Microwave imaging by mixed-order discontinuous Galerkin contrast source inversion
Bibliographic record
Abstract
Recent developments in microwave imaging algorithms have led to contrast source inversion algorithms based on finite-element method forward solvers. The resulting algorithms are flexible in their ability to support a variety of microwave imaging environments, including metallic boundaries of practically arbitrary shape. One limitation of low-order finite-element based imaging algorithms is that, unless an explicit dual mesh scheme is imposed, the discretization of the contrast and contrast sources is implied by the mesh that describes the underlying field discretization. High-order expansions for the fields, contrasts and contrast sources overcoming this deficiency. In this work we present a high-order contrast source inversion method for dielectric targets that supports distinct expansion orders for the fields, contrasts and contrast sources. High-order expansions for the contrasts essentially decouples the image reconstruction from the underlying mesh discretization. The field solver is based on a high-order frequency-domain discontinuous Galerkin formulation of Maxwell's curl equations. Results presented for both numerical and experimentally collected transverse magnetic data-sets illustrate the potential of the proposed method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".