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    Item type:Research Data, Access status: with files ,
    Dataset and Software for: "Simulation Framework for Electrical Impedance Tomography Systems"
    (2026-06-16)
    Liebing, Tom
    ;
    Hollenberg, Moritz
    ;
    Kähler, Dennis
    ;
    Kern, Thorsten Alexander
    Context: This dataset contains the underlying research data supporting the findings of the study published in Measurement: Sensors (Elsevier), identified by PII S2665917424003921. The research focuses on Electrical Impedance Tomography, aiming to validate the proposed models and sensor performance. Content: The repository is organized into three primary categories: Code: Includes the source code (e.g., [Python / MATLAB]) used for simulation setup / data processing / algorithm implementation. Simulation Results: Contains the raw and processed output files generated from COMSOL. Measurement Data: Comprises experimental datasets collected EIT Measurements from a custom hardware Possible Reuse: This data is made available to support further research in the field of Electrical Imedanke Timigraphy.
    Data Type: Software ; Data Type: Measurement and Test Data ; Data Type: Simulation Data
      57  28
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    Item type:Research Data, Access status: with files ,
    Impedance-Based Estimation of Process Parameters in Electrolytic Systems via Circuit-Embedded Neural Networks (CENN) - DATA
    (2026-03-04)
    Ostovar, Hossein
    ;
    Bossert, Marine
    The Data folder contains all data supporting the impedance-based estimation of electrolyte concentration and temperature in aqueous H₂SO₄ using nanoporous gold electrodes. It includes 260 electrochemical impedance spectroscopy (EIS) spectra measured across 20 concentrations (1–20 mM) and 13 temperatures (26–50 °C). Raw measurements are organized by concentration and temperature, together with equivalent-circuit fits (ZARC + transmission-line model) that yield parameters such as solution resistance, interfacial polarization, and porous transport. The folder also holds outputs from the Circuit-Embedded Neural Network (CENN) forward and inverse models, including impedance predictions, concentration and temperature estimates from full and reduced-frequency sets, and Jacobian-based sensitivity analysis that identifies informative frequency bands. Results for different frequency-selection strategies (single-band, multi-band, and full-spectrum) are provided, along with classical machine learning benchmarks (Ridge, SVR, GPR, MLP, etc.) for comparison. The data support the main findings of the paper: CENN achieves a forward RMSE of about 0.06 Ω, inverse estimation with mean absolute errors of roughly 0.10 mM for concentration and 1.0 °C for temperature on full spectra, and comparable accuracy (about 0.12 mM and 1.1 °C) using only three optimized frequency anchors, reducing acquisition time from minutes to about six seconds.
    Data Type: Dataset
      51  45
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    Item type:Research Data, Access status: with files ,
    Simulation-driven Electrical Impedance Tomography for buoyancy-driven electrolyte mixing: A hybrid hydrodynamic–circuit modeling framework
    (2025-12-04)
    Ostovar, Hossein
    ;
    Hollenberg, Moritz
    ;
    Liebing, Tom
    This dataset accompanies the article “Simulation-driven Electrical Impedance Tomography for buoyancy-driven electrolyte mixing: A hybrid hydrodynamic–circuit modeling framework” (Measurement, 2025, https://doi.org/10.1016/j.measurement.2025.119745). The data represent a physics- and hardware-consistent, EIT benchmark case of single-phase, buoyancy-driven mixing of an electrolyte in a laboratory-scale vessel. Starting from numerical solutions of the coupled Stokes–Boussinesq and advection–diffusion equations, local solute concentrations are converted into conductivity fields and then mapped onto a multi-electrode EIT tank model implemented as a resistor network. The resulting files include time-resolved concentration and conductivity fields, axisymmetric conductivity maps, and simulated boundary voltage measurements generated with a circuit-level EIT front-end. Together, these materials allow readers to reproduce the forward simulations in the paper, to regenerate key figures, and to use exactly the same ground-truth fields and measurement data for benchmarking alternative EIT reconstruction algorithms and evaluation metrics described in the publication.
    Data Type: Simulation Data ; Data Type: Source Code ; Data Type: Experimental Data
      101  21
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    Item type:Research Data, Access status: with files ,
    Impedance-Based Estimation of Process Parameters in Electrolytic Systems via Circuit-Embedded Neural Networks (CENN) - SOURCE CODES
    (2026-03-04)
    Ostovar, Hossein
    The Modeling_ML_Scripts folder contains Jupyter notebooks and Python code used for the impedance-based estimation of electrolyte concentration and temperature in aqueous H₂SO₄ with nanoporous gold electrodes. It covers the full workflow from raw EIS data to inverse estimation. Scripts handle data extraction and preprocessing (cutting spectra to selected frequency bands, fitting, and interpolation), physics-aware batch fitting of the ZARC + transmission-line equivalent circuit across the (C,T) domain, and extraction of solution resistance and conductivity with degeneracy analysis. The core modeling includes a Circuit-Embedded Neural Network (CENN) forward model that predicts impedance from concentration (introduced for the first time by the author), temperature, and frequency, and an inverse pipeline that estimates C and T from measured spectra using gradient-based optimization. Sensitivity analysis scripts compute Jacobian-based frequency rankings and band-wise contributions of Z′ and −Z″ to concentration and temperature. Classical machine learning benchmarks (Ridge, SVR, GPR, MLP, Random Forest, etc.) are provided for comparison with the CENN. The folder also includes a requirements file for dependencies and a README with installation instructions, script descriptions, and usage guidance for reproducibility.
    Data Type: Source Code
      33  29
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    Item type:Research Data, Access status: with files ,
    A Cost-Efficient, USB Powered and Pattern Flexible Electrical Impedance Tomography System
    (2026-02-26)
    Liebing, Tom
    ;
    Kern, Thorsten Alexander
    Data, source code, and hardware documentation of an USB Powered and Pattern Flexible. It includes Python control software, STM32 firmware, experimental measurements in frequency, time domain and on system level, and PCB design files. Supplementary Data for the Publication: A Cost-Efficient Electrical Impedance Tomography System for Adaptive Process Monitoring.
    Data Type: Source Code ; Data Type: Measurement and Test Data ; Data Type: Technical Documentation
      54  26