High-gain UWB Fabry–Perot cavity antenna with dual-notched band for high-resolution imaging applications
Bibliographic record
Abstract
Abstract In this paper, we propose an ultra-wideband (UWB) Fabry–Perot cavity (FPC) antenna with dual-notch (DN) bands, utilizing a partially reflective surface (PRS) as a superstrate and an artificial magnetic conductor (AMC) reflector to support and enhance a DN band UWB antenna. The antenna components work synergistically to improve gain and provide directional radiation characteristics, while effectively mitigating interference from 5G and WLAN signals in urban environment. The proposed FPC design is executed in two main steps. First, a planar monopole UWB antenna is designed to operate within the frequency range of 2.69 GHz–12.27 GHz, incorporating a DN at 5G-3.5 GHz and 5 GHz WLAN bands through a single-slotted electromagnetic bandgap (EBG) unit-cell placed near the feedline. Second, a 5 × 5 array of AMC reflector elements and a PRS are strategically placed at specific distances from the UWB antenna to increase the gain. The resulting FPC structure was designed, optimized in HFSS, fabricated, and experimentally validated. Both measured and simulated results confirm that the proposed FPC structure achieves a peak gain of 10.21 dBi at 8.8 GHz, highlighting its potential to address challenges in meeting UWB application requirements, including Radar systems dedicated to high-resolution infrastructure monitoring and microwave medical imaging.
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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 | 0.001 | 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".