Multichannel Arbitrary-Order Photonic Temporal Differentiator for Wavelength-Division-Multiplexed Signal Processing Using a Single Fiber Bragg Grating
Bibliographic record
Abstract
A multichannel photonic temporal differentiator implemented based on a single multichannel fiber Bragg grating (FBG) for wavelength-division-multiplexed (WDM) signal processing is proposed for the first time to our knowledge. The multichannel FBG is designed using the discrete layer peeling (DLP) algorithm together with the spatial sampling technique. Specifically, the DLP algorithm is used to design the spectral response of an individual channel, while the spatial sampling is employed to generate a multichannel response. The key feature of the proposed temporal differentiator is that WDM signals at multiple optical wavelengths can be simultaneously processed. Two sampling techniques, the phase-only and the amplitude-only, are employed. The use of the phase-only sampling technique to design a 45-channel first-order and second-order temporal differentiator is performed, and the use of the amplitude sampling technique to design a 3-channel first-order and second-order temporal differentiator is also performed. A proof-of-concept experiment is then carried out. A 3-channel first-order differentiator with a bandwidth of 33.75 GHz and a channel spacing of 100 GHz is fabricated. The use of the fabricated 3-channel FBG to perform first-order temporal differentiation of a 13.2-GHz Gaussian-like optical pulse with different optical carrier wavelength is 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".