Enhancement of microwave tomography through the use of electrically conducting enclosures
Bibliographic record
Abstract
We consider microwave tomography (MWT) where the imaging region is surrounded by an electrically conducting surface. This surface acts as both a shield from outside interference, holding tank for any possible matching media, and, in certain cases, serves to enhance the performance of electromagnetic (EM) inversion algorithms. For the 2D transverse magnetic (TM) case and where the surface consists of a perfect electrical conductor (PEC) in the shape of a circular cylinder, we formulate an appropriate Greens function which is amenable to implementation in the existing EM inversion codes. We utilize this Greens function in the multiplicative-regularized contrast source inversion (MR-CSI) method. Several different synthetic examples are used to test the performance of the inversion when the PEC surface is present and the results show that in many cases, the tomographic image is significantly improved. The reasons for the improved inversion results are an area of active research, but are likely to be due to the increased interrogation energy deposited into the imaging region. Results are also shown which demonstrate the problems which may arise if the unbounded domain Greens function is used in an MWT system that utilizes a matching medium of finite extent—a problem which is overcome by the inclusion of a PEC surface on the exterior of the MWT system.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".