Experimental Method for Measurements of Time-resolved Reflectance in Scattering Media
Bibliographic record
Abstract
When a pulse of light propagates through scattering media, the temporal information of the light carries can be used to extract the optical properties of the media.To achieve this goal, we used a supercontinuum laser source with a timecorrelated single-photon counting (TCSPC) based coupled to a single-photon avalanche diode (SPAD) to measure the time-resolved reflectance signals using fibers.This thesis focuses on building an optimal set-up for the measurement by measuring the instrument response function (IRF) and the distribution for photon time of flight (DTOF).To find the optimal set-up, which has the IRF that is consistent with the manufacture and show the difference of DTOF of medium with different optical properties, we not only collected the data from different detectors but also collected it from set-up with different optical configurations and with different fiber probes.Finally, we measured signals from solid tissue phantoms and deduced its optical properties of it using diffusion theory.Preliminary results indicate that data are well modeled by theory.In the future, this system can be used to measure signals from real biological tissues and extract their optical properties.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".