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 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 | 0.001 |
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 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".