Textile‐based high‐gain bow‐tie antenna using metamaterials for medical applications
Bibliographic record
Abstract
Abstract This paper presents a high‐gain wearable circularly polarized bow‐tie antenna (WCPBTA) with metamaterial unit‐cells and 5 pins (5Ps) technique for telemedicine applications such as telemonitoring the elderly or the patients especially for emergency conditions of contagious diseases. The frequency range of the proposed antenna is 5.725–5.850 GHz, which belongs to the unlicensed‐national information infrastructure (U‐NII‐3) for the industrial, scientific, and medical (ISM) sub‐channels. A light‐weight and flexible felt with dielectric constant ε r = 3, thickness 1.27 mm, and tan ( δ ) = 0.0095 is used as the substrate for patient comfort and wearability. The overall dimensions of the proposed antenna are 64 × 62 × 1.27 mm 3 or 0.102 λ g 3 at 5.8 GHz. The maximum simulated gain at 5.8 GHz is 8.25 dB, which is more than 4 dB compared to that of the original bow‐tie antenna. Besides, the axial ratio (AR) and the specific absorption rate (SAR) are also analyzed, which meet the perfect requirement for medical applications. The fabricated prototype of the antenna shows good compatibility between simulation and measurement results. These characteristics make the proposed WCPBTA a good choice for wireless body area networks (WBANs) in telemonitoring applications especially with the aim of preventing the spread of contagious diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".