A Modified Wireless Power Transfer System for Medical Implants
Bibliographic record
Abstract
Wireless Power Transfer (WPT) is a promising technique, yet still an experimental solution, to replace batteries in existing implants and overcome the related health complications. However, not all techniques are adequate to meet the safety requirements of medical implants for patients. Ensuring a compromise between a small form factor and a high Power Transfer Efficiency (PTE) for transcutaneous applications still remains a challenge. In this work, we have used a resonant inductive coupling for WPT and a coil geometry optimization approach to address constraints related to maintaining a small form factor and the efficiency of power transfer. Thus, we propose a WPT system for medical implants operating at 13.56 MHz using high-efficiency Complementary Metal Oxide-Semiconductor (CMOS) components and an optimized Printed Circuit Coil (PCC). It is divided into two main circuits, a transmitter circuit located outside the human body and a receiver circuit implanted inside the body. The transmitter circuit was designed with an oscillator, driver and a Class-E power amplifier. Experimental results acquired in the air medium show that the proposed system reaches a power transfer efficiency of 75.1% for 0.5 cm and reaches 5 cm as a maximum transfer distance for 10.67% of the efficiency, all of which holds promise for implementing WPT for medical implants that don’t require further medical intervention, and without taking up a lot of space.
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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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".