A Compact Tissue-Insensitive Ultra-Wideband Implantable Antenna for Wireless Power Transfer in Implantable Medical Devices
Bibliographic record
Abstract
This work presents the development of a new, compact, two-port ultra-wideband implantable antenna with low sensitivity to implanting tissue and depth for energy harvesting applications in implantable medical devices (IMD). The proposed antenna features two compact radiating elements, operating at a center frequency of 2.45 GHz and occupying a very compact volume of 22.1 mm (7.25 × 6 × 0.508 mm). Miniaturization techniques such as meandered line slots, Defected Ground Structure (DGS), and shorting via were utilized to achieve this compactness. The stable performance of the antenna versus the implantation depth is established numerically and experimentally by considering both shallow and deep implantation depths. The antenna was implanted in a meat phantom and the results revealed wideband performance with measured bandwidths of 80.5% (1.38 3.24 GHz) and 32.6% (2.08 2.89 GHz) for Port-1 and Port-2, respectively. This ultra-wideband performance is shown to be effective in reducing the antenna’s sensitivity to the different types and depths of human biological tissues. Finally, the performance of the proposed implantable antenna has been successfully examined as part of a complete Wireless Power Transfer (WPT) system developed to improve Power Transfer Efficiency (PTE). The results show the superiority of the proposed two-port implantable antenna in improving PTE compared to the typical single-port implantable antennas.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".