Development of a Coded Aperture X-Ray Backscatter Imager for Explosive Device Detection
Bibliographic record
Abstract
Defence R&D Canada has an active research and development program on detection of explosive devices using nuclear methods. One system under development is a coded aperture-based X-ray backscatter imaging detector designed to provide sufficient speed, contrast and spatial resolution to detect antipersonnel landmines and improvised explosive devices. The successful development of a hand-held imaging detector requires, among other things, a light-weight, ruggedized detector with low power requirements, supplying high spatial resolution. The University of California, San Diego-designed HEXIS detector provides a modern, large area, high-temperature CZT imaging surface, robustly packaged in a light-weight housing with sound mechanical properties. Based on the potential for the HEXIS detector to be incorporated as the detection element of a hand-held imaging detector, the authors initiated a collaborative effort to demonstrate the capability of a coded aperture-based X-ray backscatter imaging detector. This paper will discuss the landmine and IED detection problem and review the coded aperture technique. Results from initial proof-of-principle experiments will then be reported.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".