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Record W2024444620 · doi:10.1364/oe.21.019097

VCSEL-powered and polarization-maintaining fiber-optic grating vector rotation sensor

2013· article· en· W2024444620 on OpenAlexaff
Tuan Guo, Fu Liu, Fa Du, Zhaochuan Zhang, Chunjie Li, Bai‐Ou Guan, Jacques Albert

Bibliographic record

VenueOptics Express · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCarleton University
Fundersnot available
KeywordsOpticsFiber Bragg gratingMaterials sciencePolarization-maintaining optical fiberFiber optic sensorFiber laserOrthogonal polarization spectral imagingOptical fiberOptoelectronicsLaserPhysics

Abstract

fetched live from OpenAlex

A compact fiber-optic vector rotation sensor in which a short section of polarization-maintaining (PM) fiber stub containing a straight fiber Bragg grating (FBG) is spliced to another single mode fiber without any lateral offset is proposed and experimentally demonstrated. Due to the intrinsic birefringence of the PM fiber, two well-defined resonances (i.e. orthogonally polarized FBG core modes) with wavelength separation of 0.5 nm have been achieved in reflection, and they exhibit a high sensitivity to fiber rotation. Both the orientation and the angle of rotation can be determined unambiguously via simple power detection of the relative amplitudes of the orthogonal core reflections. Meanwhile, instead of using a broadband source (BBS), the sensor is powered by a commercial vertical cavity surface emitting laser (VCSEL) with the laser wavelength matched to the PM-FBG core modes, which enables the sensor to work at much higher power levels (~15 dB better than BBS). This improves the signal-to-noise ratio considerably (~50 dB), and makes a demodulation filter unnecessary. Vector rotation measurement with a sensitivity of 0.09 dB/deg has been achieved via cost-effective single detector real time power measurement, and the unwanted power fluctuations and temperature perturbations can be effectively referenced out.

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 score0.517
Threshold uncertainty score0.996

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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