILoViT research created two methods for solving the data association problem. One method only provides occupancy counting but has modest, polynomial time complexity. The other method draws inspiration from prior work in the radar community on the multi-target tracking problem, specifically drawing from the multiple hypothesis tracking strategy. This dissertation research makes new enhancements to this tracking strategy to account for human gait and characteristics of footstep-derived multilateration. The Virginia Polytechnic Institute and State University's College of Engineering recognized this dissertation research with the Paul E. Torgersen Graduate Student Research Excellence Award. / Ph. D. / Indoor localization remains an open problem in geolocation research, and once this is solved the localization enables counting and tracking of building occupants. This information is vital in an emergency, enables occupancy-optimized heating or cooling and assists smart buildings in tailoring services for occupants. Unfortunately, two prevalent technologies—GPS and cellular-based positioning—are ill-suited here due to the way a building’s weakens and distorts wireless signals. To address this issue the research community devised many alternatives for indoor localization. A drawback with most is the requirement for those being located to carry a properly-configured device at all times. An alternative based on computer vision techniques poses significant privacy concerns due to cameras recording building occupants. By contrast, ILoViT research makes novel use of a mature sensor technology already present in some buildings. These sensors were originally intended to monitor structural health or to study structural dynamics. The key idea behind this unconventional role for building sensors is that when a person’s footstep-generated floor vibrations can be detected and located then it is possible to locate persons moving within a building. Vibration propagation in buildings has complexities not encountered by acoustic or radio wave propagation in air; thus, conventional localization algorithms designed for those applications are inadequate. ILoVIT algorithms account for these conditions and have been demonstrated in a public building to provide sub-meter accuracy. Localization provides the foundation for counting and tracking, but providing these additional capabilities confronts new challenges. In particular, how does one determine the correct association of footsteps to the person making them? The ILoViT research created two methods for solving the data association problem. One method only provides area occupancy counting but has modest complexity. The other method draws inspiration from prior work in the radar community on the multi-target tracking problem, and the dissertation research makes new enhancements to account for human gait and footstep-based localization. The Virginia Polytechnic Institute and State University’s College of Engineering recognized this dissertation research with the Paul E. Torgersen Graduate Student Research Excellence Award.

No description available.

Les débris spatiaux sont devenus une menace considérable pour la viabilité des satellites opérationnels en orbite basse. Afin de pouvoir éviter des collisions accidentelles, des systèmes de surveillance de l'espace existent mais sont limités en performances de détection pour les objets de petite taille (diamètre inférieur à 10cm), ce qui pousse à l'étude de nouvelles solutions. Cette thèse a pour objectif d'appuyer la faisabilité d'un système radar au sol utilisant un champ de veille étroit pour le catalogage de petits débris en orbite basse. Un tel système fournirait en effet des observations dites " isolées ", c'est-à-dire qu'une orbite n'est pas immédiatement déductible de chacune d'entre elles. Le grand nombre combinaisons nécessaires est alors prohibitif en termes de temps de calcul pour la résolution de ce problème de pistage. Nous proposons dans ces travaux une nouvelle méthode pour initialiser les pistes, c'est-à-dire associer des observations isolées avec une faible ambiguïté et en déduire des orbites précises. Les pistes ainsi obtenues sont combinées et filtrées grâce à un algorithme de pistage multicible que nous avons adapté aux particularités du problème. Avec un taux de couverture de plus de 80% obtenu en temps réel sur 3 jours pour des scénarios de 500 à 800 objets en plus d'un fort taux de fausses alarmes, les performances de la méthode proposée tendent à prouver la faisabilité du système envisagé. Afin d'extrapoler les résultats obtenus à de plus fortes densités d'observations, nous proposons un modèle de complexité combinatoire calibré sur les performances de l'algorithme aux faibles densités. L'apport d'un second capteur identique est également étudié et met en évidence un point de compromis entre réactivité et complexité combinatoire, ce qui offre un degré de liberté supplémentaire dans la conception d'un tel système.