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Record W2166808484 · doi:10.1109/lpt.2003.814899

Direct design of multichannel fiber Bragg grating with discrete layer-peeling algorithm

2003· article· en· W2166808484 on OpenAlexaff
Hongpu Li, Yunlong Sheng

Bibliographic record

VenueIEEE Photonics Technology Letters · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsGratingFiber Bragg gratingOpticsDispersion (optics)Channel (broadcasting)Reflection (computer programming)Coupling (piping)Materials scienceDistortion (music)Channel spacingModulation (music)AlgorithmComputer sciencePhysicsTelecommunicationsOptical fiberBandwidth (computing)Wavelength-division multiplexingWavelengthAcoustics

Abstract

fetched live from OpenAlex

The conventional sampling approach fails when the seed grating contains phase shifts because this approach is based on the resonant coupling principle, which explains the multichannel spectrum formation but does not provide an accurate solution. We use the discrete layer-peeling algorithm for directly synthesizing the multichannel grating. The new method restores the channel distortion in the sampled grating, resulting in uniform reflection and dispersion spectrum channels. We introduce further an optimized set of channel phases, such that the maximum reflective index modulation required for N channels is only /spl radic/N times that for the single channel.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.462
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 integrity0.0000.000
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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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
GenreMethods

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

Citations28
Published2003
Admission routes1
Has abstractyes

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