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Record W1976086097 · doi:10.1117/12.567546

Bragg gratings in multimode fiber

2004· article· en· W1976086097 on OpenAlexaff
Honggang Yu, Chun Yang, Yong Wang, Jian Yang, Róbert Farkas, Chang‐Qing Xu

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMulti-mode optical fiberOpticsFiber Bragg gratingOffset (computer science)Materials scienceSpectral lineRefractive indexOptical fiberPolarization (electrochemistry)Single-mode optical fiberTransmission (telecommunications)Polarization-maintaining optical fiberOptoelectronicsPhysicsTelecommunicationsComputer scienceFiber optic sensor

Abstract

fetched live from OpenAlex

Recently, multimode fiber (MMF) and components based on MMF have attracted much attention due to their potential applications in future optical access networks. Fiber Bragg gratings (FBGs) are considered to be key components in both telecommunication and sensing applications. Although single-mode fiber based FBGs (SMFBG) have been studied thoroughly, few studies have been reported on MMFBGs, mainly because of the complexity and multiple mode nature of the MMF. In this paper, transmission and reflection spectra of MMFBGs are studied systematically. Relationships between transmission/reflection spectra and the excitation conditions are clearly demonstrated by observing the far-field pattern. Different launching methods including the lateral offset launching and angular offset launching and light sources with different spectrum width are used in experiments. Furthermore, the transmission/reflection spectra dependence on the polarization state of excitation light and asymmetric refractive index perturbation profile are studied in detail. Theoretical simulations are used to compare these experimental results.

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 categoriesMeta-epidemiology (narrow)
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 score0.365
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.225
Teacher spread0.216 · 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 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

Citations3
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Fiber Optic SensorsFrench-language works237,207