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Record W2030533103 · doi:10.1364/ao.45.003307

Full vectorial modal analysis of specialty fibers and their Bragg grating characterization

2006· article· en· W2030533103 on OpenAlexaff
Waleed S. Mohammed, Xijia Gu, P. W. Smith

Bibliographic record

VenueApplied Optics · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsToronto Metropolitan UniversityGenia Photonics (Canada)University of Toronto
Fundersnot available
KeywordsOpticsFiber Bragg gratingModalOptical fiberConcentricRefractive indexGraded-index fiberPhotonic-crystal fiberGratingRefractive index profileMaterials sciencePhysicsFiber optic sensorMathematics

Abstract

fetched live from OpenAlex

Optical fibers and specialty waveguides are the bases of the majority of today's telecommunication, biomedical, sensing, and light-delivery applications. Modal analysis plays an important role in optimizing the optical performance of these fibers when they are integrated with optical systems. We present a full vectorial modal theoretical analysis of specialty cylindrical symmetric fibers with arbitrary index profiles, using a staircase approximation and scattering matrix approach with no constraints on the refractive index profile. We demonstrate the generality of this method by investigating the modal characteristics of two specialty fibers: graded-index fiber and concentric-shell multicore fiber. The calculated modal effective indices for the graded-index fiber are compared with those calculated by the WKB method, stressing the main differences between the scalar and vectorial approaches. Using the same approach, we calculate the Bragg grating response of a holographic grating written in the guiding regions of a concentric-shell fiber and compared them with experimental measurements.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.602

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)0.0000.000

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 head0.005
GPT teacher head0.180
Teacher spread0.176 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2006
Admission routes1
Has abstractyes

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