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Record W2926332932 · doi:10.1109/jlt.2019.2909162

Integrated Differential Area Method for Variable Sensitivity Interrogation of Tilted Fiber Bragg Grating Sensors

2019· article· en· W2926332932 on OpenAlexafffund
Tingting Gang, Fu Liu, Manli Hu, Jacques Albert

Bibliographic record

VenueJournal of Lightwave Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCarleton University
FundersFundamental Research Funds for the Central UniversitiesChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsOpticsFiber Bragg gratingRefractive indexMaterials scienceRefractometryCladding modeGratingBandwidth (computing)Optical fiberFiber optic sensorOptoelectronicsPhysicsPolarization-maintaining optical fiber

Abstract

fetched live from OpenAlex

A novel demodulation method for tilted fiber Bragg grating (TFBG) refractometry is proposed and experimentally demonstrated. This “integrated differential area” method consists of calculating the difference between measured spectral transmission over a selected bandwidth and a reference spectrum and then integrating the absolute value of the result. This has the effect of multiplying the effect of the individual cladding mode resonance shifts relative to the background noise. The method further allows the use of spectrum analyzers with low resolution, including portable ones with spectral resolution of the order of 150-200 pm without loss of sensitivity. In practical measurements, a reflection configuration is demonstrated using a TFBG cascaded by a chirped-fiber Bragg grating with bandwidth of 19 nm. A refractive index sensitivity of 7.19% transmission change per refractive index unit is achieved for refractive indices ranging from 1.32 to 1.38, along with a limit of detection of 0.00026 refractive index units.

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 score0.259
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.243
Teacher spread0.233 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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