Frequency-Diverse Bunching Metasurface Antenna for Microwave Computational Imaging
Bibliographic record
Abstract
A frequency-diverse bunching metasurface antenna (FDBMA) that can be used for microwave computational imaging (MCI) systems is proposed in this article. The proposed FDBMA can generate low-correlated radiation patterns with a reduced frequency interval of 20 MHz and a bunching angle of$45\mathrm {^{\circ }}$from 32 to 36 GHz. The frequency interval is reduced by combining a disordered cavity and an optimized frequency-diverse random metasurface. The directivity of the radiation patterns is improved by leveraging the joint-bunching method that combines the metal baffle, the Fresnel dielectric lens (FDL), the quasi-gradient random metasurface, and the random-coherent superposition comprehensively. The performance of the proposed FDBMA is evaluated in terms of the reflection coefficient, singular value decomposition (SVD) of the sensing matrix, and correlation coefficients (CCs) of the measurement modes. The reduced frequency interval and the bunching characteristic are also demonstrated. Finally, MCI experiments are implemented using the proposed FDBMA. Comparative experiments are also carried out to validate the advantage of reducing the frequency interval and improving the directivity.
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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 | 0.001 | 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".