Electromagnetic Imaging System Calibration With 2-Port Error Models
Bibliographic record
Abstract
Calibration is essential in electromagnetic imaging for converting the raw measurements to a usable form for the imaging algorithm. The complexity of the calibration technique can range between a simple comparison of the raw measurement to those of a known calibration target, to a comprehensive simulation of the entire imaging chamber. This work introduces a novel approach to calibration that models the antennas and field propagation as 2-port networks (rather than scalars or a comprehensive model), for which common network theory and de-embedding techniques can be applied. The accuracy of the proposed 2-port method is experimentally tested against the scalar calibration technique on a 2D imaging system. The use of both metallic and dielectric calibration objects is tested, and the inversion performance is compared for the calibration techniques. For the experimental system tested herein, the use of a 2-port model for each transmitter/receive antenna pair moderately improved both calibration accuracy and image quality compared to a simple scalar calibration coefficient, for the cost of measuring a minimum of 2 known calibration targets.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".