Well-posed microwave imaging in focusing media: 2D generalization and impact on convergence of the contrast source inversion method
Bibliographic record
Abstract
Summary form only given. The imaging experiments of microwave tomography are typically conducted in free space or other types of environment lacking focusing properties. Such experiments lead to inherent ill-posedness of the underlying inverse problem. This ill-posedness dramatically complicates reconstruction of the sought object properties from the collected information about its scattered field. In our recent work (Okhmatovski, et.al., IEEE TAP, vol. 60, no. 5, pp. 2418-2430, 2012) we showed that the ill-posedness of the inverse problem is not inherent, however, but rather originates from improperly staged imaging experiments. If the medium in which imaging experiment is conducted features focusing properties and the field scattered by the object is collected at properly prescribed locations the ill-posedness of the imaging experiment can be eliminated. Examples of such focusing media formed by Veselago Lens, Maxwell Fish Eye Lens, and parabolic mirror have been previously shown to allow for direct reconstruction of 1D objects. In this work we demonstrate numerical experiments in which the Veselago Lens is utilized for direct non-regularized reconstruction of 2D objects. We also study the impact of media in which imaging experiment is staged on the convergence Contrast Source Inversion (CSI) iterative algorithm for solution of the inverse problem.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".