Modular infrared 640 x 480 pixel camera core for rapid device integration
Bibliographic record
Abstract
In the observation and surveillance fields, there is an increasing demand for infrared modules that can be rapidly turned into a complete autonomous device whether it is for military, security or industrial purposes. Based on this concept, INO has developed a modular 16 bit infrared camera core. The tool can be used to provide a rapid evaluation of an application concept. Moreover, a complete device can be rapidly designed and build once the concept has been demonstrated. The IRXCore-640 camera core, integrating a 640 x 480 pixel uncooled FPA and providing a 16-bit raw signal output at 60 Hz, gives total access to the detector configuration parameters to ease developers integration process. TECless operation minimizes module size and power consumption. The camera core can be configured at the factory for outdoors operation from -30°C to +60°C with 200°C scene dynamic range at maximum sensitivity. The device can be used with refractive optics or catadioptric optical objectives. Windowing capability provides flexibility in frame frequency, sensitivity selection, and a choice of operating field of view. High resolution/high sensitivity can be achieved. In this paper, the camera core will be reviewed as well as its performances. The control software functionalities are detailed and some typical imaging examples will be 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.010 | 0.003 |
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".