High speed short wave infrared (SWIR) imaging and range gating cameras
Bibliographic record
Abstract
Imaging in the Short Wave Infrared (SWIR) provides unique surveillance capabilities, both with passive illumination from the night glow in the atmosphere or with active illumination from covert LED or eye-safe lasers. Spectral effects specific to the 0.9 to 1.7 um wavelength range reveal camouflage and chemical signatures of ordinance. The longer wavelength range also improves image resolution over visible cameras in foggy or dusty environments. Increased military interest in cameras that image all laser range finders and target designators on the battlefield has driven development of a new class of uncooled InGaAs cameras with higher resolution and larger field of view than previously available. Current and upcoming needs include: imaging in all lighting conditions, from direct sunlight to partial starlight while using minimal power; range gating the camera to image through obscurants or beyond unimportant objects; and high speed capture of muzzle flare, projectile tracking, guide star and communications laser-beam tracking and wavefront correction. This paper will present images from new COTS cameras now available to address these needs and discuss the technology roadmap for further improvements.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.009 | 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".