A novel quasi-static channel enhancing technique for body channel communication
Bibliographic record
Abstract
Body channel communication (BCC) is a most power efficient way for communications among sensors in a wireless body-area network (WBAN). In BCC, the forward signal of the quasi-static field is conducted by the body surface, whereas the backward path is formed by the electrostatic coupling between the GND electrodes (GEs) of transmitter and receiver. As a result, the transmission loss is dominated by the backward path, which has high impedance due to small air capacitance between two compact GEs. Conventional backward path enhancement techniques make use of a large inductor to resonate with the air capacitance in order to reduce the impedance. Such approach is not suitable for integrated solution and not reconfigurable for varying communication distances. In this paper, we propose a novel active channel enhancer to compensate the loss in backward path, which is integratable and reconfigurable for variable distances and frequencies. Designed with 0.13 µm CMOS process, the proposed active enhancer improves the quasi-static coupling by more than 15 dB for a wide frequency band of 40 MHz–120 MHz compared to the 4 dB enhancement of conventional method; and the power consumption is only 0.6 mW.
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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 | 0.000 | 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".