Exceptional-point-enhanced sensing in an all-fiber bending sensor
Bibliographic record
Abstract
An exceptional-point (EP) enhanced fiber-optic bending sensor is reported. The sensor is implemented based on parity-time (PT)-symmetry using two coupled Fabry-Perot (FP) resonators consisting of three cascaded fiber Bragg gratings (FBGs) inscribed in an erbium-ytterbium co-doped fiber (EYDF). The EP is achieved by controlling the pumping power to manipulate the gain and loss of the gain and loss FP resonators. Once a bending force is applied to the gain FP resonator to make the operation of the system away from its EP, frequency splitting occurs, and the frequency spacing is a nonlinear function of the bending curvature, with an increased slope near the EP. Thus, by measuring the frequency spacing, the bending information is measured with increased sensitivity. To achieve high-speed and high-resolution interrogation, the optical spectral response of the sensor is converted to the microwave domain by implementing a dual-passband microwave-photonic filter (MPF), with the spacing between the two passbands equal to that of the frequency splitting. The proposed sensor is evaluated experimentally. A curvature sensing range from 0.28 to 2.74 m−1 is achieved with an accuracy of 7.56×10−4 m−1 and a sensitivity of 1.32 GHz/m−1, which is more than 4 times higher than those reported previously.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".