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Record W2039784081 · doi:10.1109/lpt.2014.2330758

Microwave Interferometric Technique for Characterizing Few Mode Fibers

2014· article· en· W2039784081 on OpenAlexafffund
Lixian Wang, Cang Jin, Younès Messaddeq, Sophie LaRochelle

Bibliographic record

VenueIEEE Photonics Technology Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsInterference (communication)InterferometryMicrowaveOptical fiberSIGNAL (programming language)FiberPhysicsMode (computer interface)PhotodetectorMaterials scienceComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

We propose and experimentally demonstrate a simple and accurate technique for measuring differential mode group delay (DMGD) in few mode fibers (FMF). A frequency-swept microwave signal is modulated on a filtered optical incoherent source. The microwave signals carried on different fiber modes experience different time delays and interfere with each other in the photodetector. Optical interference between propagating fiber modes is avoided by the use of an incoherent optical source. A mathematical model is established to analyze the interference pattern and extract the DMGD values. A 456-m two-mode fiber and a 981-m FMF, which supports four LP modes, are measured. The measurement covers the whole C-band and the results coincide well with those obtained by the time-of-flight method and the numerical simulations. A precision of ±0.002 ps/m is achieved.

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)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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

Citations21
Published2014
Admission routes2
Has abstractyes

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