Hybrid Approaches in Microwave Imaging Using Quantitative Time- and Frequency-Domain Algorithms
Bibliographic record
Abstract
In this work we propose two hybrid time-and frequency-domain microwave imagingschemes aimed to improve time-to-solution of quantitative time-domain imaging algorithms and image resolution of quantitative frequency-domain imaging algorithms. The proposed hybrid methods combine discontinuous Galerkin method (DGM) implementations of the time-domain (TD) forward-backward time-stepping (FBTS) algorithm and the frequency-domain (FD) contrast source inversion (CSI) or Gauss Newton Inversion (GNI). Simply put, an initial inversion in one domain (time or frequency) is used as prior information for the other. These schemes, referred to as FD-TD when FD prior is used in a TD algorithm, and TD-FD when TD prior is used in a FD algorithm, are applied to experimental and synthetic data. The results of the hybrid imaging approaches manifest an appreciable improvement relative to the stand-alone of FD and TD algorithms. Specifically, this study demonstrates that low-resolution frequency-domain prior information improves TD convergence. Additionally, we show that early-iteration time-domain solutions improves FD algorithm performance. We hope that these hybridization techniques pave the way for future investigations of optimal strategies for combining TD and FD schemes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".