On the Regularization and Linearization of the Microwave Inverse Scattering Problem Using Subwavelength-Focused Fields
Bibliographic record
Abstract
This communication investigates the use of subwavelength-focused incident fields to reduce the ill-posedness of the microwave inverse scattering problem by decreasing the smoothness of the scattering kernel, while also localizing the fields to reduce multiple scattering and linearize the problem. In the ideal case of a point-like incident field it is demonstrated that the inverse problem becomes well-posed. Improved imaging results are demonstrated using more realistic subwavelength-focused incident fields compared to conventional approaches. Conventional regularization and nonlinear inversion techniques are used. The impact of the full width at half-maximum and sidelobe level of the subwavelength-focused incident field on image accuracy are investigated to assess the feasibility of using subwavelength-focused fields for quantitative imaging.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".