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Record W2164649195 · doi:10.1109/jlt.2003.819864

Study of optical pulses - Fiber gratings interaction by means of joint time-frequency signal representations

2003· article· en· W2164649195 on OpenAlexaff
José Azaña, Miguel A. Muriel

Bibliographic record

VenueJournal of Lightwave Technology · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsInstitut National de la Recherche Scientifique
FundersUniversidad Politécnica de Madrid
KeywordsFiber Bragg gratingOpticsGratingLong-period fiber gratingChirpPHOSFOSMaterials scienceBlazed gratingApodizationDiffraction gratingOptical fiberPolarization-maintaining optical fiberFiber optic sensorPhysicsLaser

Abstract

fetched live from OpenAlex

In this paper, we carry out a systematic study on the interaction between ultrashort optical pulses and fiber Bragg grating structures operating in the linear regime. Our study is based on the joint time-frequency representation (spectrogram) of the reflection impulse response from the grating structures under analysis. By means of such a representation we get to visualize in a single image all the relevant information concerning the optical and dispersive behavior of the grating structures and more importantly, we obtain information on the pulse-grating interaction process, which otherwise is not accessible from any other method. Here, we analyze uniform and nonuniform fiber Bragg gratings. The effects of the apodization of the coupling coefficient and chirp of the grating period on the macroscopic optical properties of the considered gratings are investigated. Furthermore, we extend our analysis to more complicated in-fiber grating structures such as concatenated gratings, Fabry-Perot-like grating structures, and superimposed gratings. The results of our study indicate that the time-frequency methods constitute a powerful tool for the analysis and design of fiber Bragg grating structures.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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 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

Citations12
Published2003
Admission routes1
Has abstractyes

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