Effect of time gating and polarization discrimination of propagating light in turbid media during angular domain imaging (ADI)
Bibliographic record
Abstract
Angular Domain Imaging (ADI) employs an angular filter array to accept photons within a small acceptance angle along the axis of an aligned laser light source and preferentially reject scattered light. Simulations show that the accepted photons travel the shortest paths between source and detector and are therefore the earliest to arrive. We fabricated angular filter arrays using silicon bulk micromachining and found that an array of 60 μm square shape microtunnels 1 cm in length accepted photons within 0.48 degree of axis of the micro-tunnels. This small acceptance angle rejected most of the scattered light and sub-millimeter resolution targets could be resolved in a few centimeters of turbid medium with at least six times reduced mean free path. ADI through media with higher scattering coefficients was not achievable due to unwanted acceptance of late arriving scattered photons. To reject the late arriving photons, we added time-domain filtration and linear polarization to ADI. The implementation of a time-gated camera, a 780 nm femtosecond pulsed laser, and linear polarization to our ADI system resulted in improved image contrast. The use of ADI with time-gating (gate width 250 ps) and linear polarization enabled visualization of sub-millimeter absorbing objects with approximately eight times higher image contrast compared to ADI in a scattering medium equivalent to six times reduced mean free path.
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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 | 0.000 | 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 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".