Informatik/Technik
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Item Active contours with spatially-variant definitions of energy terms based on local region descriptors(2008) Darolti, CristinaItem Adaptive service migration and transaction processing in wireless sensor networks(2011) Reinke, ChristophItem Adaptive und Mutual-Information-basierte Verfahren für eQTL-Studien(2011) Szymczak, SilkeItem Adaptivity and self-repair in robot self-assembly(2020) Divband Soorati, MohammadItem Advanced sensor fusion methods with applications to localization and navigation(2025-03-18) Fetzer, ToniWe use sensors to track how many steps we take during the day or how well we sleep. Sensor fusion methods are used to draw these conclusions. A particularly difficult application is indoor localization, i.e. finding a person’s position within a building. This is mainly due to the many degrees of freedom of human movement and the physical properties of sensors inside buildings. Suitable approaches for sensor fusion for the purpose of self-localization using a smartphone are the subject of this thesis. To best address the complexity of this problem, a non-linear and non-Gaussian distributed state space must be assumed. For the required position estimation, we therefore focus on the class of particle filters and build a novel generic filter framework on top of it. The special feature of this framework is the modular approach and the low requirements towards the sensor and movement models. In this work, we investigate models for Wi-Fi and Bluetooth RSSI measurements using radio propagation models, the relatively new standard Wi-Fi FTM, which is explicitly designed for localization purposes, the barometer to determine floor changes as accurately as possible, and activity recognition to find out what the pedestrian is doing, e.g., ascending stairs. The human motion is then modeled in a movement model using IMU data. Here we propose two approaches: a regular tessellated grid graph and an irregular tessellated navigation mesh. From these we formulate our proposal for an indoor localization system (ILS). However, some fundamental problems of the particle filter lead to critical errors. These can be a multi- modal density to be estimated, unbalanced sensor models or the so-called sample impoverish- ment. Compensation, or in the best case elimination, of these errors by advanced sensor fusion methods is the main contribution of this thesis. The most important approach in this context is our adaptation of an interacting multiple modal particle filter (IMMPF) to the requirements of indoor localization. This results in a completely new approach to the formulation of an ILS. Using quality metrics, it is possible to dynamically switch between arbitrarily formulated par- ticle filters running in parallel. Furthermore, we explicitly propose several approaches from the field of particle distribution optimization (PDO) to avoid the sample impoverishment problem. In particular, the support filter approach (SFA), which is also based on the IMMPF principle, leads to excellent position estimates even under the most difficult conditions, as extensive ex- periments show.Item Advances in algorithmic side-channel countermeasures for modern cryptography(2022) Seker, OkanItem Advances in compressed sensing for magnetic resonance imaging(2011) Doneva, MariyaItem Advancing ultrasound image guidance(2024) Wulff, DanielItem Item Algorithmen zur automatisierten Analyse von in vitro-Stammzellpopulationen(2013) Becker, TimItem Item Algorithmic learning of DNF formulas, finite automata, and distributions(2020) Lutter, MatthiasItem Algorithmics of identifying causal effects in graphical models(2021) van der Zander, BenitoItem Algorithms for Markov equivalence(2024) Wienöbst, MarcelItem Algorithms for minimum graph bisection and their performance on specific graph classes(2018) Schuster, Martin R.Item Ambient assisted living with dynamic interaction ensembles(2014) Altakrouri, BasharItem Analyse von Ultraschallbildern zur Schlaganfall- und Parkinson-Diagnose(2011) Kier, ChristianItem Analyse, Entwicklung und Erprobung von User Interfaces für die mobile Interaktion mit Online-Befragungen(2022) Nissen, Helge Eric