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Record W2729081316 · doi:10.1364/oe.25.015828

Single-mode SOA-based 1kHz-linewidth dual-wavelength random fiber laser

2017· article· en· W2729081316 on OpenAlexafffund
Yanping Xu, Liang Zhang, Liang Chen, Xiaoyi Bao

Bibliographic record

VenueOptics Express · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsLaser linewidthFiber laserOpticsMaterials sciencePolarization-maintaining optical fiberDispersion-shifted fiberLaserFiber Bragg gratingDistributed feedback laserOptoelectronicsPhotonic-crystal fiberOptical fiberOptical amplifierFiber optic sensorPhysics

Abstract

fetched live from OpenAlex

Narrow-linewidth multi-wavelength fiber lasers are of significant interests for fiber-optic sensors, spectroscopy, optical communications, and microwave generation. A novel narrow-linewidth dual-wavelength random fiber laser with single-mode operation, based on the semiconductor optical amplifier (SOA) gain, is achieved in this work for the first time, to the best of our knowledge. A simplified theoretical model is established to characterize such kind of random fiber laser. The inhomogeneous gain in SOA mitigates the mode competition significantly and alleviates the laser instability, which are frequently encountered in multi-wavelength fiber lasers with Erbium-doped fiber gain. The enhanced random distributed feedback from a 5km non-uniform fiber provides coherent feedback, acting as mode selection element to ensure single-mode operation with narrow linewidth of ~1kHz. The laser noises are also comprehensively investigated and studied, showing the improvements of the proposed random fiber laser with suppressed intensity and frequency noises.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.258
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations75
Published2017
Admission routes2
Has abstractyes

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