Simultaneous 1310/1550 dual-band swept laser source and fiber-based dual-band common-path swept source optical coherence tomography
Bibliographic record
Abstract
A simultaneous two wavelength band swept laser source and a fiber-based dual-band common-path swept source optical coherence tomography is reported. Simultaneous 1310/1550 dual-wavelength tuning is performed by using two fiber-ring cavities with corresponding optical semiconductor amplifier as their gain mediums and two narrowband optical filters with a single dual-window polygonal scanner. Measured average output powers of 60 mW and 27 mW have been achieved for 1310 and 1550 nm bands, respectively, while the two wavelengths were swept simultaneously from 1227 nm to 1387 nm for 1310 nm band and from 1519 nm to 1581 nm for 1550 nm band at an A-scan rate of 65 kHz. A broadband 1310/1550 wavelength-division multiplexing is used for coupling two wavelengths into a common-path single-mode GRIN-lensed fiber probe to form a dual-band common-path swept-source optical coherence tomography. Simultaneous OCT imaging at 1310 and 1550 nm is achieved by using a depth ratio correction method. This technique allows potentially for in vivo endoscopic high-speed functional OCT imaging with high quality spectroscopic contrast with low computational costs. On the other hand, the common path configuration is able to reject common mode noise and potentially implement high stability quantitative phase measurements.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".