The design of ultrasonic lead magnesium niobate-lead titanate composite transducers for power and signal delivery to implanted hearing aids
Bibliographic record
Abstract
We present a system for efficiently powering implanted hearing aids by transmitting an ultrasonic signal across the skin. The use of ultrasound as method for power and signal transfer is known for embedded systems in industrial applications and has more recently been investigated for use with other medical implants. In our application, ultrasonic transducers are investigated as they offer substantially reduced size relative to traditional magnetic induction coil power delivery. The developed transducers use lead magnesium niobate-lead titanate (PMN-PT) piezoelectric material in a 1–3 composite formulation. PMN-PT offers an electromechanical coupling factor (kt, an indicator of maximum efficiency) that is up to 60% greater than traditional piezoceramics, while the use of composite transducers removes geometric constraints that can limit the achieved efficiency. The fabrication methods for the transducers are detailed. Experimental results are presented to show the composite transducers achieve a kt of 0.86 (out of 1.00), and a power transmission efficiency that is improved by 38% relative to a similar non-composite transducers. It is also demonstrated that these transducers offer sufficient bandwidth for amplitude or frequency modulation schemes to transmit data signals along the power carrier beam.
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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 | 0.001 | 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".