Use of Field-Perturbing Elements to Increase Nonredundant Data for Microwave Imaging Systems
Bibliographic record
Abstract
Field-perturbing elements (FPEs) are introduced for microwave imaging. These elements affect the imaging performance by increasing the amount of nonredundant data. Although this technique can be implemented in nonmetallic chambers, it is especially effective inside metallic enclosures where small perturbations can change the interrogating fields significantly. Results of simulations and a numerical investigation based on synthetic data are presented. The method is validated using an experimental system comprised of 24 coresident radially oriented monopoles that collect the normal component of the electric field on the inside surface of the enclosure. The measured data are used as input to a finite-element contrast source inversion algorithm. To investigate the effectiveness of the approach, a second experimental example is presented where a simplistic breast phantom with a tumor inclusion is imaged inside a smaller cylindrical chamber with 18 radially oriented monopoles and a single FPE. Because FPEs are easy to manufacture and are low costs, they can reduce the cost of an imaging system significantly by reducing the number of required RF ports, as well as reducing the system complexity and modeling error.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".