MétaCan
Menu
Back to cohort
Record W2158861463 · doi:10.1109/jlt.2011.2123081

Real-Time Interrogation of a Linearly Chirped Fiber Bragg Grating Sensor Based on Chirped Pulse Compression With Improved Resolution and Signal-to-Noise Ratio

2011· article· en· W2158861463 on OpenAlexaff
Weilin Liu, Ming Li, Chao Wang, Jianping Yao

Bibliographic record

VenueJournal of Lightwave Technology · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFiber Bragg gratingOpticsWaveformInterferometryPulse compressionChirpMaterials scienceFree spectral rangeFiber optic sensorSIGNAL (programming language)Spectral resolutionNoise (video)PhysicsWavelengthOptical fiberLaserSpectral lineComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

A novel approach to interrogating in real time a linearly chirped fiber Bragg grating (LCFBG) sensor based on spectral-shaping and wavelength-to-time (SS-WTT) mapping with improved interrogation resolution and signal-to-noise (SNR) ratio is proposed and experimentally demonstrated. The proposed system consists of a mode-locked laser source, an optical interferometer incorporating an LCFBG, and a dispersive element. The optical interferometer has a spectral response with an increasing free spectral range (FSR). The incorporation of the LCFBG in the interferometer would encode the sensing information in the spectral response as a change in the FSR. After SS-WTT mapping, a linearly chirped microwave waveform is obtained. The correlation of the linearly chirped microwave waveform with a chirped reference waveform would provide a sharp correlation peak with its position indicating the wavelength shift of the LCFBG. A theoretical analysis is carried out, which is validated by a numerical simulation and an experiment. The experimental results show that the proposed system can provide an interrogation resolution as high as 0.25 με at a speed of 48.6 MHz.

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 score0.278
Threshold uncertainty score0.750

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 head0.009
GPT teacher head0.212
Teacher spread0.202 · 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

Citations45
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Lightwave TechnologySame topicAdvanced Fiber Optic SensorsFrench-language works237,207