School of Computer Science and Engineering
This thesis investigates how uniaxial tensile strain affects the optical and molecular properties of polymer thin films used in flexible organic electronics. It focuses on poly(ethylene terephthalate) (PET) as a flexible substrate and poly(3-hexylthiophene-2,5-diyl) (P3HT) as a semiconducting polymer. The materials are examined as single layers and in PET/P3HT and PET/PEDOT:PSS/P3HT stacks at room temperature.
Strain is applied reproducibly using a custom-built stretching setup. Ultraviolet–visible spectroscopy is used to track changes in optical response and band gap, while Raman spectroscopy reveals chain reorientation, conformational changes, and defect formation during and after deformation.
For PET, tensile strain causes progressive optical degradation, including increased absorption across the ultraviolet and visible regions. Above ≈5% strain, the changes become irreversible and are accompanied by evidence of permanent structural modification. This identifies two regimes: elastic behavior at low strain and irreversible deformation at higher strain.
For P3HT, the optical band gap remains stable up to 7% strain, regardless of annealing temperature or stack configuration. At 10% strain, a reproducible increase of about 4–5 meV appears, indicating onset of measurable electronic perturbation.
Raman results show that P3HT strain response depends strongly on thermal history. Unannealed films mainly deform through reversible chain alignment. Films annealed at 50 °C show a mixed response with greater microstructural heterogeneity, while films annealed at 75 °C exhibit more constrained deformation and stronger defect accumulation. A PEDOT:PSS interlayer improves strain transfer, reduces strain localization, and enhances mechanical tolerance, especially in highly ordered films.
Overall, the work defines experimentally grounded strain limits for flexible devices and clarifies how processing history and stack architecture govern mechanical and optoelectronic behavior.
In intelligent and autonomous systems, users must often share private data with external platforms to enable advanced functionalities. A key challenge in this process arises when the data to be protected is not a single, precise value, but is instead inherently uncertain or represents a range of possible values. This thesis addresses the privacy of such uncertain data, which is formally represented and processed using set-based methods. We review existing privacy-preserving techniques and introduce new mechanisms to safeguard this data during processing on untrusted platforms, exploring both cryptographic and non-cryptographic approaches.
Specifically, the thesis presents novel privacy-preserving mechanisms for set-based data processing, categorized by the data type within the sets.
For sets of real-valued data, we introduce a differential privacy mechanism for set-based estimation in linear and non-linear dynamical systems. This approach protects the sensitive information contained within sets, such as zonotopes that model system uncertainties, while minimizing the loss of utility for the estimation process.
For sets of discrete, binary data, we propose a mechanism that uses Fast Fully Homomorphic Encryption to ensure privacy. This method allows for the secure processing of sets represented as logical zonotopes in untrusted environments, maintaining computational practicality.
The evaluations demonstrate the effectiveness and practicality of the proposed mechanisms across various applications in autonomous and intelligent systems.
Underwater robots need reliable perception for navigation, mapping, diver interaction, and manipulation, yet vision is degraded by wavelength-dependent attenuation, scattering, and variable water optics. These effects reduce contrast, distort color, and destabilize visual cues, so perception must be tailored to underwater image formation and field reliability constraints.
This thesis develops multimodal, 3D-aware perception for adverse marine and deep-sea conditions, based on experiments and integration within the EU projects MORPH, CADDY, and DexROV. By combining complementary sensors (2D imagery, stereo 3D structure, inertial and acoustic cues) with learning pipelines, the approaches compensate for individual sensor weaknesses.
First, it enriches 2D perception with 3D context and underwater-specific enhancement. Contributions include terrain-complexity estimation from texture metrics and stereo geometry to adapt AUV speed during surveys, plus color restoration/image enhancement to improve detection and pose estimation. For human-robot interaction, it introduces diver detection and pose estimation that merge stereo point-cloud descriptors with recurrent neural networks to handle low-contrast imagery.
Second, it presents end-to-end systems, including the CADDY underwater stereo-vision dataset for gesture-based communication and a gesture-recognition pipeline that blends classical learning, deep detectors, and a grammar-guided human-in-the-loop design for safer diver and AUV communication.
Finally, for deep-sea intervention, it proposes a simulation-in-the-loop validation to reduce sim-to-real gaps and an adaptive localization framework fusing dense 3D reconstruction, planar geometry, image-quality cues, and visual odometry to maintain accurate navigation in low visibility. The methods are validated on real data and integrated into autonomous demonstrators for safety-critical missions during field trials.
Mapping challenging confined underwater environments pushes the boundaries of what is possible for state-of-the-art robotics. Current state-of-the-art high-performance equipment allows already for accurate mapping in such scenarios. However, these systems are often expensive. Affordable underwater robotic systems and sensors come with significantly reduced capabilities. Especially sonars are necessary for mapping unknown environments, due to cluttered water resulting in bad visibility for vision based sensors. Yet, affordable sonar sensors suffer from higher noise levels, reduced accuracy, and limited coverage. Consequently, developing methods to achieve reliable and accurate mapping of challenging environments with affordable hardware remains an open research question.
This thesis presents a Fourier-SOFT in 2D (FS2D) registration method for robust matching of high-noise 2D sonar scans. A Simultaneous Localization and Mapping (SLAM) framework designed to the unique challenges of affordable Mechanical Scanning Sonars (MSS) is presented, integrating this FS2D registration method.
In the context of the digitization of cultural heritage, the Bunker Valentin Memorial in Bremen is surveyed, and maps of its multiple basins are generated.
Additionally, this thesis contributes an open dataset with accurate ground truth for development and benchmarking 2D sonar navigation, mapping, and SLAM algorithms.
Overall, this thesis demonstrates that, when the unique characteristics of affordable hard ware are considered correctly, and the methods are designed accordingly, affordable underwater robots can effectively map and explore challenging, unknown environments.
The BlueAUV design, the FS2D registration method, SLAM framework for affordable hardware, and an openly available dataset provide a foundation for advancing robust mapping of challenging underwater environments within the research community.
This thesis investigates consistency challenges in distributed stream processing systems. Prior work on this topic has made significant progress, with many ideas being implemented in state-of-the-art Stream Processing Engines (SPEs). In this thesis, we focus on formal modeling to better characterize existing problems and explore potential improvements.
We introduce a formal model of delivery guarantees and show that deterministic SPEs can theoretically achieve lower latency than non-deterministic ones for exactly-once guarantee. This is supported by experimental results demonstrating that a novel deterministic implementation performs better than current alternatives.
The thesis also presents a formal model for substream management, identifying a lower bound on the additional network traffic required for detecting substream termination. A corresponding framework is implemented that meets this bound and demonstrates improved performance over existing approaches.
These results contribute formal foundations and practical techniques for improving the performance and predictability of distributed stream processing systems.
This thesis addresses the challenge of accurately representing oceanic dynamics characterized by a multitude of interacting processes in numerical models. Specifically, it focuses on the simulation of oceanic circular patterns ranging from 10 to 100 km in diameter, known as mesoscale eddies. These eddies play a critical role in transporting energy, water properties, and nutrients across the ocean. This research uses grid resolutions that directly capture some larger mesoscale eddies (resolved) while employing advanced mathematical techniques to represent the effects of smaller, unresolved eddies. The primary aim of the thesis is to develop and incorporate novel mathematical and numerical approaches into the Finite Volume Sea Ice-Ocean Model (FESOM2) to improve the representation of mesoscale eddies while maintaining manageable computational costs.
To bridge the gap between low-resolution and high-resolution simulations, the study enhances the mesoscale eddy modeling framework formulated by Juricke et al. (2019) through the implementation of new components that address unresolved dynamics. This includes the addition of an advection-based component to capture nonlinear interactions between resolved and unresolved eddies, which demonstrates positive performance. Furthermore, stochastic elements are introduced into the governing equations to better represent small-scale variability missing from deterministic formulations. In parallel, the thesis explores alternative and complementary parameterization strategies, offering fresh perspectives on modeling at partially resolved scales. Each enhancement is rigorously evaluated using a suite of diagnostic tools — many developed as part of this work — with a particular focus on spectral analysis and energy pathways. Overall, the thesis proposes an integrated approach to mesoscale eddy modeling, advancing the accuracy and consistency of ocean simulations across eddy-permitting resolutions.
Tests and proofs are two main techniques in modern software verification. To test a program means running the program to check if its execution yields an expected outcome. To prove a program is to build a mathematical proof, showing the correctness of the program against its desired properties. In the traditional view, however, tests and proofs are considered as two incompatible techniques. They are often treated as warring siblings and mostly applied in isolation. The complementarity of tests and proofs — though not immediately apparent — has been relatively underexplored. Can their combination mitigate each other’s weaknesses while harnessing their respective strengths? This thesis tries to straddle the border between tests and proofs and suggest a concrete answer. It explores how the two approaches can collaborate with and mutually benefit one another. Three key contributions arise from this exploration. The first contribution is Proof2Test, a framework that transforms failed proofs into useful test cases, allowing programmers to use tests to debug failed proofs effectively. The second contribution consists of several proof-based test generation strategies, which use proofs to enhance both the efficiency and effectiveness of test generation. This thesis also extends SC with “loop unrolling”, considering not just zero or one but any number of iterations, up to a set limit. It also includes an empirical study to examine how much (if anything) testing strategies miss when they limit themselves to standard branch coverage and, conversely, how many more bugs we can find if we unroll loops. The last contribution of this thesis is an automatic program repair approach, Proof2Fix, which takes advantages of the proposed test generation methods to produce meaningful corrections to faults revealed by proofs.
This dissertation contributes to three distinct focuses of BF design as: BF for flexible connectivity, BF for enhanced connectivity, and BF for over-the-air-computating (AirComp). The first objective is the “BF for flexible connectivity”. In order to realize flexibility for preserving connectivity regardless of the user position in the coverage area, the recent cell-free MIMO (CF-MIMO) system is considered. For the BF design, a flexible design is proposed, which is directly adaptable not only for both uplink (UL) and downlink (DL) communication modes but also for both under-loaded and over-loaded scenarios. As for the second focus, termed “BF for enhanced connectivity,” a novel BF design compatible with three distinct power allocation schemes is proposed. For the sake of connectivity enhancement, a DL MIMO-rate splitting multiple access (RSMA) system is considered. Even though the proposed BF design is assumed to be used only for under-loaded or fully-loaded scenarios, the computational complexity required to design BF is significantly less than that of the state-of-the-art (SotA) alternative. Finally, the ”BF for Over the Air Computing” is considered for envisioning the realization of integrated AI and communication. In this focus, novel receiver (RX) BF designs compatible with uniform-forcing (UF) precoding for a multi-user UL multiple-input single-output (MISO)-AirComp system are considered for higher performance or lower complexity. Toward higher performance design, while the proposed design sacrifices computational complexity in the BF, the resulting AirComp has a lower mean square error(MSE) performance. On the other hand, the proposed BF design for lower complexity realizes equivalent MSE performance achieved by the high-performance BF design with significantly lower complexity thanks to the combination of recent convex optimization and Bayesian optimization (BO) methods.
Wireless communications technology has gone through a significant evolution since its inception in the late 19th century, integrating itself as a critical pillar of modern society and the functions of the world. Successive generations of the mobile network systems since the first generation (1G) until the present have seen an ever-increasing demand of the key performance indicators (KPIs), such as data rate, reliability, spectral effciency, device connectivity, and more – outlined by the scope of the current fifth generation (5G) systems as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine- type communications (mMTC), to support the emergent use cases such as autonomous and intelligent networks, extended reality (XR) applications, and Internet-of-Things (IoT), under the enabling technologies such as millimeter-wave (mmWave)/Terahertz (THz) bands, massive multiple-input multiple-output (mMIMO), cell-free MIMO (CF-MIMO), reconfigurable intelligent surface (RIS), and more. Furthermore, the imminent beyond fifth generation (B5G) and sixth-generation (6G) systems anticipate even higher requirements and ambitious paradigms in wireless technologies, aiming to improve upon the performance of 5G but also addressing other challenges which have assumed greater importance for the future, such as resource effciency (spectrum, energy, and hardware), physical layer security, system scalability, and high-mobility. In summary, this thesis entails a comprehensive investigation into the realisation of beyond fifth generation (B5G), underpinned by the two major topics of resource-effciency and low-complexity within the frameworks of the two selected enabling technologies, index modulation (IM) and integrated sensing and communications (ISAC), thereby proposing novel methods and analyses from unique yet complementary perspectives of the addressed problem of achieving scalable and high-performance next-generation wireless systems.
The field of logistics and supply chain management deals with various supply network problems on multiple levels starting from strategic years-long decisions regarding network topology, down to operational weekly-based decisions. Moreover, due to the interaction of customers and random accidents, the system becomes stochastic and difficult to control based only on logistic experience. To help with the problem of supply chain design and management, scientists try to approach the field with existing instruments including network science, mathematical modeling, control theory, machine learning, etc. In this thesis, a complex approach that addresses different aspects of supply network management is demonstrated. First, an automatization scheme for the daily management of logistic requirements is proposed. Second, an in-depth investigation of non-conventional usage of natural language processing and machine learning algorithms is presented. The developed approach can be used to enhance the process of requirement management by extracting additional knowledge about the supply network operation. Third, the strategic problem of designing robust supply networks is addressed by developing a minimalistic model of a supply network. The model is designed to simulate a scenario of supply-demand imbalance and generate networks that satisfy the imbalance in a robust way. The overall outcome of the work is a better understanding of separate supply network aspects and an attempt to holistically improve the way how supply networks are managed.
