Flexible, Polarization-Diverse UWB Antennas for Implantable Neural Recording Systems
Bibliographic record
Abstract
Implanted antennas for implant-to-air data communications must be composed of material compatible with biological tissues. We design single and dual-polarization antennas for wireless ultra-wideband neural recording systems using an inhomogeneous multi-layer model of the human head. Antennas made from flexible materials are more easily adapted to implantation; we investigate both flexible and rigid materials and examine performance trade-offs. The proposed antennas are designed to operate in a frequency range of 2-11 GHz (having S11 below -10 dB) covering both the 2.45 GHz (ISM) band and the 3.1-10.6 GHz UWB band. Measurements confirm simulation results showing flexible antennas have little performance degradation due to bending effects (in terms of impedance matching). Our miniaturized flexible antennas are 12 mm×12 mm and 10 mm×9 mm for single- and dual-polarizations, respectively. Finally, a comparison is made of four implantable antennas covering the 2-11 GHz range: 1) rigid, single polarization, 2) rigid, dual polarization, 3) flexible, single polarization and 4) flexible, dual polarization. In all cases a rigid antenna is used outside the body, with an appropriate polarization. Several advantages were confirmed for dual polarization antennas: 1) smaller size, 2) lower sensitivity to angular misalignments, and 3) higher fidelity.
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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 | 0.000 |
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".