Dielectric Lens-Based Millimeter Wave Imaging for Concealed Object Detection in Security Applications
Bibliographic record
Abstract
To improve throughput in security inspection procedures, a millimeter-wave (mmW) imaging system with a high-throughput operation with reasonable resolution compared to conventional mmW imaging systems is developed. Investigates the distinctive attributes of mmW, including its safe penetration through clothing, the study demonstrates the generation of detailed two-dimensional reconstructions of objects. Through the strategic use of a lens, signal amplitudes and phases are effectively captured, yielding reconstruction images from the signal reflected from the target. Experimental validations further affirm the effectiveness of mmW imaging with a dielectric lens, showcasing successful reconstructions of targets positioned at the lens’s front focal plane. Notably, the approach exhibits proficiency in discerning objects obscured behind non-metallic materials such as paper and cloth. These findings highlight the potential of utilizing Fourier transform analysis and a dielectric lens in mmW imaging, presenting a promising approach for security applications, particularly in the detection of concealed objects.
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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 | 0.001 |
| 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".