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Record W2884532098 · doi:10.3390/app8071135

Tunable and Switchable Erbium-Doped Fiber Laser Using a Multimode-Fiber Based Filter

2018· article· en· W2884532098 on OpenAlexafffund
Anum Khattak, Gerard Tatel, Li Wei

Bibliographic record

VenueApplied Sciences · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of WaterlooWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePolarization controllerPolarization-maintaining optical fiberOpticsDispersion-shifted fiberComb filterMulti-mode optical fiberFiber laserPlastic optical fiberOptoelectronicsPhotonic-crystal fiberFiber optic sensorOptical fiberWavelengthFiberBandwidth (computing)Computer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

We demonstrated a compact tunable and switchable single/dual-wavelength erbium-doped fiber laser. The fiber laser can be tuned and switched from single-wavelength to dual-wavelength oscillation by using our recently proposed tunable comb filter. The comb filter consists of a section of multimode fiber (MMF) coiled into a polarization controller and two sections of single mode fibers (SMFs) to form a SMF/MMF/SMF structure, serving as a simple tunable all-fiber Mach-Zehnder interferometer. Due to the insertion of the MMF-based polarization controller (PC), an additional phase shift is introduced from the difference of the birefringence intensity in different dominant modes, which can be used to tune the fiber laser. In the experiment, by properly adjusting the PC, a tuning range of 9.3 nm can be achieved for the single-wavelength operation. Moreover, dual-wavelength operation with different free-spectral-ranges can be obtained. The tunable and switchable fiber lasers are of great importance for their applications in optical testing, optical fiber sensing, and signal processing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.249
Teacher spread0.223 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations47
Published2018
Admission routes2
Has abstractyes

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