Real-Time Interrogation of a Linearly Chirped Fiber Bragg Grating Sensor Based on Chirped Pulse Compression With Improved Resolution and Signal-to-Noise Ratio
Bibliographic record
Abstract
A novel approach to interrogating in real time a linearly chirped fiber Bragg grating (LCFBG) sensor based on spectral-shaping and wavelength-to-time (SS-WTT) mapping with improved interrogation resolution and signal-to-noise (SNR) ratio is proposed and experimentally demonstrated. The proposed system consists of a mode-locked laser source, an optical interferometer incorporating an LCFBG, and a dispersive element. The optical interferometer has a spectral response with an increasing free spectral range (FSR). The incorporation of the LCFBG in the interferometer would encode the sensing information in the spectral response as a change in the FSR. After SS-WTT mapping, a linearly chirped microwave waveform is obtained. The correlation of the linearly chirped microwave waveform with a chirped reference waveform would provide a sharp correlation peak with its position indicating the wavelength shift of the LCFBG. A theoretical analysis is carried out, which is validated by a numerical simulation and an experiment. The experimental results show that the proposed system can provide an interrogation resolution as high as 0.25 με at a speed of 48.6 MHz.
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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 | 0.000 | 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 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".