Novel lightweight uncooled thermal weapon sight
Bibliographic record
Abstract
INO in collaboration with DRDC Valcartier has been involved in the design and development of uncooled IR bolometric detector technology since the early 1990s for a broad range of military and commercial applications. From the beginning, the strategy has been to develop small-size bidimensional detector arrays and specialty linear arrays, both equipped with on-chip readout electronics. The detector arrays have been implemented in various instruments for both imaging and non-imaging applications. This paper describes two TWS1 and TWS2 prototypes of single band thermal weapon sights (TWS) making use of a novel catadioptric, i.e. refractive/reflective, optics and INO's miniature IR cameras. These cameras employ a 160x120 pixel uncooled bolometric FPA with a 52 µm pitch and NETD at 50 mK, and modular electronics consisting of three boards stacked together to fit into a 3-inch cube volume. The ultra lightweight catadioptric objective is inherently athermalized in the -30°C to +40°C range. The TWS1 is also equipped with a miniature RF link allowing bi-directional video transmission. This TWS1 weighs only 900 g and has a total volume of about 75 in3. Its power consumption is 2 W. The experimental performance showed that human detection, recognition and identification could be achieved at 800 m, 200 m, and 120 m, respectively. Construction of an improved TWS2 model is in progress. The objective is the reduction of TWS2 model weight down to 700 g, its volume down to 50 in3, replacing the RF video link with a wireless digital link, and increasing resolution to 320x240 pixels.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".