1280 x 960 pixel microscanned infrared imaging module
Bibliographic record
Abstract
The needs of surveillance/detection operations in the infrared range, for industrial, spatial and military applications continuously tend toward larger field of view and resolution while maintaining the system as compact as possible. To answer this need, INO has developed a 1280x960 pixel thermal imager, said HRXCAM, with 22.6° field of view. This system consists in the assembly of a catadioptric optics with microscan mechanism and a detection electronic module based on a 640x480 25μm pitch pixel bolometric detector. The detection module, said IRXCAM, is a flexible platform developed for fast prototyping of varied systems thanks to its ability to support a large range of infrared detectors. With its multiple hardware and software functionalities, IRXCAM can also be used as the complete electronic module of a finalized system. HRXCAM is an example of fast prototyping with IRXCAM and an optical lens that fully demonstrates the imaging performance of the final system. HRXCAM provides 1280x960 pixel images at a nominal 5-15 Hz frequency with 60 mK NETD. It can also be used in the 640x480 mode at 58 Hz with the same sensitivity. In this paper, the catadioptric optics with integrated microscan and IRXCAM architecture and specifications are reviewed. Some typical examples of image obtained with HRXCAM in outdoor conditions are presented.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".