A design of dual band wearable MIMO antenna using Organza fabric for medical applications
Bibliographic record
Abstract
A novel wearable dual-band MIMO antenna featuring good conformal characteristics under bending conditions is presented. The dielectric part of the antenna is built on a flexible substrate using layered lightweight plain weave fabric Organza with a relative permittivity of 1.63 to achieve a low-light and flabby textile. A defected ground structure (DGS) is employed for improving diverse parameters such as narrow bandwidth and resonant adjusting. The DGS part of the antenna is a resonant slot in the ground layer, placed directly under the patches. The electromagnetic energy is transmitted to the radiating slots using a recessed microstrip line feed along with semi co-planar waveguide (CPW) structure with defective ground metal in the bottom layer. It is worthwhile to point out that using the highly flexible Organza fabric with the ability to change the substrate thickness provided acceptable antenna characteristics under the bending condition in different directions. To validate the design, a prototype antenna is fabricated and experimentally investigated. The effect of substrate thickness on antenna performance is investigated by changing the number of Organza layers in both simulation and experiments. A good agreement is attained between the simulation results and the measurement results. The antenna properties such as scattering parameters, radiation patterns, and SAR values are investigated under bending conditions. Results show that the maximum measured gain is 7.8 dB at 2.4 GHz and 7.3 dB at 5 GHz. The simulated specific absorption rate (SAR) values are less than 0.336 W/Kg and 0.468 W/Kg at 2.4 GHz and 5 GHz for 1 g human body tissue, respectively. The SAR values for the bent antenna are less than 0.302 W/Kg and 0.458 W/Kg at 2.4 GHz and 5 GHz for 1 g human body tissue, respectively.
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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 | 0.001 |
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".