Lock-in optical instrumentation for snapshot hyperspectral imaging
Bibliographic record
Abstract
Hyperspectral imaging (HSI) technology has become prominent, with a wide range of applications: food quality control, crop monitoring, and medical diagnostics. As HSI is able to capture spatial and spectral data, it is highly desirable, but highly complex. However, this functionality presents a challenge for data acquisition as three-dimensional HSI images must be acquired by an image sensor of one less dimension. Thus, HSI systems are often pushbroom systems, with twodimensional images being successively constructed over time from line scans. Additionally, HSI is expensive and difficult to operate. A snapshot HSI system is developed to address these challenges, whereby the additional image dimension is encoded onto an occupied dimension on the image sensor. Additionally, the snapshot HSI system is constructed from low cost, readily available components. The presented snapshot HSI system consists of a transparent diffraction optical disc bonded to an aperture mask, with alternating transparent and opaque regions, acting as an optical chopper when rotated by a DC brushless motor. This allows separation of the spectra of overlapped pixels on the HSI image sensor. When an incident beam passes through this optical chopper, many frequencies (corresponding to spatial channels) are imposed by the binary mask, while undergoing diffraction across the visible spectrum. Overlapped spectra are directed at a charge coupled device, where Fourier analyses distinguish each spatial channel. System geometry is used to transform the Fourier amplitude spectra into functions of wavelength for each spatial pixel. The design is experimentally validated through comparison to a commercially available spectrometer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".