On the Design of Efficient and Accurate Arbitrary-Order Temporal Optical Integrators Using Fiber Bragg Gratings
Bibliographic record
Abstract
In this paper, we propose and numerically investigate a simple and practical all-fiber design for implementing first-order and higher order all-optical passive temporal integrators with optimized energetic efficiencies. The proposed solution is based on a high-reflectivity fiber Bragg grating (FBG) providing a reflection spectral response that approaches the frequency transfer function of a time-limitedNth-order optical integrator (N= 1, 2, 3 ...). A closed-form analytical expression has been derived for the frequency response to be targeted for implementing an optical integrator of any given integration order operating over a prescribed limited time window. The required grating profile can then be designed using a layer-peeling FBG synthesis algorithm. Our simulations show that for a sufficiently long FBG, a relatively smooth amplitude-only apodization profile is required for any desired integration order even when an FBG peak reflectivity > 99% is targeted. The resulting FBG integrators can provide at least a sixfold increase in energetic efficiency as compared with previously proposed FBG designs while offering a similar or superior performance in terms of processing accuracy. We estimate that ultrafast highly efficient arbitrary-order all-optical temporal integrators capable of accurate operation over nanosecond time windows could be implemented using readily feasible, centimeters-long FBGs.
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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 | 0.001 |
| 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 | 0.001 | 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".