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Record W1970631186 · doi:10.1109/2944.962266

Novel multimode fiber for narrow-band Bragg gratings

2001· article· en· W1970631186 on OpenAlexaff
Thomas Szkopek, V. Pasupathy, J. E. Sipe, P. W. Smith

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2001
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFiber Bragg gratingMulti-mode optical fiberOpticsGraded-index fiberNormalized frequency (unit)Long-period fiber gratingPhysicsMaterials sciencePolarization-maintaining optical fiberModal dispersionDispersion-shifted fiberOptical fiberPlastic optical fiberFiber optic sensor

Abstract

fetched live from OpenAlex

We propose a novel multimode fiber structure with modal propagation characteristics tailored to facilitate the creation of narrow-band high-reflectivity fiber Bragg gratings. The fiber structure proposed consists of concentric cylindrical shells of higher and lower refractive index material. A full vector second-order finite-element method is used to analyze the proposed multimode fiber structure. Simulations of the modal profiles show that high-order modes are localized to particular high-refractive index shells. We present the theoretical characterization of the modal propagation constant as a function of inner shell radius, shell separation, and harmonic-mode parameter. It is shown that a fiber with a minimum inner shell radius of at least 25/spl lambda/ (where /spl lambda/ is the vacuum wavelength), and a minimum shell separation of at least 10/spl lambda/ provides a reasonable tradeoff between fiber size and grating performance. A simulation of the multimode fiber grating shows that a grating with a full-width at half-maximum bandwidth on the order of 10/sup -4//spl lambda/ is theoretically possible, if optical power is launched strictly into modes with angular harmonic parameter p=1.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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

Citations21
Published2001
Admission routes1
Has abstractyes

Explore more

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