arXiv subjects

Sarah Reiss

Publications and source records attributed to Sarah Reiss.

4 recordsLinked to original sources

arXiv2026

The Tilting Mode: A New Degree of Freedom for Magneto-Mechanical Resonator Sensors

Magneto-mechanical resonators (MMRs) are an emerging class of passive, wireless sensors. Their torsional oscillation mode has recently been established for sensing and tracking applications. In this work, we report the identification and characterization of a second mechanical mode, the tilting mode, that provides sensitivity along an axis inaccessible to the torsional mode, opening up a new degree of freedom for tracking and sensing with a single MMR sensor. We derive an analytical model predicting the tilting frequency as a function of the geometric and magnetic parameters of the resonator, compare the tilting mode frequency to that of the torsional mode, and obtain a characteristic frequency ratio between the torsional and the tilting mode in the small angle approximation. Experimental characterization using three-axis excitation and detection confirms the mode's existence and its directional selectivity. Notably, the three-axis frequency response shows no observable cross-coupling between the torsional and the tilting mode. We further show that the tilting mode frequency follows the predicted dependence on magnet distance, confirming the analytical model and the mode's applicability for sensing, analogous to that of the torsional mode.

physics.app-ph↗

arXiv2026

Impact of Structural Design on Magneto-Mechanical Resonators: The Case of a Jewel Bearing Variant

Magneto-mechanical resonators (MMRs) are passive, wirelessly read sensors whose small size, miniaturizability, and low cost make them attractive for tracking and for sensing physical and chemical quantities. Their operation is based on a permanent-magnet rotor whose mechanical resonance encodes the sensing and tracking information. The bearing that suspends this rotor is therefore decisive both for the in-operation performance and for the manufacturability of the device. The original design suspends the rotor on a thin thread, which is nontrivial to assemble. This work investigates the impact of that structural design choice by introducing an alternative bearing in which the spherical rotor magnet rests in a cup-shaped industrial jewel. A dynamic model of this Jewel-MMR is derived, including the angle-dependent dry friction torque at the jewel contact. From it, a geometric trade-off between friction torque and resilience against unwanted oscillation modes is identified and quantified. Since the conventional quality factor loses its meaning under dominant dry friction, a two-tiered framework is proposed that compares MMR variants by estimation precision at equal magnet size and natural frequency. Both variants are characterized at a fixed pose on a Helmholtz-coil detection platform. The Jewel-MMR is self-aligning and assembled in less than half the time from fewer parts. Its response signal decays faster, so that under laboratory noise conditions the natural frequency and the orientation are recovered more precisely from the thread variant. The jewel bearing, however, tolerates a considerably larger deflection angle and thus a stronger signal, which reverses this relation above an experimentally determined noise level and indicates an advantage wherever the signal-to-noise ratio is reduced.

physics.app-ph↗

arXiv2026

Parameter Estimation for Model-Based Sensing of Magneto-Mechanical Resonators

Magneto-mechanical resonators (MMRs) represent a recently proposed type of passive sensor that enables the estimation of its pose as well as sensing other parameters in its environment. The working principle of MMRs entails an excitation of the sensors by oscillating magnetic fields, followed by a readout process facilitated by inductive receiver coils. The sensing technology relies on real-time parameter estimation. This encompasses the solution of a nonlinear inverse problem, with the induced signals and a suitable forward model as inputs. The aim of this paper is twofold: first, to introduce a reference model and simplified models for the MMR dynamics and inductive readout, and second, to provide robust and real-time capable methods to estimate the model parameters. The effectiveness of the presented methods is evaluated in terms of their real-time potential, precision, and accuracy. All presented methods demonstrate the capacity to estimate the measured signal, with the simplified methods reducing the corresponding parameter estimation time by up to two orders of magnitude at the expense of less than 4 % deviation for large maximum deflection angles.

physics.app-ph↗

arXiv2025

Deep Learning for Restoring MPI System Matrices Using Simulated Training Data

Magnetic particle imaging reconstructs tracer distributions using a system matrix obtained through time-consuming, noise-prone calibration measurements. Methods for addressing imperfections in measured system matrices increasingly rely on deep neural networks, yet curated training data remain scarce. This study evaluates whether physics-based simulated system matrices can be used to train deep learning models for different system matrix restoration tasks, i.e., denoising, accelerated calibration, upsampling, and inpainting, that generalize to measured data. A large system matrices dataset was generated using an equilibrium magnetization model extended with uniaxial anisotropy. The dataset spans particle, scanner, and calibration parameters for 2D and 3D trajectories, and includes background noise injected from empty-frame measurements. For each restoration task, deep learning models were compared with classical non-learning baseline methods. The models trained solely on simulated system matrices generalized to measured data across all tasks: for denoising, DnCNN/RDN/SwinIR outperformed DCT-F baseline by >10 dB PSNR and up to 0.1 SSIM on simulations and led to perceptually better reconstuctions of real data; for 2D upsampling, SMRnet exceeded bicubic by 20 dB PSNR and 0.08 SSIM at $\times 2$-$\times 4$ which did not transfer qualitatively to real measurements. For 3D accelerated calibration, SMRnet matched tricubic in noiseless cases and was more robust under noise, and for 3D inpainting, biharmonic inpainting was superior when noise-free but degraded with noise, while a PConvUNet maintained quality and yielded less blurry reconstructions. The demonstrated transferability of deep learning models trained on simulations to real measurements mitigates the data-scarcity problem and enables the development of new methods beyond current measurement capabilities.

eess.IV↗