275–305 GHz FM-CW Radar 3D Imaging for Walk-Through Security Body Scanner
Bibliographic record
Abstract
Imaging using millimeter waves (MMW) and terahertz (THz) waves can help inspect hazardous materials hidden beneath clothing in a non-contact and non-invasive manner. A 3D terahertz imaging system for security gate applications in the 275–305 GHz range was developed and experimentally demonstrated to detect concealed objects carried by pedestrians. This system performs 3D measurements by combining depth detection using frequency-modulated continuous wave (FM-CW) radar, vertical scanning of the detection spot using a 1D high-speed mechanical beam scanner, and horizontal movement of the irradiated area and detection spot as the pedestrian walks. The high-speed beam scanner comprises an F-Theta telecentric lens and a polygon mirror. It has a vertical line scan rate of 142 lines/s and spatial resolution of ~10 mm, consistent with the design value, and a depth resolution of ~7 mm, which is 40% larger than the theoretical value estimated from the FM-CW radar principle. The depth-dependent lateral distortion in 3D images, known as telecentricity, measured using the body scanner imaging system, was also evaluated. Consequently, images with the same magnification were obtained at a range of more than 500 mm of focus depth. Finally, the detection of concealed objects carried by pedestrians was demonstrated, showing that the system can work for a pedestrian walking at speeds from 4 km/h to 7 km/h.
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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 | 0.000 | 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".