Fast fiber-optic tunable filter based on axial compression on a fiber Bragg grating
Bibliographic record
Abstract
We describe the design, fabrication, and performance of a fiber Bragg grating-based tunable optic filter. The filter, driven by two piezostacks, consists of a flexural hinge structure for displacement magnification and a fiber-ferrule assembly for axial compression of the fiber grating. Finite-element analysis was used to design the mechanical structure to achieve the required displacement magnification and the force for grating compression. A passive thermal compensation design was implemented to reduce thermal-induced wavelength drift. A feedback control system with a linear variable differential transformer was employed to control the displacement for accurate wavelength tuning and fine-tuning resolution. This tunable filter has achieved a closed-loop switching time of 17.3 ms, and a passive thermal compensation that reduced the thermal drift of the Bragg wavelength to 1.5 pm/C. The flexural-hinge structure that offers negligible backlash, noise-free motion, no need of lubricants, and no wear ensures its long-term reliability.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".