Split Ring Antennas and Their Application for Antenna Miniaturization
Bibliographic record
Abstract
This paper investigates the miniaturization capability of split ring array antennas embedded in a low-permittivity dielectric substrate, in comparison with the same-sized high-permittivity dielectric resonator antennas (DRAs). In order to understand the miniaturization performance, a size-fixed dielectric substrate with different split ring arrays is studied. The simulation results show that the miniaturization capability increases with decreased unit cell resonant frequency and/or increased unit cell induced permeability. Miniaturizations as high as 25.54 times that of a high-permittivity DRA are obtained with split rings, etched on a dielectric substrate having a low permittivity of 2.2. Furthermore, this excessive miniaturization does not come at the expense of excessive deterioration of the antenna impedance bandwidth, gain, and radiation efficiency. Consequently, the miniaturized split ring arrays still provide high gains over wider bandwidths. This inference is further verified by comparing the miniaturization and other antenna performance parameters with three other modified split ring configurations. To experimentally verify this work, a split ring antenna was fabricated and tested, and good agreement between the simulated and measured results was observed. The results of this study indicate that adding resonant metallic inclusions into low- permittivity DRAs significantly increases their miniaturization capability, without overly deteriorating the performance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".