Compensating for Tissue Changes in an Ultrasonic Power Link for Implanted Medical Devices
Bibliographic record
Abstract
Ultrasonic power transfer using piezoelectric devices is a promising wireless power transfer technology for biomedical implants. However, for sub-dermal implants where the separation between the transmitter and receiver is on the order of several acoustic wavelengths, the ultrasonic power transfer efficiency (PTE) is highly sensitive to the distance between the transmitter and receiver. This sensitivity can cause large swings in efficiency and presents a serious limitation on battery life and overall performance. A practical ultrasonic transcutaneous energy transfer (UTET) system design must accommodate different implant depths and unpredictable acoustic changes caused by tissue growth, hydration, ambient temperature, and movement. This paper describes a method used to compensate for acoustic separation distance by varying the transmit (Tx) frequency in a UTET system. In a benchtop UTET system we experimentally show that without compensation, power transfer efficiency can range from 9% to 25% as a 5 mm porcine tissue sample is manipulated to simulate in situ implant conditions. Using an active frequency compensation method, we show that the power transfer efficiency can be kept uniformly high, ranging from 20% to 27%. The frequency compensation strategy we propose is low-power, non-invasive, and uses only transmit-side measurements, making it suitable for active implanted medical device 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 | 0.001 |
| 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".