A Self-Duplexing Antenna For High-Performance Medical Implants
Bibliographic record
Abstract
Multi-tasking implantable antennas require additional multiplexer circuitry which makes the device bulky and more power consuming. Therefore, in this work, an implantable antenna with self-duplexing capabilities for use in biomedical applications is showcased. Both the 1.48 GHz Wireless Medical Telemetry Service (WMTS) spectrum and the 2.45 GHz Industrial, Scientific, and Medical (ISM) spectrum are intended for use by the antenna. It uses two ports in a total volume of 6.5 mm3: The resonance frequencies were tuned by using two shorting pins, one for each antenna. Implementing a superstrate layer to the patch prevents the radiating patch from coming into direct touch with bodily tissues. With the consistent skin layer framework, the antenna inside the body is simulated. The envisioned antenna has a gain of -27.02 dBi and -19.42 dBi at 1.48 GHz and 2.45 GHz, correspondingly. The antenna exhibits an impedance bandwidth of 250 MHz (233–258 MHz) at 2.45 GHz and 120 MHz (142–154 MHz) at 1.48 GHz with favorable radiation characteristics at both frequencies. Over a single gram of tissue, at 1.48 GHz and 2.45 GHz, respectively, our antenna assessed the Specific Absorption Rate (SAR), which came out to be 220 W/kg and 100 W/kg. The antenna satisfies IEEE specifications when it comes to SAR values, where given 10 g of tissue extend up to 40 W/kg at 1.48 GHz and 30 W/kg at 2.45 GHz. The suggested antenna is simulated at a skin depth of 110 × 110 ×50 mm3, Moreover, for biotelemetry, a wireless communication connection budget is computed at transmission of data pace of 7 and 100 kb/s for figuring out the range. Based on the predicted findings, the antenna seems like a great fit for implantable uses.
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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 | 0.001 | 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".