Enhancement of optical penetration depth of LED-based NIRS systems by comparing different beam profiles
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Bibliographic record
Abstract
Abstract Near-Infrared Spectroscopy (NIRS) is a non-invasive brain imaging technique involving the quantification of oxy and deoxy-hemoglobin concentrations resolved from the measurement of Near-Infrared (NIR) light attenuation within the tissue. Previous studies have shown that NIR light is more influenced by the optical properties of the superficial layers than those of the deeper target layers such as cortex. NIR light produced by the Laser source penetrates deeper regions of the tissue rather than the LED source although Laser needs more expensive instrumentation. In this study, we investigate the effect of Uniform and Gaussian beam profiles on the enhancement of LED light penetration depth. The latter beam profiles were generated and compared using Flat and Aspherical lenses applied to the LED sources. In order to increase the signal to noise ratio, the lenses were also applied to the light detector. For performance analysis, two experiments were carried out by scanning the intra space of a liquid phantom by static and dynamic (pulsating) absorbers. Monte Carlo simulations were also carried out to be compared with the experiment. The results showed that Gaussian beam profile and in particular, Bi-Convex lenses applied to both source and detector leads to a greater light penetration depth in the liquid phantom close to that of a Laser source.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it