Narrow band SWIR hyperspectral imaging: a new approach based on volume Bragg grating
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Volume Bragg grating technology has enabled the development of a new type of staring hyperspectral camera. Based on Bragg Tunable filters, these hyperspectral cameras have both high spectral and spatial resolution, and significantly higher sensitivity than competing technologies like push broom spectrometer, liquid crystal tunable filters, or acousto-optic tunable filters. They are minimally sensitive to polarization and their spectral isolation can reach 10<sup>6</sup>. Here we thus present an innovative tool to collect SWIR hyperspectral data with high spectral and spatial resolution. This new instrument is based on a 3nm bandwidth Bragg Tunable Filter, continuously tunable from 1.0um and 2.5um. Because high spectral resolution also means less light per channel, a low noise custom HgCdTe (MCT) camera was also developed to meet the requirement of the filter. The high speed capability of more than 300 fps and the low operating temperature of 200K (deep cooled option to 77K) allow full frame 500 spectral channel datacube acquisitions in minimal time. Basic principle of this imaging filter will be reviewed as well as the custom MCT camera performances. High resolution hyperspectral measurements will be demonstrated between 1.0um and 2.5um on different objects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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