An Optimized Feed Hexagonal Antenna with Defective Ground Plane for UWB Body Area Network Application
Bibliographic record
Abstract
Body area network has facilitated monitoring, authentication and security through sensors or microstrip antenna with specified frequency. The purpose of this research work is to propose a simplified way to search for an optimal length of the inset for edge feeding using the Evolutionary Algorithm search by minimizing the reflection coefficient using ANSYS HFSS. The optimal inset length resulted in an antenna with better radiation efficiency and wider bandwidth. The antenna structure is 70x70x1.6 mm3, with a modified ground. The purpose of this antenna is communication in Ultra-wideband and works in 5.4, 8.1, and 9.8 GHz bands respectively. The resonant bandwidth measured are 1.02, 0.28, 0.12 GHz, respectively. Simultaneously the achievable gains are 3.18, 7.81, and 19.95 dB, respectively in free space. As the antenna is of wearable type, the front-to-back ratio evaluated for each band is 2.31, 7.01, and 13.91 respectively. The results of the fabricated antenna agree with the simulated results. The specific absorption rates at resonant frequencies were observed to be 0.3, 0.56 and 0.24 W/kg respectively when antenna is placed on a human tissue model. The antenna is useful for on-body communication at ISM band, and high data rate off-body communication in body area networks.
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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".