Modern digital imaging pipelines transform incident light from the physical world into high-level semantic information for a variety of applications, such as medical imaging, robotics, visualization, and autonomous driving. They consist of multiple stages, including the focusing of light on an image sensor by optical systems, the measurement on the sensor itself, and further processing by an image signal processor (ISP) that also reduces noise and other errors from the measurement process, before the image is consumed by a task-specific algorithm such as a deep neural network. Conventionally, the training of the network is largely decoupled from the rest of the components, which are typically optimized for general-purpose use cases or human perception. This thesis investigates how imaging pipelines can be improved (i) as a whole by jointly optimizing sensor parameters and downstream learning-based algorithms, and (ii) by developing efficient low-level reconstruction and error-removal methods to handle the unavoidable imperfections introduced in the measurement process. The first part of this thesis focuses on the task-driven design of sensors. It develops methods to optimize the physical position of pixels on a sensor and the shape of spectral color filters, jointly with a downstream neural network to solve specific tasks. Although learned sensor layouts show a significant improvement in a variety of tasks compared to uniform grids, no such improvement could be found for learned spectral color filters in experiments for the specific task of semantic segmentation in autonomous driving, suggesting that, in this setting, the information gained from the spatial positioning of pixels is more impactful than fine-grained spectral information. The second part of the thesis improves the handling of imperfect measurements through several methods. It includes a provably convergent plug-and-play variational scheme with learned priors for solving inverse problems, a generic depth image enhancement method utilizing normal consistency constraints, and an outlier-robust point-based rendering algorithm that can directly render very noisy point clouds produced by depth sensors.

Background: The potential of artificial intelligence (AI) and AI-based systems for healthcare has steadily increased in recent years. AI-based clinical decision support systems (CDSS) make it possible to rapidly identify patterns within large datasets and generate robust predictions to support clinical decision-making – including in the context of antibiotic therapy. However, hospitals in Germany currently make little use of AI-based CDSS, partly because their implementation is complex. Objective and methods: The aim is to examine the requirements for successful implementation of AI-based CDSS for antibiotic therapy in hospitals from the perspective of healthcare stakeholders, and to derive future research and action needs. Four studies examined determinants of implementation using both quantitative and qualitative research methods. All peer-reviewed publications draw on the structuring elements of the Human–Organization–Technology(HOT)-fit model and to systematically outline facilitators and barriers that determine the implementation process of AI-based CDSS. Results: The imple-mentation of AI-based CDSS involves organizational, user-related, and technology-related measures, with the characteristics of the systems themselves representing key determining factors. In particular, usability, interoperability, recommendation quality, and system transparency set the course for successful implementation. At the organizational level, real-time-capable infrastructures, readiness for change, resources, as well as political and regulatory frameworks concerning education, responsibility, and financing constitute central requirements. Insufficient readiness for change often results from situational factors, including a perceived lack of need or the complexity of new technologies being considered too high. This may also reflect underlying fears or feelings of being overwhelmed. In addition, the attitudes, knowledge, and perceived clinical benefit among potential users are crucial. At the same time, the boundary between decision support and decision-making requires AI-specific competencies and an ongoing discourse on normative and professional-ethical issues. Conclusion: Implementation processes must take into account organizational structures, cultural and professional norms, as well as social and political factors such as guidelines, laws, and regulations. Interdisciplinary and transdisciplinary approaches are necessary to identify problem areas at an early stage and to anticipate practical as well as ethical challenges.

The development of lightweight structures is a complex and interdisciplinary process that inte-grates the fields of design, materials science, and manufacturing engineering. Increasing re-quirements due to e-mobility influence the subcomponents of the chassis directly - higher loads and demands for such lightweight structures with unchanged cost pressure. Hybrid structures were proven to be effective within this context. In the state of the art, different metal-plastic hybrids are presented. The objective of this work is to further advance such hybrid systems by substituting steel with a unidirectional glass-fiber-reinforced thermoset (UD-GFRP). Owing to its high specific strain energy and simultaneously high strength, UD-GFRP is well suited for load-path-optimized structural components. How-ever, the introduction of load transfer elements into such structures is both complex and often accompanied by a reduction in structural performance. By combining the continuously fiber-reinforced thermoset carrier structure with thermoplastic injection-molded material to form a hybrid component, these disadvantages can be largely mitigated. Nevertheless, research gaps remain with respect to the interfacial adhesion of thermoplastic–thermoset hybrids and the influence of surface pretreatment methods. The combination of dissimilar materials in a hybrid structure inherently leads to residual stresses due to differences between the curing or bonding temperature 𝑇𝐻 and the operating temperature 𝑇𝐸. These residual stresses may result in a reduction of the overall bond strength within the hybrid system. For thermoset–thermoplastic hybrids, thermally induced residual stresses have so far only been insufficiently investigated. The intrinsic hybrid structure developed in this work – consisting of a thermoset serving as a stiffening element and a thermoplastic serving as a load introduction element – is designed for application in highly loaded components. The development focuses on three main aspects: First, the bond between thermoset and thermoplastic is examined with particular attention to the adhesion mechanisms and potential methods for their improvement. The second part of the study investigates the residual stresses arising during the thermal manufacturing process are investigated, which result from the mismatch in the coefficients of thermal expansion of the constituent materials. In the final part, the interaction within the hybrid composite is examined using an application-oriented demonstrator. Specifically, it is assessed whether residual stresses can be relieved and how this affects the demonstrator’s service life. Based on the findings, a recommendation for an optimal interface between the thermoplastic and thermoset materials will be provided. Finally, a recommendation is derived for an optimized interface de-sign between thermoplastic and thermoset materials.

Implant-associated infections and insufficient tissue integration remain central challenges in the development of multifunctional biomaterials. These challenges have driven the need for modern implant systems to evolve from initially bioinert devices into regenerative tools for tissue engineering. It is well established that the implant surface plays a critical role in addressing these challenges. Consequently, significant research efforts have been directed toward optimizing implant surfaces to improve biological performance. The use of nanostructured metal oxides as coatings on implant surfaces has attracted considerable interest in this regard. Increasing evidence suggests that such nanotopographies can enhance cellular responses and act as reservoirs for controlled drug release. This thesis aims to develop and investigate zirconia nanotubes (ZrNTs) based surface coatings as multifunctional implant interfaces, with a focus on controlled drug delivery, antibacterial functionality, and host cell response interactions. ZrNTs with designed morphologies were fabricated via electrochemical anodization by systematically varying anodization parameters. Several simple yet novel structural and chemical modifications were made to the fabricated ZrNT to improve drug-delivery performance, antibacterial properties, and cell-interaction behavior. Structural and chemical characterization was performed using mainly scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and time of flight secondary ion mass spectrometry (ToF-SIMS). The main results of this dissertation include ZrNT with a modulated diameter (i.e., a bottleneck-like structure), which was successfully developed to mitigate initial burst drug release and enable an extended-release profile. Incorporation of silver nanoparticles via straightforward anodization of Zr-Ag alloys resulted in ZrNTs with broad–spectrum antibacterial activity. Protein adsorption studies revealed that surface chemistry (specifically, functionalization with hydroxyapatite) and morphology influence protein orientation and denaturation, which, in turn, affect cell–surface interactions. The findings of this thesis demonstrate novel, simple, yet effective approaches toward optimizing the interface between the implant surface and the host environment to meet the critical requirements of biocompatibility and antibacterial properties. This work contributes to the design of smart, multifunctional biomaterials for improved implant performance. It introduces the potential of zirconia nanotubes as a versatile, tunable coating for bioactive, controlled drug-releasing implant surfaces.