Improvement of sensitivity of acousto-optical imaging using a powerful long pulse laser
Bibliographic record
Abstract
Acousto-optical imaging is based on the detection of strongly scattered light which is in part modulated by its interaction with an ultrasonic wave. This method benefits from the acoustic uniformity (low acoustic scattering and absorption) of an optically diffusive medium and the spectrally selective absorption of photons. In this work, we consider the use a pulsed single-frequency laser to increase the instantaneous optical power applied to the diffusive medium while maintaining the average power below the maximum permissible exposure. Such a laser source concentrates the illumination of the diffusive medium during the transit time of the ultrasonic toneburst. This allows collecting more ultrasound-modulated photons for a given ultrasonic wave amplitude. We found, however, that a pulsed laser of this kind generates additional noise which limits the sensitivity gain expected from its high peak power. Progress toward sensitive imaging was achieved by developing methods to reduce the impact of this additional noise. Results obtained with differential detection, laser beam spatio-temporal homogenization and variable delay synchronization are presented. With such measures, the use of a pulsed laser appears a promising solution for enhancing the sensitivity in acousto-optical imaging.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".