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The 6G standard aims to be an integral part of the future economy by providing high-performance communication and sensing services. At terahertz (THz) frequencies, indoor campus networks can offer the highest sensing quality. Health monitoring in hospitals is expected to be an application site for these. This work outlines a monostatic phase-based system for breathing rate monitoring. Our feasibility study observes motion measurement accuracy down to the micrometer level. However, we also find that the patient's pose needs to be considered for generalized applicability. Thus, a solution that leverages multiple propagation paths and beam orientations is proposed., Comment: 5 pages, 10 figures. Accepted for presentation in: 2023 6th IEEE International Workshop on Mobile Terahertz Systems (IWMTS), Bonn, Germany, July 2023

This study performs a linear analysis of a turbulent reacting methane-air jet flame, with the goal of predicting the response of the reacting flow to upstream acoustic actuation. Accounting for heat release fluctuations is a vital component when investigating thermoacoustic instabilities and flame noise in a linearized framework. Unlike previous studies this work develops and applies an active flame approach, meaning the heat release oscillations of the flame resulting from the acoustic fluctuations are taken into account. To yield an active flame approach in the linear framework, a combustion model needs to be linearized. It is demonstrated that linearizing Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) combustion models leads to closure problems, making their application in the linearized framework troublesome. Reynolds-averaged Navier Stokes (RANS) combustion models, however, prove to circumvent this problem, which makes them suitable candidates for this purpose. The RANS combustion models are linearized around the temporal mean flow of the turbulent jet flame, which is obtained by LES. An a priori analysis shows that a linearized RANS-Eddy Break Up (EBU) model is the best suited among all investigated combustion models for the investigated set-up and reproduces with high accuracy the fluctuations in reaction rate obtained in the LES. Furthermore, the linearized governing equations of the flow including the linearized EBU model for the reaction rate are solved for incoming acoustic perturbations. The response modes show that the reaction rate oscillations are caused by Kelvin-Helmholtz vortex rings, which perturb the jet flame. The results are in good agreement with the LES simulations in terms of the mode shapes of both reaction rate and velocity fluctuations.

Physics-informed neural networks (PINNs) can be used to solve partial differential equations (PDEs) and identify hidden variables by incorporating the governing equations into neural network training. In this study, we apply PINNs to the assimilation of turbulent mean flow data and investigate the method's ability to identify inaccessible variables and closure terms from sparse data. Using high-fidelity large-eddy simulation (LES) data and particle image velocimetry (PIV) measured mean fields, we show that PINNs are suitable for simultaneously identifying multiple missing quantities in turbulent flows and providing continuous and differentiable mean fields consistent with the provided PDEs. In this way, consistent and complete mean states can be provided, which are essential for linearized mean field methods. The presented method does not require a grid or discretization scheme, is easy to implement, and can be used for a wide range of applications, making it a very promising tool for mean field-based methods in fluid mechanics.

Estimating parameters and properties of various materials without causing damage to the material under test (MUT) is important in many applications. Thus, in this letter, we address this by wireless sensing. Here, the accuracy of the estimation depends on the accurate estimation of the properties of the reflected signal from the MUT (e.g., number of reflections, their amplitudes and time delays). For a layered MUT, there are multiple reflections and, due to the limited bandwidth at the receiver, these reflections superimpose each other. Since the number of reflections coming from the MUT is limited, we propose sparse signal processing (SSP) to decompose the reflected signal. In SSP, a so called dictionary is required to obtain a sparse representation of the signal. Here, instead of a fixed dictionary, a dictionary update technique is proposed to improve the estimation of the reflected signal. To validate the proposed method, a vector network analyzer (VNA) based measurement setup is used. It turns out that the estimated dielectric constants are in close agreement with the dielectric constants of the MUTs reported in literature. Further, the proposed approach outperforms the state-of-the-art model-based curve-fitting approach in thickness estimation., Comment: 5 Pages, Accepted in IEEE Sensor Letters, Sept. 2020