Measurement and performance evaluation of triple band differential integrated extraoral rectifying antenna for data transfer and RF energy harvesting for tongue drive system
Bibliographic record
Abstract
Wearable assistive devices are vitally important for tetraplegic individuals to provide valuable insights into their intended directives tailored to tongue motions in wireless healthcare industries. The flexible differentially driven extraoral antenna and rectenna measurement system are developed to enable differential sensing and monitoring of the set of unique tongue gestures for extraoral tongue drive system (eTDS) applications in three frequency bands of Industrial, Scientific, and Medical (ISM) (915.0 MHz, 2400 MHz, and 5800 MHz). The performance analysis is carried out using the heterogeneous human head model. The differential rectifier is coplanarly integrated with the differential extraoral antenna on the same 0.254 mm thin and 9.5 mm wider Rogers RT/ Duroid 6010 LM substrate. The footprint of the fabricated differential rectenna is 0.135 $$\:{\varvec{\lambda\:}}_{\varvec{g}}\times\:$$ 0.082 $$\:{\varvec{\lambda\:}}_{\varvec{g}}$$ $$\:\times\:$$ 0.002 $$\:{\varvec{\lambda\:}}_{\varvec{g}}$$ where the planar size of differential rectifier is 15.75 $$\:\:\times\:$$ 2.5 mm2. The fabricated systems are situated closely to an artificial head model. The maximum conversion efficiency achieved at 2400 MHz and 5800 MHz is 83.45% and 74.8%, respectively, for 11 dBm of RF input power. Further, the link analysis, including interfacing circuit losses, was carried out theoretically at 915 MHz. Thus, the proposed differential extraoral systems can be employed to acquire and transmit the user intentions in eTDS inspired healthcare applications.
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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 | 0.001 |
| 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 | 0.001 |
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".