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Item type:Research Data, Access status: with files , Supplementary Data for the Paper: “Data-Driven Localization of Magneto-Mechanical Resonators from Time Signals”(2026-09-07); ; ; ; This dataset provides the materials necessary to reproduce the paper "Data-Driven Localization of Magneto-Mechanical Resonators from Time Signals". It includes simulated data and background noise to reproduce the training and real measurements, with ground truth to infer the provided model.Data Type: Dataset30 50 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Supplementary Data for the Publication: Parameter Estimation for Model-Based Sensing of Magneto-Mechanical Resonators(2026-02-23); ; ; ; The TUHH Open Research (TORE) repository entails the experimental data of the paper "Parameter Estimation for Model-Based Sensing of Magneto-Mechanical Resonators". Magneto-mechanical resonator (MMR) 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.Data Type: Experimental Data82 98 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Supplementary Data for the Paper: “Deep Learning for Restoring MPI System Matrices Using Simulated Training Data”(2025-12-11); ; ; ; This dataset provides the materials necessary to reproduce the paper “Deep Learning for Restoring MPI System Matrices Using Simulated Training Data.” It includes the simulation parameters used to generate a dataset of simulated system matrices, the corresponding noise, the trained models, and the real measurements used for validation. The code is available at: github.com/IBIResearch/mpi-sm-restoration.Data Type: Dataset227 1446