Nonlinear resolution enhancement of an FBG based temperature sensor using the Kerr effect
Bibliographic record
Abstract
We demonstrate the enhancement of the resolution of a fiber optical sensor using all-optical signal processing. By sweeping the frequency of a tunable laser across a fiber Bragg grating, a signal corresponding to the reflection spectrum of the FBG is generated. If another laser with fixed power and frequency is launched into a highly nonlinear fiber along with the FBG-shaped signal, the Kerr effect gives rise to a number of frequency sidebands, where the power in each of the sidebands is proportional an integer exponent of the signal and pump powers. By filtering out particular sidebands, this potentiation effect reduces the width of the FBG-shaped signal, making shifts in its central wavelength easier to distinguish. We report a maximum resolution enhancement factor of 3.35 obtained by extracting the n = -4 order sideband, and apply resolution enhancement to improve the resolution of an FBG based temperature sensor. The method described in this paper can be applied to existing fiber based sensors and optical systems to enhance their resolution.
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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 | 0.001 | 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".