A Resistive Mesh Phantom for Assessing the Performance of EIT Systems
Bibliographic record
Abstract
Assessing the performance of electrical impedance tomography (EIT) systems usually requires a phantom for validation, calibration, or comparison purposes. This paper describes a resistive mesh phantom to assess the performance of EIT systems while taking into account cabling stray effects similar to in vivo conditions. This phantom is built with 340 precision resistors on a printed circuit board representing a 2-D circular homogeneous medium. It also integrates equivalent electrical models of the Ag/AgCl electrode impedances. The parameters of the electrode models were fitted from impedance curves measured with an impedance analyzer. The technique used to build the phantom is general and applicable to phantoms of arbitrary shape and conductivity distribution. We describe three performance indicators that can be measured with our phantom for every measurement of an EIT data frame: SNR, accuracy, and modeling accuracy. These performance indicators were evaluated on our EIT system under different frame rates and applied current intensities. The performance indicators are dependent on frame rate, operating frequency, applied current intensity, measurement strategy, and intermodulation distortion when performing simultaneous measurements at several frequencies. These parameter values should, therefore, always be specified when reporting performance indicators to better appreciate their significance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".