Investigation Into the Application of Artificial Magnetic Conductors to Bandwidth Broadening, Gain Enhancement and Beam Shaping of Low Profile and Conventional Monopole Antennas
Bibliographic record
Abstract
The reflection coefficient phase is investigated for several different artificial magnetic conductors (AMCs) having canonical FSS-type shapes. Three of them are selected, each representing a different class, and fine tuned to exhibit identical resonant frequency. Polarization and angular dependence as well as the effects of losses on these structures are studied. Next, a low-profile inverted L-shape monopole antenna (ILSMA) is placed horizontally above the ground plane. Vertical monopole antenna (VMA) is also placed above them. It is shown that using some of the aforementioned AMCs, the input impedance of both ILSMA and VMA can not only be matched, but also the input impedance bandwidth enhancement as wide as 27% and 35% are obtained, respectively. The VMA study on AMC ground planes which reveals a counter-intuitive phenomenon has not been explored in the literature, previously. It is revealed that the broadband characteristics can also be achieved for smaller size of the AMC ground planes, which enables the antenna to be designed in compact size. It is also illustrated that reflection characteristics of the AMC is not sufficient to evaluate AMC performance when it is used as an antenna ground plane. This is illustrated through extensive simulation and measurement results.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".