The effect of a metamaterial-based wearable microstrip patch antenna on the human body
Bibliographic record
Abstract
Body-worn communication devices have attracted much attention due to their wide applications. In this regard, various wearable antennas that have lots of advantages have been designed by researchers in recent years. Metamaterials that have negative refractive index can also be used in the wearable antenna designs because they have an ability to eliminate harmful health effects. In this study, a metamaterial-based wearable microstrip patch textile antenna operating in industrial, scientific, and medical bands was designed and simulated. The radiation values of this antenna were analyzed. Then metamaterial-based electromagnetic band gap (EBG) structure and wearable antenna integration was designed. Two different integrated antenna designs (3 × 3 and 5 × 3) were proposed. We obtained the performances of these antenna designs and calculated the specific absorption rate (SAR) values. We calculated the absorbed power 48.8, 0.167, and 0.0485 W/kg for 10 g tissue of wearable microstrip patch antenna, EBG integrated wearable antenna with 3 × 3 design, and EBG integrated wearable antenna with 5 × 3 design, respectively. Because the SAR values of the EBG integrated wearable antenna designs are well below the standard values, we can say that the designs proposed in the study can be used in many areas, such as military, health monitoring, and wireless communication.
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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 | 0.001 | 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".