A full-duplex wireless integrated transceiver for implant-to-air data communications
Bibliographic record
Abstract
This paper presents a novel fully integrated full-duplex data transceiver to support bidirectional high-data rate neural interfaces (electrical stimulation and neural recording). The transmitter (TX) and the receiver (RX) are designed to share a single implantable antenna. The TX generates IR-UWB based on edge combining, and the RX uses a novel ISM-2.4-GHz narrow-band OOK receiver. Separation between the TX and RX path is implemented by: 1) properly shaping the transmitted pulse, so its spectrum falls between 3.1 and 7 GHz UWB subband, and 2) carefully filtering the spectrum of the received waveform directly in the receiver low-noise amplifier (LNA), to avoid using a circulator or a diplexer (found in most full duplex systems). The receiver is designed to support downlink telemetry of neural stimulation applications with a data rate as high as 100 Mbps within a power budget of 5 mW. The transmitter is designed to support uplink back telemetry of neural recording applications with a data rate of up to 500 Mbps for power consumption of 5.4 mW and 10.8 mW for OOK and BPSK modulations, respectively (10.8 pJ/b). Measurement results obtained with biological tissues confirm full functionality of the fabricated full duplex transceiver. The total size of the chip is 0.8 mm2.
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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) | 0.005 | 0.003 |
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".