Far-Field Magnification of Subdiffraction Conducting Features Using Metamaterial-Lined Aperture Arrays
Bibliographic record
Abstract
This paper offers a new approach for far-field high-resolution imaging of conducting obstacles based on arrays of frequency-multiplexed subwavelength resonant elements. Each resonator is a circular aperture in a metallic screen that is strongly miniaturized by means of loading by a thin epsilon-negative and near-zero metamaterial (MTM) liner. Each MTM-lined aperture exhibits a fano-shape transmission profile, i.e., a peak followed by a minimum, and the resonance frequencies of different apertures are chosen such that the resonance of one lies within/very close to the antiresonance of the other to ensure strong decoupling. This paper shows that blocking an aperture using a conducting disc removes the corresponding resonance peak/minimum from the transmission/far-field amplitude spectrum, enabling far-field detection of any distribution of such obstacles with a spatial resolution determined by the aperture sizes, which measure less than one-sixth of free-space wavelength at their respective resonance frequencies. The proposed imaging mechanism is verified through full-wave HFSS simulations as well as far-field measurements. Some challenges associated with this approach are then discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".