Radar near-field sensing using metasurface for biomedical applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Metasurfaces, promising technology exemplified by their precise manipulation of incident wave properties and exquisite control over electromagnetic field propagation, offer unparalleled benefits when integrated into radar systems, providing higher resolution and increased sensitivity. Here, we introduce a metasurface-enhanced millimeter-wave radar system for advanced near-field bio-sensing, underscoring its adaptability to the skin-device interface, and heightened diagnostic precision in non-invasive healthcare monitoring. The low-profile planar metasurface, featuring a phase-synthesized array for near-field impedance matching, integrates with radar antennas to concentrate absorbed power density within the skin medium while simultaneously improving the received power level, thereby enhancing sensor signal-to-noise ratio. Measurement verification employs a phantom with material properties resembling human skin within the radar frequency range of 58 to 63 GHz. Results demonstrate a notable increase of over 11 dB in near-field Poynting power density within the phantom model, while radar signal processing analysis indicates a commensurate improvement in signal-to-noise ratio, thus facilitating enhanced sensing in biomedical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it