3–10 GHz ultra wideband front‐end transceiver in 0.13 μm complementary metal oxide semiconductor for low‐power biomedical radar
Bibliographic record
Abstract
A new integrated low‐power, low‐complexity ultra wideband (UWB) transceiver front‐end in standard 130 nm complementary metal oxide semiconductor technology which can be used in UWB radar biomedical sensing applications is proposed in this study. The transceiver comprises of a full UWB band transmitter, an on‐chip diplexer and a full UWB band receiver front‐end. The transmitter generates Gaussian‐pulse‐modulated and rectangular‐pulse‐modulated signals at different carrier frequencies within the designated UWB by using a digitally controlled oscillator. The transmitter consumes an average power of 8 mW at a 10 MHz pulse rate. The on‐chip diplexer has a 1 dB insertion loss and an isolation of −30 dB. Its switch is co‐designed with the receiver's input matching network to optimise the power matching while achieving good noise performance. The receiver low noise amplifier has a 3–10 GHz input matching bandwidth with a power gain of 16 dB. The overall receiver front‐end consumes an average power of 12 mW. The core area of the transceiver circuit is 500 μm by 1100 μm. The experiments show that the proposed radar transceiver can successfully detect a human respiration pattern within 50 cm. This novel design using a DCO‐type UWB transceiver integrated with an on‐chip diplexer demonstrates the use of the low power UWB radar detection in biomedical applications.
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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) | 0.001 | 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".