Noise-equivalent power characterization of an uncooled microbolometer-based THz imaging camera
Bibliographic record
Abstract
A THz camera based on an uncooled microbolometer 160X120 pixel array with nominal pitch of 52 μm has been developed at INO and initial transmission and reflection images showed promise. In the present paper, the characterization of both standard infrared and THz-optimized uncooled microbolometer pixel arrays are presented at both infrared and THz wavelengths. Measurements in the THz region has been performed using non-uniform low-power quantum-cascade laser (QCL) and uniform high-power far-infrared laser (FIR laser) beams at 3 THz and 4.25 and 2.54 THz, respectively. A measurement comparison has been achieved in the infrared using a blackbody radiation. Different methods for noise-equivalent power (NEP) measurements have been investigated. These characterization methods are promising especially for non-uniform laser beams irradiated on pixel arrays. The NEP results obtained from the different methods are in good agreement independent of the method used in the experiments. The results show a high sensitivity of the THz-optimized pixel array in the THz region. Large beam area reflection imaging of obscured materials at 2.54 THz have been performed at video rates of 30 frames per second using the THz-optimized pixel array equipped with a semi-custom fast THz objective, proving that the INO THz camera provides a promising solution for stand-alone imaging systems.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".