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Record W2146168806 · doi:10.1117/12.835133

Signal-to-noise ratio improvement in Brillouin sensing

2009· article· en· W2146168806 on OpenAlexaff
Nicolas Linze, Wenhai Li, Xiaoyi Bao

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBrillouin zoneBrillouin scatteringSignal-to-noise ratio (imaging)OpticsTime domainCoding (social sciences)Pulse (music)BroadbandPhysicsSIGNAL (programming language)Computer scienceOptical fiberMathematicsStatistics

Abstract

fetched live from OpenAlex

The signal-to-noise ratio (SNR) enhancement using a coded pulse in Brillouin Optical Time Domain Analysis (BOTDA) is studied theoretically and experimentally and compared with the one pulse case. This new technique is based on simplex coding and provides a theoretical SNR enhancement of 3.6 dB. We find that the experimental SNR improvement is around 3.8 dB, which is in good accordance with the theory. We also observe that the Differential Pulsewidth Pair - Brillouin Optical Time Domain Analysis (DPP-BOTDA) method can be applied. This allows to combine high spatial and frequency resolutions<sup>1</sup> and higher SNR.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.221
Teacher spread0.212 · 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
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

Citations6
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Fiber Optic SensorsFrench-language works237,207