Novel Performance Evaluation of Thermal Camera Based on VOx Bolometer Focal Plane Array via Analysis of Sigma NETD, Mean NETD, and Roughness Index
Why this work is in the frame
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Bibliographic record
Abstract
roughness index (RI), noise equivalent temperature difference (NETD), full width at half maximum (FWHM), non-uniformity correction (NUC) With recent advancements in thermal imaging, the evaluation of thermal imaging performance has become important. In this study, the thermal-camera performance parameters of roughness index (RI), noise equivalent temperature difference (NETD), and the full width at half maximum (FWHM) of a statistical NETD histogram are investigated and compared by varying the integration times at different operating temperatures for vanadium oxide (VOx)based microbolometer focal plane arrays (FPAs) with the use of the Matlab algorithm platform. The quantitative performance assessment of an uncooled VOx microbolometer-based thermal imager, which was designed and fabricated by researchers from the National Chung-Shan Institute Science of Technology (NCSIST), Taiwan, and the National Optics Institute (INO), Canada, is proposed systematically. Explicitly, the uncompressed video data streams before non-uniformity correction (NUC) using two-point temperature calibration were acquired for integration times of 16.67, 33.33, and 50 ms at three operating temperatures of 10, 15, and 20 C. The results from the estimations of NETD, FWHM of the NETD histogram, and the RI for the thermal imager are discussed for the imaging performance evaluation in different infrared operation scenarios. We believe that our findings can significantly contribute to the further development of IR imaging technology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it