Bibliographic record
Abstract
Tunable fiber optic filter has extensive applications in telecommunications, spectroscopy, and fiber optic sensing. Many research attempts have been devoted to develop a filter with a wide tuning range, a fast tuning speed, a fine tuning resolution, and high reliability. Despite of the progress made so far, a tunable fiber optic filter that combines all these qualities is still a subject of intensive research. This thesis describes the design, fabrication and test results of a high performance tunable fiber optic filter. The filter is piezo-driven using a flexural hinge structure for displacement magnification and an axial strain of a fiber Bragg grating. Finite element analysis was used to design the mechanical structure to achieve the required displacement magnification and reaction force for grating compression. A passive thermal compensation design was implemented with two spacers of different coefficients of thermal expansion to compensate the thermal-induced wavelength drift. A feedback control system with a linear variable differential transformer was employed to control the displacement and to achieve the designed tuning accuracy. A tuning range of 13.7 nm, a maximum closed loop switching time of 17.3 ms, and a wavelength drift of 1.4 pm/C were achieved. The flexural-hinge structure, that offers noise-free motion, no need of lubricants and no wear, ensures its long-term reliability.
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 | 0.000 |
| 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) | 0.006 | 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".