Metasurface Design for Millimeter-Wave Radar Biomedical Sensing
Bibliographic record
Abstract
Metasurfaces, with their precise control over electromagnetic waves, significantly enhance resolution and sensitivity when integrated into millimeter (mm)-wave radar systems. This paper presents the latest developments in metasurface technology for biomedical sensing, featuring an adaptable, compact, near-field-focused transmissive array designed for air-skin interface matching. Constructed using frequency-selective surface theory, the metasurface comprises layers of phase-synthesized unitcells and seamlessly integrates with commercially available radar operating within the$\mathbf{5 8}-\mathbf{6 3 G H z}$frequency band. The evolution of this system is depicted, starting with a single-focus metasurface and advancing to dual-focus configurations that overcome the inherent limitations of the previous design. Furthermore, this approach is extended to multi-focus sensor architectures, where data fusion from multiple radars enhances power delivery to the skin at various depths and locations. The design effectively concentrates absorbed power density within the skin, while radar signal processing analysis demonstrates a corresponding improvement in the signal-to-noise ratio.
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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 | 0.000 | 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".