Global ETD Search

51	Tracking Under Countermeasures Using Infrared Imagery Modorato, Sara January 2022 (has links) Object tracking can be done in numerous ways, where the goal is to track a target through all frames in a sequence. The ground truth bounding box is used to initialize the object tracking algorithm. Object tracking can be carried out on infrared imagery suitable for military applications to execute tracking even without illumination. Objects, such as aircraft, can deploy countermeasures to impede tracking. The countermeasures most often mainly impact one wavelength band. Therefore, using two different wavelength bands for object tracking can counteract the impact of the countermeasures. The dataset was created from simulations. The countermeasures applied to the dataset are flares and Directional Infrared Countermeasures (DIRCMs). Different object tracking algorithms exist, and many are based on discriminative correlation filters (DCF). The thesis investigated the DCF-based trackers STRCF and ECO on the created dataset. The STRCF and the ECO trackers were analyzed using one and two wavelength bands. The following features were investigated for both trackers: grayscale, Histogram of Oriented Gradients (HOG), and pre-trained deep features. The results indicated that the STRCF and the ECO trackers using two wavelength bands instead of one improved performance on sequences with countermeasures. The use of HOG, deep features, or a combination of both improved the performance of the STRCF tracker using two wavelength bands. Likewise, the performance of the ECO tracker using two wavelength bands was improved by the use of deep features. However, the negative aspect of using two wavelength bands and introducing more features is that it resulted in a lower frame rate. computer vision infrared imagery tracking countermeasures discriminative correlation filters Efficient Convolution Operator tracker Visual Object Tracking flare DIRCM multiple wavelength bands features
52	Holographic imaging of cold atoms Turner, Lincoln David Unknown Date (has links) (PDF) This thesis presents a new optical imaging technique which measures the structure of objects without the use of lenses. Termed diffraction-contrast imaging (DCI), the method retrieves the object structure from a Fresnel diffraction pattern of the object, using a deconvolution algorithm. DCI is particularly adept at imaging highly transparent objects and this is demonstrated by retrieving the structure of an almost transparent cloud of laser-cooled atoms. Applied to transparent Bose-Einstein condensates, DCI should allow the non-destructive imaging of the condensate while requiring only the minimum possible apparatus of a light source and a detector. (For complete abstract open document)
53	Développement des méthodes génériques d'analyses multi-variées pour la surveillance de la qualité du produit / Development of multivariate analysis methods for the product quality prediction Melhem, Mariam 20 November 2017 (has links) L’industrie microélectronique est un domaine compétitif, confronté de manière permanente à plusieurs défis. Pour évaluer les étapes de fabrication, des tests de qualité sont appliqués. Ces tests étant discontinus, une défaillance des équipements peut causer une dégradation de la qualité du produit. Des alarmes peuvent être déclenchées pour indiquer des problèmes. D’autre part, on dispose d’une grande quantité de données des équipements obtenues à partir de capteurs. Une gestion des alarmes, une interpolation de mesures de qualité et une réduction de données équipements sont nécessaires. Il s’agit dans notre travail à développer des méthodes génériques d’analyse multi-variée permettant d’agréger toutes les informations disponibles sur les équipements pour prédire la qualité de produit en prenant en compte la qualité des différentes étapes de fabrication. En se basant sur le principe de reconnaissance de formes, nous avons proposé une approche pour prédire le nombre de produits restant à produire avant les pertes de performance liée aux spécifications clients en fonction des indices de santé des équipement. Notre approche permet aussi d'isoler les équipements responsables de dégradation. En plus, une méthodologie à base de régression régularisée est développée pour prédire la qualité du produit tout en prenant en compte les relations de corrélations et de dépendance existantes dans le processus. Un modèle pour la gestion des alarmes est construit où des indices de criticité et de similarité sont proposés. Les données alarmes sont ensuite utilisées pour prédire le rejet de produits. Une application sur des données industrielles provenant de STMicroelectronics est fournie. / The microelectronics industry is a highly competitive field, constantly confronted with several challenges. To evaluate the manufacturing steps, quality tests are applied during and at the end of production. As these tests are discontinuous, a defect or failure of the equipment can cause a deterioration in the product quality and a loss in the manufacturing Yield. Alarms are setting off to indicate problems, but periodic alarms can be triggered resulting in alarm flows. On the other hand, a large quantity of data of the equipment obtained from sensors is available. Alarm management, interpolation of quality measurements and reduction of correlated equipment data are required. We aim in our work to develop generic methods of multi-variate analysis allowing to aggregate all the available information (equipment health indicators, alarms) to predict the product quality taking into account the quality of the various manufacturing steps. Based on the pattern recognition principle, data of the degradation trajectory are compared with health indices for failing equipment. The objective is to predict the remaining number of products before loss of the performance related to customer specifications, and the isolation of equipment responsible for degradation. In addition, regression- ased methods are used to predict the product quality while taking into account the existing correlation and the dependency relationships in the process. A model for the alarm management is constructed where criticality and similarity indices are proposed. Then, alarm data are used to predict the product scrap. An application to industrial data from STMicroelectronics is provided. Analyse statistique multi-Variée Régression régularisée Analyse de similarité Gestion d’alarmes Indices de santé Prédiction de la qualité du produit Amélioration du rendement Industrie micro-Électronique. Data-Based diagnosis and prognosis Multivariate statistical analysis Regularized regression Similarity analysis Alarm management Health index Product quality prediction Yield improvement Microelectronic industry.
54	Primene polugrupa operatora u nekim klasama Košijevih početnih problema / Applications of Semigroups of Operators in Some Classes of Cauchy Problems Žigić Milica 22 December 2014 (has links) <p>Doktorska disertacija je posvećena primeni teorije polugrupa operatora na re&scaron;avanje dve klase Cauchy-jevih početnih problema. U prvom delu smo<br />ispitivali parabolične stohastičke parcijalne diferencijalne jednačine (SPDJ-ne), odredjene sa dva tipa operatora: linearnim zatvorenim operatorom koji<br />generi&scaron;e <em>C</em><sub>0</sub>−polugrupu i linearnim ograničenim operatorom kombinovanim<br />sa Wick-ovim proizvodom. Svi stohastički procesi su dati Wiener-Itô-ovom<br />haos ekspanzijom. Dokazali smo postojanje i jedinstvenost re&scaron;enja ove klase<br />SPDJ-na. Posebno, posmatrali smo i stacionarni slučaj kada je izvod po<br />vremenu jednak nuli. U drugom delu smo konstruisali kompleksne stepene<br /><em>C</em>-sektorijalnih operatora na sekvencijalno kompletnim lokalno konveksnim<br />prostorima. Kompleksne stepene operatora smo posmatrali kao integralne<br />generatore uniformno ograničenih analitičkih <em>C</em>-regularizovanih rezolventnih<br />familija, i upotrebili dobijene rezultate na izučavanje nepotpunih Cauchy-jevih problema vi&scaron;3eg ili necelog reda.</p> / <p>The doctoral dissertation is devoted to applications of the theory<br />of semigroups of operators on two classes of Cauchy problems. In the first<br />part, we studied parabolic stochastic partial differential equations (SPDEs),<br />driven by two types of operators: one linear closed operator generating a<br /><em>C</em><sub>0</sub>−semigroup and one linear bounded operator with Wick-type multipli-cation. All stochastic processes are considered in the setting of Wiener-Itô<br />chaos expansions. We proved existence and uniqueness of solutions for this<br />class of SPDEs. In particular, we also treated the stationary case when the<br />time-derivative is equal to zero. In the second part, we constructed com-plex powers of <em>C</em>−sectorial operators in the setting of sequentially complete<br />locally convex spaces. We considered these complex powers as the integral<br />generators of equicontinuous analytic <em>C</em>−regularized resolvent families, and<br />incorporated the obtained results in the study of incomplete higher or frac-tional order Cauchy problems.</p>
55	On choice models in the context of MDPs Mohammadpour, Sobhan 10 1900 (has links) Cette thèse se penche sur les modèles de choix, des distributions sur des ensembles d'alternatives. Les modèles de choix sur les processus décisionnels de Markov (MDP) peuvent décomposer de très grands espaces alternatifs en procédures étape par étape conçues pour non seulement combattre la malédiction de la dimensionnalité mais aussi pour mieux refléter la dynamique sous-jacente. La première partie est consacrée à l'estimation du temps de trajet dans le cadre de la modélisation du choix de chemin. Les modèles de choix de chemin sont des modèles de choix sur l'ensemble des chemins utilisés pour modéliser le flux de circulation. Intuitivement, le temps de trajet est l'une des caractéristiques les plus importantes lors du choix des chemins, mais les temps de trajet ne sont pas toujours connus. En revanche, le cadre classique suppose que ces deux étapes sont séquentielles, car les temps de trajet des arcs font partie de l'entrée du processus d'estimation du choix de chemin. Pourtant, les interdépendances complexes signifient que ce modèle de choix de chemin peut complémenter toute observation lors de l'estimation des temps de trajet. Nous construisons un modèle statistique pour l'estimation du temps de trajet et proposons de marginaliser les caractéristiques non observées. En utilisant ces idées, nous montrons que nous sommes capables d'apprendre des modèles de choix de chemin sans observer de chemins réels et à différentes granularités. La deuxième partie se concentre sur les échecs des MDP régularisés et comment la régularisation peut avoir des effets secondaires inattendus, tels que la divergence dans les chemins stochastiques les plus courts ou des fonctions de valeur déraisonnablement grandes. Les MDP régularisés ne sont rien d'autre qu'une application des modèles de choix aux MDP. Ils sont utilisés dans l'apprentissage par renforcement (RL) pour obtenir, entre autres choses, un modèle de choix sur les trajectoires possibles pour l'apprentissage par renforcement inverse, transférer des connaissances préalables au modèle, ou obtenir des politiques qui exploitent tous les objectifs dans l'environnement. Ces effets secondaires sont exacerbés dans les espaces d'action dépendants de l'état. Comme mesure d'atténuation, nous introduisons deux transformations potentielles, et nous évaluons leur performance sur un problème de conception de médicaments. / This thesis delves on choice models, distributions on sets of alternatives. Choice models on Markov decision processes (MDPs) can break down very large alternative spaces into step-by-step procedures designed to not only tackle the curse of dimensionality but also to reflect the underlying dynamics better. The first part is devoted to travel time estimation as part of path choice modeling. Path choice models are choice models on the set of paths used to model traffic flow. Intuitively, travel time is one of the more important features when choosing paths, yet travel times are not always known. In contrast, the classical setting assumes that these two steps are sequential, as arc travel times are part of the input of the path choice estimation process. Yet the intricate interdependences mean that that path choice model can complement any observation when estimating travel times. We build a statistical model for travel time estimation and propose marginalizing the unobserved features. Using these ideas, we show that we are able to learn path choice models without observing actual paths and at different granularity. The second part focuses on the failings of regularized MDPs and how regularization may have unexpected side effects, such as divergence in stochastic shortest paths or unreasonably large value functions. Regularized MDPs are nothing but an application of choice models to MDPs. They are used in reinforcement learning (RL) to get, among other things, a choice model on possible trajectories for inverse reinforcement learning, transfer prior knowledge to the model, or to get policies that exploit all goals in the environment. These side effects are exacerbated in state-dependent action spaces. As a mitigation, we introduce two potential transformations, and we benchmark their performance on a drug design problem. Estimation du temps de trajet Route choice modeling Path choice models Modèles de choix de chemin Modélisation du choix d’itinéraire Maximum entropy reinforcement learning Regularized Markov decision process Travel time estimation
56	Incorporating Scene Depth in Discriminative Correlation Filters for Visual Tracking Stynsberg, John January 2018 (has links) Visual tracking is a computer vision problem where the task is to follow a targetthrough a video sequence. Tracking has many important real-world applications in several fields such as autonomous vehicles and robot-vision. Since visual tracking does not assume any prior knowledge about the target, it faces different challenges such occlusion, appearance change, background clutter and scale change. In this thesis we try to improve the capabilities of tracking frameworks using discriminative correlation filters by incorporating scene depth information. We utilize scene depth information on three main levels. First, we use raw depth information to segment the target from its surroundings enabling occlusion detection and scale estimation. Second, we investigate different visual features calculated from depth data to decide which features are good at encoding geometric information available solely in depth data. Third, we investigate handling missing data in the depth maps using a modified version of the normalized convolution framework. Finally, we introduce a novel approach for parameter search using genetic algorithms to find the best hyperparameters for our tracking framework. Experiments show that depth data can be used to estimate scale changes and handle occlusions. In addition, visual features calculated from depth are more representative if they were combined with color features. It is also shown that utilizing normalized convolution improves the overall performance in some cases. Lastly, the usage of genetic algorithms for hyperparameter search leads to accuracy gains as well as some insights on the performance of different components within the framework. Tracking Visual Deep Learning Machine Learning CNN Convolutional Neural Network Unsupervised Learning Clustering Genetic Algorithms Features Visual featues Channel Coding RGBD Scene Depth Map Kinect Discriminative Correlation Filters SRDCF DCF Spatial Spatially Regularized Hyperparameter Search Occlusion Detection Handling Kalman Filters Normalized Convolution Bayesian Gaussian Mixture Scale Estimation Conjugate Gradient Linkoping Sweden Visuell Följning Särdrag Djupa Faltningsnätverk Maskininlärning Djup Inlärning Genetiska Algoritmer Klustring Djup RGBD Linköping Sverige

Page generated in 0.0614 seconds