Magneto-acousto-electrical tomography: a potential method for imaging current density and electrical impedance
Why this work is in the frame
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Bibliographic record
Abstract
Primarily this report outlines our investigation on utilizing magneto-acousto-electrical-tomography (MAET) to image the lead field current density in volume conductors. A lead field current density distribution is obtained when a current/voltage source is applied to a sample via a pair of electrodes. This is the first time a high-spatial-resolution image of current density is presented using MAET. We also compare an experimental image of current density in a sample with its corresponding numerical simulation. To image the lead field current density, rather than applying a current/voltage source directly to the sample, we place the sample in a static magnetic field and focus an ultrasonic pulse on the sample to simulate a point-like current dipole source at the focal point. Then by using electrodes we measure the voltage/current signal which, based on the reciprocity theorem, is proportional to a component of the lead field current density. In the theory section, we derive the equation relating the measured voltage to the lead field current density and the displacement velocity caused by ultrasound. The experimental data include the MAET signal and an image of the lead field current density for a thin sample. In addition, we discuss the potential improvements for MAET especially to overcome the limitation created by the observation that no signal was detected from the interior of a region having a uniform conductivity. As an auxiliary we offer a mathematical formula whereby the lead field current density may be utilized to reconstruct the distribution of the electrical impedance in a piecewise smooth object.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it