Multichannel Optical Signal Processing in NRZ Systems Based on a Frequency-Doubling Optoelectronic Oscillator
Bibliographic record
Abstract
Optical signal processing, including clock recovery, nonreturn-to-zero (NRZ) to return-to-zero (RZ) or to carrier-suppressed return-to-zero (CSRZ) format conversions, serial-to-parallel conversion, and optical regeneration in NRZ systems using a frequency-doubling optoelectronic oscillator (OEO) is investigated. The key device in the OEO is a dual-output intensity modulator (IM), which is implemented using a polarization modulator (PolM). If a continuous-wave (CW) probe along with a multichannel NRZ signal is injected into the OEO, an electrical clock at half the data rate of the NRZ signal will be generated. When the electrical clock is fed back into the PolM-based IM, it will modulate the later injected CW probe and the NRZ signal, thus carving the NRZ signal to be an RZ/CSRZ signal at the same data rate or an RZ signal at half the data rate of the NRZ signal. Meanwhile, the CW probe is also carved to be an optical pulse train with a frequency equal to the data rate or half the data rate of the NRZ signal. Multichannel line-rate and prescaled clock recovery, NRZ-to-RZ/CSRZ conversion, 1:2 serial-to-parallel conversion, and synchronous-modulation-based regeneration are thus realized. An experiment is performed with all the aforementioned signal processing functions verified.
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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 | 0.000 |
| Open science | 0.000 | 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".