All-optical enhancement of minimum detectable perturbation in intensity-based fiber sensors
Bibliographic record
Abstract
We present a novel optical signal processing scheme for enhancing the minimum detectable environmental perturbation of intensity-based fiber sensors. The light intensity is first stabilized by inducing a sinusoidal intensity modulation and extracting the first-order sideband generated by self-phase modulation (SPM) in a nonlinear medium. The light with stabilized intensity is then sent through a sensor and the sensor induced power variation is magnified by first inducing a sinusoidal intensity modulation, then undergoing SPM, and finally extracting a higher-order sideband. The advantage of the proposed stabilization-magnification (SM) sensing scheme is experimentally demonstrated by applying a damped vibration on an intensity-based fiber sensor and comparing the minimum detectable strain value of the proposed scheme with that of a conventional sensing scheme. Experimental results demonstrate minimum detectable strain improvement by a factor of 3.93. This new SM sensing scheme allows for the detection of perturbations originally too weak to be detected by a given intensity-based fiber sensor, which will be beneficial for a variety of applications such as high frequency ultra-sound detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".