Flexible and Wearable Dual-Band Differential Extraoral Antenna for eTDS Applications
Bibliographic record
Abstract
Wearable assistive devices are essential for performance monitoring and inferring the tongue gestures of disabled individuals in medical rehabilitation. In this paper, a flexible and wearable differential extraoral antenna is proposed with dual resonances (915 MHz and 2.4 GHz) in the Industrial, Scientific, and Medical (ISM) band for extraoral tongue drive system (eTDS) applications. The size of the fabricated prototype of the extraoral differential antenna is (0.135${{\bm{\lambda }}}_{\bm{g}} \, \times\, $0.065${{\bm{\lambda }}}_{\bm{g}} \, \times \, $0.002${{\bm{\lambda }}}_{\bm{g}}$). The performance of the differential extraoral antenna is analysed using a realistic human head model. Further, the extraoral antenna is fabricated and experimentally validated its performance in the close vicinity of the artificial head model. The measured impedance bandwidth (≤ −10 dB) and peak gain values are 90 MHz and$ -$20.00 dBi, respectively, at 0.915 GHz; and 180 MHz and$ -\!$12.87 dBi, respectively, at 2.4 GHz. The specific absorption rate (SAR) parameter values have also been analyzed for both the resonating frequencies over 1 g and 10 g of mass tissue through simulations. Further, the link budget was theoretically calculated based on these acceptable SAR values. The proposed antenna could communicate effectively by eliminating balun's additional impedance mismatch loss due to direct interfacing. Thus, the proposed extraoral differential antenna can establish an effective communication link for data and power transfer in the eTDS technology-based 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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".