Talks

In 2001 bi-continuous semigroups were introduced in the PhD thesis by Kühnemund. Such semi-
groups operate on a Banach space in the presence of another norming Hausdorff locally convex
topology that is used to describe their continuity properties, so-called bi-continuity. The theory
of bi-continuous semigroups was advertised as being more applicable than the already long exist-
ing theory of strongly continuous, locally equicontinuous semigroups on Hausdorff locally convex
spaces.
In this talk, we will show that this advertisement has something in common with many other adver-
tisements, it is misleading, not only for semigroups but also for cosine families (and other operator
families). Namely, we will show that bi-continuous semigroups and cosine families are exactly the
strongly continuous, locally—even exponentially—equicontinuous semigroups and cosine families
with respect to the mixed topology under some very mild assumptions.
This talk is based on joint work with Christian Seifert

Zoomlink:
https://tuhh.zoom.us/j/85249558979?pwd=lqzsbf4fkjWnNmaeuhNwUNV9Bv64MR.1

Zoomlink:
https://tuhh.zoom.us/j/82040837302?pwd=brm8cbX5IgJyHnHqqvGuroBFT1afJr.1

Electromagnetic Compatibility (EMC) and Signal Integrity (SI) are areas of electrical engineering that are vital to the functionality of all electronic devices and systems. EMC ensures compliance with internal and external electromagnetic interference. SI ensures error-free ransmission of data over wired interconnects; for example, on printed circuit boards. To address the highly complex, cross-disciplinary design for EMC and SI, decomposition, segmentation, and subsequent hierarchical, physics-based modeling are performed. Although an active field of research, machine learning (ML) has yet to become an inherent part of modeling. Given the limited availability of generalized datasets and highly complex tasks, the adoption of ML cannot be achieved using a brute-force, universal black-box approach. Instead, the modeling
hierarchy must be considered to introduce ML efficiently. Through analysis of EMC in electric vehicle powertrains and SI in high-speed interconnects we derive a general hierarchical modeling structure. Coupled with a review of ML methods and applications, we demonstrate how ML can be integrated into the established modeling workflow, demonstrated with hierarchical Gaussian process regression, neural networks, and a novel interface for circuit comprehension with large language models.

Zoomlink:
https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09

Talk (PDF, 147KB)

Radar technology enables the remote sensing of vital-sign parameters such as breathing and heart rate by measuring the temporal movements of the chest and micro-vibrations on the skin surface. The radar sensor compares the relative phase shift between the sinusoidal waveforms it transmits and receives upon scattering at the skin surface, and, thus, can resolve relative displacements orders of magnitude smaller than the wavelength. Typically, the resolution is in the single- to two-digit micrometer range. Operating without physical contact, the radar-based systems prove advantageous over state-of-the art medical instruments in terms of enabling continuous monitoring of neonates or patients after surgeries without introducing skin irritations. Although the fundamental feasibility has already been demonstrated by a multitude of proof-of-concept publications and clinical studies, it is still challenging to guarantee robustness of this method. This particularly holds true if the focus is on medically relevant parameters to be resolved in time domain such as the detection of the onset of individual heart beats. To understand the lack of robustness, a physically more accurate and, thus, mathematically more involved model of the interaction between the receive signal and the skin surface needs to be analyzed to account for nonnegligible wave propagation effects that previously have been ignored. According to this model, the radar receive signal effectively accords with the evaluation of an integral over the skin surface. This triggers the question of whether and how one could exploit the more accurate model to retrieve the skin surface motion more reliably and, thus, improve the robustness of radar-based vital-sign parameter estimation.

Zoomlink:
https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09