Continuously Tunable Fractional Hilbert Transformer by Using a Single $\pi$-Phase Shifted FBG
Bibliographic record
Abstract
A continuously tunable fractional Hilbert transformer (FHT) using a π-phase shifted fiber Bragg grating ( π-PSFBG) is proposed and experimentally demonstrated. An FHT has an output that is a weighted sum of the original input signal and its classical Hilbert-transformed signal. The classical Hilbert transform is implemented using a π-PSFBG. The output from the classical HT and the original input signal are controlled to be orthogonally polarized. The combination of the two signals at a polarizer would generate a weighted sum with the weighting coefficients determined by the angle between the principle axis of the polarizer and the polarization direction of the original input signal. A π-PSFBG is fabricated. The performance of the π-PSFBG as a classical HT is evaluated. The incorporation of the π-PSFBG into the proposed system to implement an FHT is studied. A continuously tunable FHT with a tunable fractional order of ρ = 0.7, 0.86, 0.92, 1, 1.06, 1.17, and 1.24 to perform Hilbert transformation of a Gaussian pulse with a temporal width of 80 ps is experimentally demonstrated.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".