An Adaptive Dual-Mode HBC Transceiver for Medical and Entertainment Applications
Bibliographic record
Abstract
Human body communication (HBC) is a low-power communication method using the human body as a medium for wearable and implantable devices. Existing systems support either Carrier-Based HBC (CB-HBC) with carrier modulation for noise immunity or Carrierless HBC (CL-HBC) with carrierfree signaling for simplicity and higher data rates, leading to separate transceiver (TRX) designs and limited flexibility. This work presents a unified TRX in TSMC 65 nm silicon supporting both modes with real-time adaptability. Key features include: (1) a reconfigurable transmitter (TX) switching between CB-HBC across 4 carrier frequencies (up to 21 MHz) and CL-HBC, (2) an adaptive receiver (RX) with 4 digitally controlled bias levels for mode-specific gain-bandwidth tuning, and (3) programmable data rates up to 2 Mbps in CB-HBC, and 5.25 Mbps in CL-HBC. Measurement results show an energy efficiency of $25 \mathrm{pJ} / \mathrm{bit}$ (CB-HBC) and $8.4 \mathrm{pJ} /$ bit (CL-HBC), power consumption of $50 \mu \mathbf{W}$ and $44 \mu \mathbf{W}$ respectively, bit error rate (BER) $\lt10^{-4}$ at a carrier-to-data rate ratio $\lt14$ for CB-HBC, and sensitivity of $\mathbf{- 9 1 ~ d B m}$ in a $\mathbf{0. 1 2} \mathrm{mm}^{2}$ area.
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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 | 0.000 | 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".