Acousto-Optic Comb Interrogation System for Random Fiber Grating Sensors with Sub-nm Resolution
Bibliographic record
Abstract
The broad-frequency response and nanometer-range displacements of ultrasound detection are essential for the characterization of small cracks, structural health monitoring and non-destructive evaluation. Those perturbations are generated at sub-nano-strain to nano-strain levels. This corresponds to the sub-nm level and, therefore, to about 0.1% of wavelength change at 1550 nm, making it difficult to detect them by conventional interferometric techniques. In this paper, we propose a demodulation system to read the random fiber grating spectrum using a self-heterodyne acousto-optic frequency comb. The system uses a self-heterodyne approach to extract phase and amplitude modulated signals to detect surface acoustic waves with sub-nanometer amplitudes in the frequency domain. The method can detect acoustic frequencies of 1 MHz and the associated displacement. The system is calibrated via phase detection with a heterodyne interferometer, which has a limited frequency response of up to 200 kHz. The goal is to achieve sub-nanometer strain detection at MHz frequency with random fiber gratings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
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".