Multi-Radar Near-Field System Employing Multi-Band Non-Interleaved Metasurface for Enhanced Bio-Sensing
Bibliographic record
Abstract
Multi-radar sensing significantly enhances accuracy and sensitivity in non-invasive, real-time wearable health monitoring. This paper introduces a system that employs near-field focusing of radar interactions on human skin at various locations, with the multi-radar setup gathering data from multiple points to enhance detection capabilities. A multi-band non-interleaved metasurface is integrated into this system, improving the detection of physiological parameters, including the identification of human blood abnormalities. The methodology utilizes discrete zones of a transmissive metasurface, each composed of phase-synthesized arrays. These zones serve as low-profile impedance matching networks, optimized for specific frequencies within the 58 to 63 GHz millimeter-wave radar range, thereby increasing efficiency through higher absorbed power density at different frequencies, which also enables varied penetration depths into human skin. Analysis using a customized phantom, closely resembling human skin, demonstrates significant increases in near-field absorbed power density: over 11 dB at 60 GHz and 2 mm penetration depth, over 17 dB at 58 GHz, and 9 dB at 61 GHz at the same depth.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".