Realistic modeling of the biological channel for the design of implantable wireless UWB communication systems
Bibliographic record
Abstract
Several emerging medical applications require that a miniature data acquisition device be implanted into the head to extract and wirelessly communicate brain activity to other devices. Designing a reliable communication link for such an application requires a realistic model of the surrounding biological tissues. This paper exploits a realistic model of the biological channel to design a suitable wireless ultra wideband communication link in a brain monitoring application. Two scenarios for positioning the implanted transmitting antenna are considered. The 1(st) scenario places the antenna under the skull, whereas the 2(nd) scenario places the antenna under the skin, above the skull. The propagation characteristics of the signal through the tissues of the human head have been determined with full-wave electromagnetic simulation based on Finite Element Method. The implantable antenna and the external antenna are key components to establish an electromagnetic link between an implanted transmitter and an external receiver. The average specific absorption rate (ASAR) of the implantable antennas are evaluated and compared for the two proposed scenarios. Moreover, the maximum available power from the implanted antenna is evaluated to characterize the performance of the communication link established between the implantable antenna and the external antenna, with respect to spectrum and safety regulations. We show how sensitive the receiver must be in order to implement a reliable telemetry link based on the proposed model of the channel.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".