Biotelemetry and Wireless Powering of Biomedical Implants Using a Rectifier Integrated Self-Diplexing Implantable Antenna
Bibliographic record
Abstract
This article proposes an efficient and complete wireless power transfer (WPT) system (WPTS) for multipurpose biomedical implants. The WPTS is composed of a self-diplexing implantable antenna, efficient rectifier, and WPT transmitter (WPT Tx). The proposed system is capable of simultaneously transmitting recorded data and recharging the batteries of the devices (so as to elongate the implant life). The WPT Tx occupies dimensions of 50 ×50 ×1.6 mm3and is optimized to effectively transfer power at 1470 MHz to a 55-mm deep implantable device. An efficient and compact ( 3.4 ×6.7 mm2) rectifier is used at 1470 MHz to convert the harvested RF power into a useful direct current (dc) power. The proposed rectifier circuit exhibits a high conversion efficiency of 50% even at an input power of -14 dBm and maximum efficiency of 76.1% at 2 dBm. The proposed self-diplexing implantable antenna occupies small dimensions (9.4 mm3) and operates at 915 and 1470 MHz by exciting ports 1 and 2, respectively. The biotelemetry operation is performed using a 915 MHz band (port 1), and the rectifier circuit is connected to port 2 (1470 MHz) to perform wireless powering. The simulated results are validated by examining the individual elements (WPT Tx, rectifier, and self-diplexing antenna) and overall WPTS in a saline solution and minced pork. The results prove that the proposed scheme is suitable for biotelemetry and wireless powering of biomedical implants.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".