Die dynamische Schätzung der Fahrzeugposition durch Sensordatenfusion ist eine der grundlegenden Aufgaben für moderne Verkehrsanwendungen wie zum Beispiel fahrerlose Transportsysteme oder Pre-Crash-Sicherheitssysteme. In dieser Arbeit wird ein Verfahren zur dezentralen kooperativen Fahrzeuglokalisierung vorgestellt, das auf einer allgemeinen Methode zur Fusion von Informationen mehrerer Teilnehmer beruht. Sowohl die lokale als auch die übertragene Schätzung wird durch Partikel dargestellt. Innerhalb einer Simulation wird gezeigt, dass sich die Positionsschätzung der einzelnen Teilnehmer im Netzwerk im Vergleich zu einer reinen GPS-basierten Lösung verbessert.

In large and complex systems, failures can have dramatic consequences, such as black-outs, pandemics or the loss of entire classes of an ecosystem. Nevertheless, it is a centuries-old intuition that by using networks to capture the core of the complexity of such systems, one might understand in which part of a system a phenomenon originates. I investigate this intuition using spectral methods to decouple the dynamics of complex systems near stationary states into independent dynamical modes. In this description, phenomena are tied to a specific part of a system through localized eigenvectors which have large amplitudes only on a few nodes of the system's network. Studying the occurrence of localized eigenvectors, I find that such localization occurs exactly for a few small network structures, and approximately for the dynamical modes associated with the most prominent failures in complex systems. My findings confirm that understanding the functioning of complex systems generally requires to treat them as complex entities, rather than collections of interwoven small parts. Exceptions to this are only few structures carrying exact localization, whose functioning is tied to the meso-scale, between the size of individual elements and the size of the global network. However, while understanding the functioning of a complex system is hampered by the necessary global analysis, the prominent failures, due to their localization, allow an understanding on a manageable local scale. Intriguingly, food webs might exploit this localization of failures to stabilize by causing the break-off of small problematic parts, whereas typical attempts to optimize technological systems for stability lead to delocalization and large-scale failures. Thus, this thesis provides insights into the interplay of complexity and localization, which is paramount to ascertain the functioning of the ever-growing networks on which we humans depend.:1 Introduction 2 Concepts and Tools 2.1 Networks 2.2 Food webs 2.3 Dynamics on networks 2.4 Steady state operating modes 2.5 Bifurcations affecting operating modes 2.6 Dynamical modes 2.7 Generalized models for food webs 3 Perturbation Impact 3.1 Impact of perturbations on food webs 3.2 Examples 3.3 Impact formulation with dynamical modes 3.4 Influence and sensitivity of species 3.5 Localized dynamical modes 3.6 Iterative parameter estimation 3.7 Most important parameters and species 3.8 Discussion 4 Exact Localization 4.1 Graph symmetries 4.2 Localized dynamics on symmetries 4.3 Exactly localized dynamics 4.4 Symmetry reduction in networks 4.5 Application to food webs 4.6 Localization on asymmetric structures 4.7 Nearly-exact localization 4.8 Other systems 4.9 Discussion 5 Approximate Localization 5.1 Spread of a dynamical mode 5.2 Examples for localized instabilities 5.3 Localization of extreme eigenvalues 5.4 Dependence on the system size 5.5 Localization in the model of R. May 5.6 Finding motifs that carry localization 5.7 (Self-)stabilization of food webs 5.8 Repairing localized instabilities 5.9 Discussion 6 Conclusions Acknowledgments Appendix A Parametrization of the Gatun Lake food web B The Master Stability Function approach C Approximate localization on larger structures Bibliography

Classical partial transport barriers govern both classical and quantum dynamics of generic Hamiltonian systems. Chaotic eigenstates of quantum systems are known to localize on either side of a partial barrier if the flux connecting the two sides is not resolved by means of Heisenberg's uncertainty. Surprisingly, in open systems, in which orbits can escape, chaotic resonance states exhibit such a localization even if the flux across the partial barrier is quantum mechanically resolved. We explain this using the concept of conditionally invariant measures by introducing a new quantum mechanically relevant class of such fractal measures. We numerically find quantum-to-classical correspondence for localization transitions depending on the openness of the system and on the decay rate of resonance states. Moreover, we show that the number of long-lived chaotic resonance states that localize on one particular side of the partial barrier is described by an individual fractal Weyl law. For a generic phase space, this implies a hierarchy of fractal Weyl laws, one for each region of the hierarchical decomposition of phase space.

The localized/delocalized duality of 5f electrons plays an important role in understanding the complex physics of actinides. Band-structure calculations based on the ad hoc assumption that 5f electrons are simultaneously localized and delocalized explained the observed dHvA experiments very well. This ad hoc assumption also gives the correct equilibrium volume for delta-Pu. Experimentally, the duality of 5f electrons is observed by inelastic neutron scattering experiments, or by soft X-ray angle-resolved photoelectron spectroscopy. It is worth recalling that the origin of partial localization in the 3d and 5f systems is quite different. In compounds with 3d electrons, the large crystalline electric field set up by the surrounding environment of transition metal ions plays a major role. On the other hand, in 5f systems, the Hund's rule correlations play the key role whilst the crystalline electric field is less important. In this thesis we have studied the effect of intra-atomic correlations on anisotropies in hopping matrix elements of different 5f orbitals. For that purpose, we used the effective model that includes on-site interactions that are responsible for Hund's rules and effective hopping terms that result from the hybridization of different 5f orbitals with the environment. Two different approximations, namely, rotationally invariant slave-boson mean-field (RISBMF) and infinite time-evolving block decimation (iTEBD), have been used to investigate the ground-state properties of the Hamiltonian. We have demonstrated that Hund's rule correlations enhance strongly anisotropies in hopping matrix elements. For a certain range of 5f bandwidth parameters this effect may result in a complete suppression of hopping processes for some of 5f orbitals, i.e., the system is in a partially localized phase. Within the RISBMF method, we calculated the ground-state properties and the phase diagram of the system. The suppression of hopping processes in some of 5f orbitals due to Hund's rule correlations can be seen through orbital-dependent quasiparticle weights. In a mean-field theory, a quasiparticle weight of zero for an orbital means a complete suppression of hopping processes in this orbital. Thus, quasiparticle weights and occupation numbers were used to classify partially localized phases. In the calculated phase diagram we obtain four partially localized phases that can be separated into two different sets. In the first set electrons in two orbitals are localized. In the second, electrons in one orbital are localized. The difference between the two sets is not simply the number of localized orbitals but the mechanism for the partial localization. For the first set, the Hund's rule mechanism applies: only those 5f electrons that enable the remaining ones to form a Hund's rule state will delocalize. This mechanism requires to have at least two localized orbitals, therefore it is definitely not applicable to those phases with only one localized orbital. For the second set, a situation similar to a single-band Mott-Hubbard transition applies. The direct on-site Coulomb interaction between jz and -jz electrons plays the key role for understanding the partial localization transition. In order to assess the validity of the RISBMF results we have used the iTEBD method to calculate the ground-state properties of a 1D system. Qualitatively, the two approaches agree with each other. However, we found an area where the RISBMF yields an artificial ground-state. Note that the mean-field method is worst for a 1D system. Therefore one shoud not judge from it the quality of the RISBMF method for the more general case.

The navigation tasks in advanced home robotic applications incorporating reliable revisiting strategies are dependent on very low cost but nevertheless rather accurate localization systems. In this paper a localization system based on the principle of trilateration is described. The proposed system uses only a single small base station, but achieves accuracies comparable to systems using spread beacons and it performs sufficiently for map building. Thus it is a standalone system and needs no odometry or other auxiliary sensors. Furthermore a new approach for the problem of the reliably detection of areas without direct line of sight is presented. The described system is very low cost and it is designed for use in indoor service robotics. The paper gives an overview on the system concept and special design solutions and proves the possible performances with experimental results.

Der stetig anwachsende Bedarf und die innovative Weiterentwicklung im Bereich der Großdieselmotoren als Antrieb für Schiffe und Generatoranlagen erfordert ebenfalls die Weiterentwicklung der Abgasturbolader. Hohe Leistungsfähigkeit und Wirtschaftlichkeit ist nur durch moderne Fertigungsverfahren und einer optimalen Ausnutzung der eingesetzten, hochwertigen Werkstoffe zu erreichen. Dies gilt insbesondere für die integralen Radialturbinenräder in Abgasturboladern, die aufgrund der hohen Betriebsbelastungen einen zentralen Punkt bei der Auslegung darstellen. Lebensdauerbegrenzend ist die hochzyklische Ermüdung aufgrund Resonanzschwingungen an der Beschaufelung der Turbinenräder. Die vorliegende Arbeit soll die Auslegungsmethodik zur Berechnung und Beurteilung der zu erwartenden Schwingbeanspruchungen der Turbinenräder im Hinblick der realen Geometrie verbessern. Dazu wird ein einfaches Berechnungsmodell zur Identifizierung der kritischen Schaufelmoden und Bestimmung der Schwingbeanspruchungen im integralen Turbinenrad erarbeitet. Das Modell wird auf vorhandene Turbinenräder angewendet und aus den Ergebnissen werden Hinweise für eine systematische Beurteilung der Schaufelmoden, Knotendurchmesser und Schaufelgestaltung bezüglich der kritischen Schwingbeanspruchungen angegeben. Desweiteren wird der Einfluss der Verstimmung (engl. Mistuning) des Schwingverhaltens realer, integraler Turbinenräder ausführlich im Hinblick auf die Schwingbeanspruchungen untersucht. Die wesentlichen Ursachen für die Verstimmung sind die innerhalb der Fertigungstoleranzen auftretenden Geometrieabweichungen der Schaufeln. Dabei wird ein Überblick über die typischen Geometrie- und Frequenzabweichungen Radialturbinen gegeben und Auswirkungen auf das Schwingverhalten des Rades wie Lokalisierung der Schwingformen und Amplitudenüberhöhungen ermittelt und in einen systematischen Zusammenhang mit den geometrischen Ursachen, der Komplexität der Schaufelschwingformen und Knotendurchmesser gestellt. Es zeigt sich, dass unter gewissen Voraussetzungen bei Radialturbinen KD0 und KD1 Schwingformen weniger sensibel auf die Verstimmung reagieren. Hieraus können Hinweise für die Verbesserung des Auslegungsprozess abgeleitet werden. Die Kenntnis über das reale Schwingverhalten verstimmter Turbinenräder ermöglicht die korrekte Auswahl geeigneter Schaufeln zur Applikation von Dehnmessstreifen, wodurch eine sichere Beurteilung der Betriebsbeanspruchungen erst möglich wird.

Mathematics learning seems to be a demanding and time-consuming task for many learners. Information and communication technology (ICT) is an attractive tool of learning for students at any level and it can provide an effective atmosphere for understanding mathematics. The question is how to combine mathematics teaching contents, approaches, curricula, and syllabus with new media. The key issue in European educational policy (and other countries as well) is exchange and sharing digital learning resources (learning objects) among countries. In order to accumulate the practice of various countries and use the best digital resources created by different countries, it is necessary to localize learning objects (LO). The paper deals with some problems connected with localization of LO, developed for mathematics education, and presents some solution. Software localization is mainly referred to as language translation (e.g., translation of user interface texts and help documents). However, there are many other important elements depending on the country and people who will use the localized software. In this paper, the main attention is paid to localization of learning objects used for teaching and learning mathematics.

Estimating depth and detection of object instances in 3D space is fundamental in autonomous navigation, localization, and mapping, robotic object manipulation, and augmented reality. RGB-D images and LiDAR point clouds are the most illustrative formats of depth information. However, depth sensors offer many shortcomings, such as low effective spatial resolutions and capturing of a scene from a single perspective. The thesis focuses on reproducing denser and comprehensive 3D scene structure for given monocular RGB images using depth and 3D object detection. The first contribution of this thesis is the pipeline for the depth estimation based on an unsupervised learning framework. This thesis proposes two architectures to analyze structure from motion and 3D geometric constraint methods. The proposed architectures trained and evaluated using only RGB images and no ground truth depth data. The architecture proposed in this thesis achieved better results than the state-of-the-art methods. The second contribution of this thesis is the application of the estimated depth map, which includes two algorithms: point cloud generation and collision avoidance. The predicted depth map and RGB image are used to generate the point cloud data using the proposed point cloud algorithm. The collision avoidance algorithm predicts the possibility of collision and provides the collision warning message based on decoding the color in the estimated depth map. This algorithm design is adaptable to different color map with slight changes and perceives collision information in the sequence of frames. Our third contribution is a two-stage pipeline to detect the 3D objects from a monocular image. The first stage pipeline used to detect the 2D objects and crop the patch of the image and the same provided as the input to the second stage. In the second stage, the 3D regression network train to estimate the 3D bounding boxes to the target objects. There are two architectures proposed for this 3D regression network model. This approach achieves better average precision than state-of-theart for truncation of 15% or fully visible objects and lowers but comparable results for truncation more than 30% or partly/fully occluded objects.

Das Erkennen und die Lokalisierung kritischer Risse ist ein wesentlicher Schlüssel für eine sichere und nachhaltige Bauwerksnutzung. In diesem Beitrag wird ein neuartiges, kostengünstiges Sensorsystem vorgestellt, das zur Echtzeit-Zustandsüberwachung von sowohl neuen als auch Bestandsbauwerken geeignet ist. Erste Ergebnisse zeigen, dass das System prinzipiell in der Lage ist, die Gesamtdehnung eines Bauteiles zu erfassen sowie auftretende Risse zu erkennen und zu lokalisieren. Die Erkennungsgenauigkeit hängt dabei von technischen Parametern ab, wodurch das System auf verschiedene Einsatzszenarien angepasst werden kann.

This thesis offers a probabilistic solution to robust estimation using a novel adaptive estimator. Reliable state estimation is a mandatory prerequisite for autonomous systems interacting with the real world. The presence of outliers challenges the Gaussian assumption of numerous estimation algorithms, resulting in a potentially skewed estimate that compromises reliability. Many approaches attempt to mitigate erroneous measurements by using a robust loss function – which often comes with a trade-off between robustness and numerical stability. The proposed approach is purely probabilistic and enables adaptive large-scale estimation with non-Gaussian error models. The introduced Adaptive Mixture algorithm combines a nonlinear least squares backend with Gaussian mixtures as the measurement error model. Factor graphs as graphical representations allow an efficient and flexible application to real-world problems, such as simultaneous localization and mapping or satellite navigation. The proposed algorithms are constructed using an approximate expectation-maximization approach, which justifies their design probabilistically. This expectation-maximization is further generalized to enable adaptive estimation with arbitrary probabilistic models. Evaluating the proposed Adaptive Mixture algorithm in simulated and real-world scenarios demonstrates its versatility and robustness. A synthetic range-based localization shows that it provides reliable estimation results, even under extreme outlier ratios. Real-world satellite navigation experiments prove its robustness in harsh urban environments. The evaluation on indoor simultaneous localization and mapping datasets extends these results to typical robotic use cases. The proposed adaptive estimator provides robust and reliable estimation under various instances of non-Gaussian measurement errors.