Informatik/Technik

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    A Fourier-analytical approach for field-free-point magnetic particle imaging
    (2025) Maaß, Marco
    Magnetic particle imaging is a tracer-based medical imaging technique that measures the spatial distribution of superparamagnetic nanoparticles. Alternating magnetic fields with different excitation sequences are used to measure the nanoparticle distribution in a scanner. Usually, the simplified Langevin model of paramagnetism is used as a first approximation for the complicated nonlinear magnetization behavior of nanoparticles. Although the modified Langevin model of paramagnetism can provide suitable image reconstructions for one-dimensional excitation, the situation is more complicated for higher-dimensional excitation, as several aspects cannot be fully explained by the Langevin model. A well-known example is the spatial similarity of the frequency components of the system function with tensor products of Chebyshev polynomials. This was observed for a higher-dimensional excitation of the Lissajous trajectory type and was unproven for almost ten years. With the aim of explaining such observations mathematically, this thesis makes an important contribution to the mathematical foundations of magnetic particle imaging. To this end, the spatio-temporal system function based on the Langevin model is transformed into the frequency domain using various concepts of Fourier analysis. The scientific contribution of the newly developed mathematical framework is manifold. Firstly, the developed model is able to separate the scanner-dependent excitation from the particle magnetization model, allowing better utilization of the imaging operator so that faster reconstruction methods could be developed. Secondly, it is now easier to investigate both the effect of the magnetization model and that of the excitation sequence in the imaging model separately. Thus, an extended equilibrium magnetization model is introduced in this thesis and a series representation is developed for it. Furthermore, the exact relationship between the frequency components of the system function and the tensor products of Chebyshev polynomials is shown for excitations of the Lissajous trajectory type. Finally, using the developed mathematical framework, the frequency representations of various excitation sequences known from the literature are calculated, which further increases the applicability of the model for magnetic particle imaging.
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    Action regulation in energy-efficient driving
    (2025) Moll, Vivien Esther
    Battery electric vehicles (BEVs) offer substantial potential for reducing emissions but introduce cognitive and behavioural challenges for energy-efficient driving. In contrast to internal combustion engine vehicles (ICEVs), energy flow in BEVs is less tangible, and relevant consumption patterns are more complex to perceive, predict, and interpret. Current ecodriving research often lacks cognitive grounding, a focus on the specific challenges in BEVs, and a profound analysis beyond performance measures. This dissertation addresses the need for user-centred, cognitively aligned feedback by examining how different feedback approaches affect drivers’ perception, judgements, behaviour, knowledge, and perceived support of action regulation and the mental model of ecodriving. The theoretical foundation integrates adaptive control and action regulation models, cognitive information processing, and the role of mental models and perceived capability in goal-directed behaviour. It posits that energy-efficient driving with BEVs requires continuous situational adaptation and knowledge-based reasoning. Four empirical studies were conducted using experimental designs combined with qualitative and quantitative methods across diverse settings, including an online experiment, driving simulations, and real-world driving. Each study assessed both subjective and objective indicators of action regulation and knowledge. Study 1 (N = 55, online experiment) laid the conceptual foundation by exploring how drivers interpret typical consumption feedback derived from simplified acceleration dynamics. Rooted in bounded rationality, results revealed a systematic overestimation of energy use, particularly for high and brief maximum consumption values. There was no significant correlation between the correct energy efficiency ranking and the ranking derived from participants’ estimations. The study also identified interindividual differences in heuristic information processing, showing that both stimulus properties and cognitive predispositions shape perception. Study 2 (N = 63, driving simulator study) focused on knowledge gaps and their behavioural implications. It contrasted three feedback approaches: a baseline without support, a consumption trace display, and a recommendation system indicating optimal speed. Drivers frequently relied on incomplete or inaccurate conceptions of energy efficiency. While those using the recommendation system felt less uncertain, this confidence did not translate into better performance or more accurate knowledge. However, their tendency to verbalise more vehicle- and environment-related information suggests a more active reasoning process regarding energy-efficient driving. Study 3 (N = 50, field study) built on these findings and introduced a comprehension-based approach with pre-drive tip lists. When behavioural strategies were paired with technical reasoning, drivers reported higher perceived knowledge, stronger support for action regulation and the mental model, and better driving performance. This highlights the potential of explanation-based feedback to improve effectiveness, knowledge, and user experience. Study 4 (N = 112, driving simulator study) extended this approach into real-time driving by integrating elaborated auditory ecodriving tips into a recommendation system. This combined approach significantly improved driving performance and strengthened perceived mental model support, although cognitive load, information acquisition, and subjective information processing awareness were negatively influenced. The dissertation offers novel instruments and methods to evaluate ecodriving feedback. Key contributions include a new experimental paradigm for assessing dynamic magnitude perception, and two new constructs: perceived support of action regulation and perceived support of the mental model, enabling a finer-grained evaluation of action regulation quality beyond conventional usability or satisfaction metrics. Furthermore, existing items for measuring perceived ecodriving knowledge were revised based on theoretical considerations. Finally, an AI-assisted method was employed to systematically analyse verbalised driving strategies and their technical explanations, demonstrating scalable content analysis. Theoretically, the dissertation integrates psychological frameworks with an emphasis on mental models and information processing, provides a systematic literature review, and links various feedback approaches to cognitive processing and behavioural regulation. Moreover, it extends established cognitive biases by identifying a novel bias specific to dynamic data visualisation. Empirically, it demonstrates that comprehension-oriented feedback can improve energy-efficient behaviour, deepen understanding, and enhance perceived support, especially when it explains behavioural strategies and clarifies causal relationships. The practical implications are synthesised into design guidelines for future feedback systems in BEVs and beyond. The innovations in this dissertation extend beyond the context of BEVs. Action regulation in complex and dynamic systems—such as aviation, industrial control, or AI-assisted decision-making, especially in light of the growing role of generative, speech-based AI—can benefit from these findings. When users must form accurate mental models or interpret raw data in real-time, feedback should explain mechanisms and facilitate information analysis rather than merely presenting outcomes. This dissertation lays the groundwork for future research on cognitively aligned feedback systems that foster effective action regulation, adequate mental models, and user experience.
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    Adaptivity and self-repair in robot self-assembly
    (2020) Divband Soorati, Mohammad
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    Advanced sensor fusion methods with applications to localization and navigation
    (2025-03-18) Fetzer, Toni
    We 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.
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    Advancing ultrasound image guidance
    (2024) Wulff, Daniel
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    Algorithmics of identifying causal effects in graphical models
    (2021) van der Zander, Benito
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    Algorithms for Markov equivalence
    (2024) Wienöbst, Marcel
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    Altersgerechte Technikentwicklung
    (2025) Volkmann, Torben
    Die allgegenwärtige und für jeden verständliche Interaktion mit digitalen Technologien stellt eine der zentralen Herausforderungen für die Mensch-Computer-Interaktion-Forschung dar. Dabei wird die Teilhabe aller Gesellschaftsgruppen zunehmend wichtiger, um von den Vorteilen der Digitalisierung profitieren zu können. Insbesondere ältere Erwachsene stehen oft vor Barrieren im Umgang mit digitalen Technologien, weshalb der Erwerb digitaler Kompetenzen und die Förderung lebenslangen Lernens entscheidend für ihre Teilhabe sind. Dies erfordert nicht nur technische Lösungen, sondern auch einen gesellschaftlichen Wandel, um die digitale Spaltung zu verringern und allen Bevölkerungsgruppen, insbesondere in einer alternden Gesellschaft, den Zugang zu digitalen Technologien zu erleichtern. Diese Arbeit zielt darauf ab, sowohl die digitale Teilhabe älterer Erwachsener als auch demokratische Prinzipien wie Inklusion und Gleichberechtigung im Entwicklungsprozess zu stärken. Indem ältere Erwachsene aktiv in die Entwicklung digitaler Technologien einbezogen werden, sollen ihre spezifischen Bedürfnisse und Präferenzen in die Gestaltung der Lösungen einfließen. Der partizipative Ansatz verringert die digitale Spaltung und fördert eine inklusive Gesellschaft, in der alle Bevölkerungsgruppen – unabhängig von Alter oder digitaler Vorerfahrung – gleichermaßen von der fortschreitenden Digitalisierung profitieren können. Ein konkretes Beispiel für die Anwendung dieses partizipativen Ansatzes ist das Historytelling-System, das es älteren Erwachsenen ermöglicht, ihre Lebensgeschichten digital festzuhalten und zu teilen. Um die Forschungsfrage zu beantworten, werden im Folgenden vier zentrale Ergebnisse präsentiert, die den Entwicklungsprozess und die Gestaltungsprinzipien des Systems darlegen. Erstens wird ein erweitertes Modell zur Technologieakzeptanz speziell für ältere Erwachsene präsentiert. Zweitens wird die Entwicklung von Gestaltungsrichtlinien vorgestellt, die altersbedingte Veränderungen berücksichtigen und im Historytelling-System Anwendung finden. Drittens wird ein agiler, partizipativer Technikentwicklungsprozess beschrieben, der die Entwicklung des Historytelling-Systems unterstützt. Viertens wird ein Reflexionsframework entwickelt, das die Akteure, Methoden und Ziele partizipativer Technikentwicklungsprozesse systematisch einordnet. Darauf aufbauend wurde ein Reflexionswerkzeug erstellt, mit dem die Methodendurchführungen der Historytelling-Systementwicklung eingeordnet wurden. Damit leistet diese Arbeit insgesamt einen wichtigen Beitrag zur Gestaltung inklusiver digitaler Technologien und bietet einen Ansatz, der die Teilhabe älterer Erwachsener fördert und gleichzeitig zur digitalen Inklusion in einer alternden Gesellschaft beiträgt.
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    Ambient assisted living with dynamic interaction ensembles
    (2014) Altakrouri, Bashar
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    Ambient Serious Games
    (2025) Brandl, Lea Christine

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