High-speed narrow linewidth All-PM dispersive swept source for real-time FBG sensor interrogation
Bibliographic record
Abstract
We present an environmentally stable, all-polarization-maintaining (PM) dispersion-based swept source system optimized for narrow linewidth and high-speed wavelength scanning, specifically tailored for fiber Bragg grating (FBG) sensor interrogation. The system integrates a chirped fiber Bragg grating (CFBG) as a highly dispersive element to balance scan speed and spectral resolution, enabling a compact cavity design and stable lasing. The laser achieves precise wavelength tuning over a 40 nm range in the telecom C-band, linewidth around 12 MHz, and an impressive scanning speed exceeding 3 MHz with stable output power of over 20 mW, outperforming mechanically tuned lasers. The all-PM design ensures robustness against environmental perturbations. We validate the performance through static and dynamic characterization, including linewidth narrowing via the spatial hole burning effect. Furthermore, we demonstrate its application in the interrogation of FBG sensor arrays in wavelength division multiplexing (WDM), time division multiplexing (TDM), and hybrid modes, as well as real-time dynamic strain sensing up to 1kHz. These results position the system as a scalable and robust solution for applications requiring rapid spectral acquisition and fine resolution, such as border security, biomedical imaging, and structural health monitoring.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".