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Record W2144291530 · doi:10.1109/lpt.2013.2249058

Optical Vector Network Analyzer Based on Unbalanced Double-Sideband Modulation

2013· article· en· W2144291530 on OpenAlexaff
Muguang Wang, Jianping Yao

Bibliographic record

VenueIEEE Photonics Technology Letters · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSidebandFiber Bragg gratingOpticsModulation (music)Spectrum analyzerPhase modulationOptical powerFrequency modulationPhysicsCompatible sideband transmissionOptical modulation amplitudeOptical fiberRadio frequencyComputer scienceOptical amplifierTelecommunicationsPhase noiseAcousticsLaser

Abstract

fetched live from OpenAlex

An optical vector network analyzer (OVNA) based on unbalanced double-sideband (UB-DSB) modulation with improved measurement accuracy is proposed and experimentally demonstrated. It is different from an OVNA based on optical single-sideband (OSSB) modulation in which one-to-one mapping between the optical and radio frequency responses is employed to measure the magnitude and phase responses of an optical component, the proposed technique measures the magnitude and phase responses by taking into consideration of the power of the other sideband through solving two equations that are associated with the UB-DSB modulation, thus the errors due to the residual power of the other sideband in an OSSB modulation based approach are completely eliminated. A mathematical model providing the transfer function of an optical component is derived. The measurement of a phase-shifted fiber Bragg grating and a linearly chirped fiber Bragg grating is performed. Comparing with the measured results based on OSSB modulation, obvious improvement in measurement accuracy is demonstrated.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.217
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations64
Published2013
Admission routes1
Has abstractyes

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