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Item type:Publication, Access status: with files , Parameter estimation for model-based sensing of magneto-mechanical resonatorsMagneto-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 themodel 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.Publicationtype: Journal Article21 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Simulating Gadolinium-induced magnetic field variations for temperature sensing with magneto-mechanical resonatorsSmall-size magneto-mechanical resonators (MMR) represent an emerging class of passive, wireless sensors that combine a sensing functionality with a tracking option. The operation principle is based on a resonating rotor oscillation whose frequency is defined by the magnetic flux density of a stator magnet. One general sensing mechanism is the coupling of an external parameter to this resonator frequency. In this study, we investigate an approach for encoding a temperature information as a shift in the natural oscillation frequency utilizing the temperature-dependent magnetic properties of gadolinium (Gd). We perform an isolated simulation study on the temperature scaling of the magnetic field generation for stators coated with Gd of varying thickness. Our results show that the magnetic phase transition of Gd at its Curie temperature leads to a pronounced change in the magnetic permeability enabling a significant magnetic shielding behavior only for lower temperatures. In the transition regime, we find a peak sensitivity reaching 45.8 Hz/K exceeding existing values from the literature by up to a factor of 20. The findings of this work are an important step toward quantitative high-sensitivity temperature extraction with MMRs.Publicationtype: Conference Paper95 50 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Natural frequency dependency of magneto-mechanical resonators on magnet distanceThe precise derivation of physical quantities like temperature or pressure at arbitrary locations is useful in numerous contexts, e.g. medical procedures or industrial process engineering. The novel sensor technology of magneto-mechanical resonators (MMR), based on the interaction of a rotor and stator permanent magnet, allows for the combined tracking of the sensor position and orientation while simultaneously sensing an external measurand. Thereby, the quantity is coupled to the torsional oscillation frequency, e.g. by varying the magnet distance. In this paper, we analyze the (deflection angle-independent) natural frequency dependency of MMR sensors on the rotor-stator distance, and evaluate the performance of theoretical models. The three presented sensors incorporate magnets of spherical and/or cylindrical geometry and can be operated at adjustable frequencies within the range of 61.9-307.3 Hz. Our proposed method to obtain the natural frequency demonstrates notable robustness to variations in the initial deflection amplitudes and quality factors resulting in statistical errors on the mean smaller than 0.05%. We find that the distance-frequency relationship is well described by an adapted dipole model accounting for material and manufacturing uncertainties. Their combined effect can be compensated by an adjustment of a single parameter which drives the median model deviation generally below 0.2%. Our depicted methods and results are important for the design and calibration process of new sensor types utilizing the MMR technique.Publicationtype: Journal Article61 58 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Wireless and passive pressure detection using magneto-mechanical resonances in process engineering(IOP Publishing, 2025-08-31); ; ; ; A custom-developed magneto-mechanical resonator (MMR) for wireless pressure measurement is investigated for potential applications in process engineering. The MMR sensor utilises changes in the resonance frequency caused by pressure on a flexible 3D printed membrane. The thickness of the printed membrane plays a crucial role in determining the performance and sensitivity of MMRs and can be tailored to meet the requirements of specific applications. The study includes static and dynamic measurements to determine the pressure sensitivity and temporal resolution of the sensor. The results show a minimum sensitivity of 0.06 Hz mbar − 1 and are in agreement with theoretical calculations and measurements. The maximum sensor readout frequency is 2 Hz in this study. Additionally, the temperature dependence of the sensor is investigated, revealing a significant dependence of the resonance frequency on temperature. The developed MMR offers a promising and versatile method for precise pressure measurements in process engineering environments.Publicationtype: Journal Article75 70 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Neural implicit representations for grid-agnostic MPI reconstructionsMagnetic particle imaging (MPI) reconstructs the spatial distribution of magnetic nanoparticles on a fixed grid, the resolution of which is limited by the noise present in the system. This paper addresses the reconstruction problem while integrating single-image super-resolution for concentration maps. We introduce Neural Implicit Representations (NIR) as an image prior, enabling arbitrary grid size sampling after training. Experimental results using a spiral phantom measurement reveal that NIR-based reconstruction maintains image sharpness across diverse grid sizes, surpassing the two-stage Kaczmarz-ℓ2 reconstruction followed by bicubic up-sampling in preserving fine structural details. This technique has a potential for high-resolution MPI imaging without relying on extensive datasets.Publicationtype: Journal Article23 47 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Empirical study of magnet distance on magneto-mechanical resonance frequency(De Gruyter, 2024-12-19); ; ; ; Determining the position and orientation of amedical instrument is essential for accurate procedures in en-doscopy, surgery, and vascular interventions. Recently, a novelsensor based on torsional pendulum-like magneto-mechanicalmotion has been proposed. This sensor is passive, wireless andinductively coupled to a transmit-receive coil array. This setupallows the determination of all 6 degrees of freedom usingthe characteristic resonance of the sensor. Additional physicalquantities such as temperature and pressure can be measuredbased on the frequency of the sensor, which mainly dependson the distance between the two involved permanent magnets.In this study, we analyze a sensor composed of two magneticcylinders with variable magnet-to-magnet distance and a basicphysical model based on a dipole assumption. Experimentalanalysis of the resonance frequency and comparison with themodel values show both qualitative and quantitative agreementwith an average relative error of only 0.8 %. This validates theimplemented model and shows the suitability of our magnetic-mechanical resonator made from cylindrical permanent mag-nets for sensing applications.Publicationtype: Journal Article25 75 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Extension of the Kaczmarz algorithm with a deep plug-and-play regularizer(Infinite Science Publishing, 2024); ; ; ; The Kaczmarz algorithm is widely used for image reconstruction in magnetic particle imaging (MPI) because it converges rapidly and provides good image quality even after a few iterations. It is often combined with Tikhonov regularization to cope with noisy measurements and the ill-posed nature of the imaging problem. In this abstract, we propose to combine the Kaczmarz method with a plug-and-play (PnP) denoiser for regularization, which can provide more specific prior knowledge than handcrafted priors. Using measurement data of a spiral phantom, we show that Kaczmarz-PnP yields excellent image quality, while speeding up the already fast convergence. Since the PnP denoiser is not coupled to the imaging operator, the Kaczmarz-PnP method is very generic and can be used for image reconstruction independently of the measurement sequence and MPI tracer type.Publicationtype: Journal Article47 117