Technical considerations for designing low-cost, long-wave infrared objectives
Bibliographic record
Abstract
With the growth of uncooled infrared imaging in the consumer market, the balance between cost implications and performance criteria in the objective lens must be examined carefully. The increased availability of consumer-grade, long-wave infrared cameras is related to a decrease in military usage but it is also due to the decreasing costs of the cameras themselves. This has also driven up demand for low-cost, long-wave objectives that can resolve smaller pixels while maintaining high performance. Smaller pixels are traditionally associated with high cost objectives because of higher resolution requirements but, with careful consideration of all the requirements and proper selection of materials, costs can be moderated. This paper examines the cost/performance trade-off implications associated with optical and mechanical requirements of long-wave infrared objectives. Optical performance, f-number, field of view, distortion, focus range and thermal range all affect the cost of the objective. Because raw lens material cost is often the most expensive item in the construction, selection of the material as well as the shape of the lens while maintaining acceptable performance and cost targets were explored. As a result of these considerations, a low-cost, lightweight, well-performing objective was successfully designed, manufactured and tested.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".