Development of a coded-aperture backscatter imager using the UC San Diego HEXIS detector
Bibliographic record
Abstract
Defence R&D Canada-Suffield and the University of California, San Diego, have recently begun a collaborative effort to develop a coded aperture based X-ray backscatter imaging detector that will provide sufficient speed, contrast and spatial resolution to detect antipersonnel landmines and improvised explosive devices. While our final objective is to field a hand-held detector, we have currently constrained ourselves to a design that can be fielded on a small robotic platform. Coded aperture imaging has been used by the observational X-ray and gamma ray astronomy community for a number of years, which has driven advances in detector design that is now being realized in systems that are substantially faster, cheaper and lighter than those only a decade ago. With these advances, a coded aperture hand-held imaging system has only recently become a possibility. One group at the Center for Astrophysics and Space Sciences, University of California, San Diego, has had a longterm programme developing the CZT based HEXIS detector as the detection element of a coded aperture imager. Designed as a satellite payload, this low-power system is ruggedized and light-weight, all necessary qualities for incorporation into the envisioned portable imaging system. This paper will begin with an introduction to the landmine and improvised explosive device detection problem, followed by a discussion of the HEXIS detector. We will then present early results from our proof-of-principle experiments, and conclude with a discussion on future work.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".