Ultrafast and Ultrahigh-Resolution Interrogation of a Fiber Bragg Grating Sensor Based on Interferometric Temporal Spectroscopy
Bibliographic record
Abstract
A novel technique to achieve ultrafast and ultra-high-resolution interrogation of a fiber Bragg grating (FBG) sensor based on interferometric temporal spectroscopy is proposed and experimentally demonstrated. In the proposed system, two FBGs with one serving as the sensor grating and the other serving as the reference grating are connected at two arms of an interferometer. An ultrashort optical pulse from a pulsed laser is sent to the interferometer. Two pulses will be obtained due to the reflection of the two FBGs and then both are sent to a dispersive element to map the sensor grating wavelength shift to a temporal spacing change between the two dispersed pulses due to the dispersion-induced wavelength-to-time mapping. A temporal interference pattern is generated between the temporal pulses. The temporal spacing change is further mapped to the interference pattern frequency change, leading to a greatly improved interrogation resolution due to the inherently high sensitivity of a temporal interferometer. The proposed technique overcomes the fundamental tradeoff between the interrogation speed and resolution in a temporal-spectroscopy-based FBG interrogation system and that between the measurement resolution and dynamic range in a dual-wavelength heterodyne-based interrogation system. An ultrafast real-time interrogation of an FBG strain sensor with a sampling rate of 48.6 MHz and an interrogation resolution as high as 0.61 pm are experimentally demonstrated.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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 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".