Reflection imaging in the millimeter-wave range using a video-rate terahertz camera
Bibliographic record
Abstract
The ability of millimeter waves (1-10 mm, or 30-300 GHz) to penetrate through dense materials, such as leather, wool, wood and gyprock, and to also transmit over long distances due to low atmospheric absorption, makes them ideal for numerous applications, such as body scanning, building inspection and seeing in degraded visual environments. Current drawbacks of millimeter wave imaging systems are they use single detector or linear arrays that require scanning or the two dimensional arrays are bulky, often consisting of rather large antenna-couple focal plane arrays (FPAs). Previous work from INO has demonstrated the capability of its compact lightweight camera, based on a 384 x 288 microbolometer pixel FPA with custom optics for active video-rate imaging at wavelengths of 118 μm (2.54 THz), 432 μm (0.69 THz), 663 μm (0.45 THz), and 750 μm (0.4 THz). Most of the work focused on transmission imaging, as a first step, but some preliminary demonstrations of reflection imaging at these were also reported. In addition, previous work also showed that the broadband FPA remains sensitive to wavelengths at least up to 3.2 mm (94 GHz). The work presented here demonstrates the ability of the INO terahertz camera for reflection imaging at millimeter wavelengths. Snapshots taken at video rates of objects show the excellent quality of the images. In addition, a description of the imaging system that includes the terahertz camera and different millimeter sources is provided.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".