arXiv subjects

Sebastian Reiter

Publications and source records attributed to Sebastian Reiter.

7 recordsLinked to original sources

arXiv2026

LEMONS: Leveraging Model-Based Techniques to Enable Non-Intrusive Semantic Enrichment in Wireless Sensor Networks

The paper presents an efficient approach to the semantic enrichment of measured sensor data in Wireless Sensor Networks (WSNs), by bridging techniques from Model-driven Software Development (MDSD) and Semantic Web Technology (SWT). Our approach reinforces data interoperability, fostering data sharing and reuse, by utilizing SWT. Model-based and type-agnostic configuration reduces the overall effort for WSN setup and maintenance, which are traditionally complex and time-consuming tasks. The presented approach addresses the problem of large-scale WSN management through the application of SWT in WSN configuration and management without requiring expert knowledge. Additionally, we present a generic architecture and an implementation which is also supplemented by hands-on descriptions of an illustrative use case. Our experimental results demonstrate that our model-based approach provides non-intrusive semantic enrichment with sub-millisecond computational overhead, as well as partially automated configuration of WSNs.

cs.NI↗

arXiv2026

Ontology-supported AI Model and Dataset Management

Recently, there has been a great deal of research into improving AI methods and their application. The main focus is on tracking progress, enabling transparent comparisons, and fostering a more profound understanding of AI. In that process, different organizations generate and use plenty of assets that need to be tracked, traced and managed. Moreover, it is important to discover assets relevant for the task at hand. This paper presents research aiming to contribute to answering the question of what is required to exchange and manage AI models and related assets effectively without semantic gaps in an industrial context. We introduce a platform for AI model exchange, which facilitates the usage, exchange, and analysis of AI models and datasets. The platform incorporates an ontology that can foster a more profound common understanding of what is required in these tasks and help tackle the issues mentioned above. Finally, we elucidate the utility of the platform through the illustration of a use case in the context of real-time critical systems.

cs.AI↗

arXiv2026

On-Device Adaptive Battery Power Prediction for Electric Vehicles

Adaptive power management in Electric Vehicles (EVs) requires accurate power prediction. Although deep learning models have emerged as highly effective for time-series forecasting in this domain, their performance is prone to degradation when exposed to data with distributions different from the training data. We introduce a novel approach that enables on-device learning in resource-constrained EV systems to continuously adapt pretrained battery prediction models to new, unseen data. We leverage existing pretrained models by transforming them into adaptable versions that retain critical hyperparameter knowledge from their initial training. We comprehensively investigate both online and offline model adaptation strategies. Our results demonstrate significant improvements in forecasting performance across various models and time horizons, achieving mean absolute error reductions of up to 7.49\% and 14.88\% with online and offline adaptation techniques, respectively. This study highlights the substantial benefit of on-device adaptation, resulting in enhanced battery power predictions than unadapted model deployments in real-world EV scenarios.

cs.LG↗

arXiv2026

Sensor-Adaptive Infrared Spectral Reconstruction with Plug-and-Play Diffusion Priors

Hyperspectral sensing enables material identification; however, state-of-the-art spectrometers are costly and bulky, which limits their use in mobile applications. We address this by proposing sparse spectrum reconstruction from narrowband photocurrents using a pseudoinverse-guided diffusion model ({\Pi}GDM). With {\Pi}GDM we use a denoising diffusion probabilistic model (DDPM) to reconstruct the spectrum, which is trained on a large public spectral dataset to learn realistic spectral priors, eliminating the need for paired sensor measurements. At inference, {\Pi}GDM alternates reverse-diffusion denoising steps with pseudoinverse projection to enforce consistency with measured photocurrents via the calibrated responsivity matrices of sensors. Consequently, our method is sensor-adaptive: when detector arrays change, we simply substitute the responsivity matrix in the pseudoinverse projection without retraining of the diffusion model. The resulting computational spectrometer achieves 1.502% average estimation error, outperforming Tikhonov, Gaussian, compressive-sensing, and multilayer perceptron (MLP) baselines, while providing calibrated uncertainty estimates via Monte Carlo sampling from different random initializations of {\Pi}GDM. Summarizing, our approach offers an accurate, compact alternative for spectral recovery on resource-constrained platforms.

eess.SP↗

arXiv2021

Verification of Component Fault Trees Using Error Effect Simulations

The growing complexity of safety-relevant systems causes an increasing effort for safety assurance. The reduction of development costs and time-to-market, while guaranteeing safe operation, is therefore a major challenge. In order to enable efficient safety assessment of complex architectures, we present an approach, which combines deductive safety analyses, in form of Component Fault Trees (CFTs), with an Error Effect Simulation (EES) for sanity checks. The combination reduces the drawbacks of both analyses, such as the subjective failure propagation assumptions in the CFTs or the determination of relevant fault scenarios for the EES. Both CFTs and the EES provide a modular, reusable and compositional safety analysis and are applicable throughout the whole design process. They support continuous model refinement and the reuse of conducted safety analysis and simulation models. Hence, safety goal violations can be identified in early design stages and the reuse of conducted safety analyses reduces the overhead for safety assessment.

cs.SE↗

arXiv2020

Photoprotecting uracil by coupling with lossy nanocavities

We analyze how the photorelaxation dynamics of a molecule can be controlled by modifying its electromagnetic environment using a nanocavity mode. In particular, we consider the photorelaxation of the RNA nucleobase uracil, which is the natural mechanism to prevent photodamage. In our theoretical work, we identify the operative conditions in which strong coupling with the cavity mode can open an efficient photoprotective channel, resulting in a relaxation dynamics twice as fast than the natural one. We rely on a state-of-the-art chemically-detailed molecular model and a non-Hermitian Hamiltonian propagation approach to perform full-quantum simulations of the system dissipative dynamics. By focusing on the photon decay, our analysis unveils the active role played by cavity-induced dissipative processes in modifying chemical reaction rates, in the context of molecular polaritonics. Remarkably, we find that the photorelaxation efficiency is maximized when an optimal trade-off between light-matter coupling strength and photon decay rate is satisfied. This result is in contrast with the common intuition that increasing the quality factor of nanocavities and plasmonic devices improves their performance. Finally, we use a detailed model of a metal nanoparticle to show that the speedup of the uracil relaxation could be observed via coupling with a nanosphere pseudomode, without requiring the implementation of complex nanophotonic structures.

cond-mat.mes-hall↗

arXiv2014

Hanle-effect measurements of spin injection from Mn5Ge3C0.8/Al2O3-contacts into degenerately doped Ge channels on Si

We report electrical spin injection and detection in degenerately doped n-type Ge channels using Mn5Ge3C0.8/Al2O3/n^{+}-Ge tunneling contacts for spin injection and detection. The whole structure is integrated on a Si wafer for complementary metal-oxide-semiconductor compatibility. From three-terminal Hanle-effect measurements, we observe a spin accumulation up to 10 K. The spin lifetime is extracted to be 38 ps at T = 4K using Lorentzian fitting, and the spin diffusion length is estimated to be 367 nm due to the high diffusion coefficient of the highly doped Ge channel.

cond-mat.mes-hall↗