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Record W2132480743 · doi:10.1109/tmtt.2012.2189231

A Wideband Frequency Tunable Optoelectronic Oscillator Incorporating a Tunable Microwave Photonic Filter Based on Phase-Modulation to Intensity-Modulation Conversion Using a Phase-Shifted Fiber Bragg Grating

2012· article· en· W2132480743 on OpenAlexaff
Wangzhe Li, Jianping Yao

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceFiber Bragg gratingOptoelectronicsWidebandPhase noiseOpticsPhase modulationModulation (music)MicrowaveOptical filterFrequency modulationPhotonicsFrequency multiplierWavelengthRadio frequencyPhysicsTelecommunications

Abstract

fetched live from OpenAlex

An optically tunable optoelectronic oscillator (OEO) with a wide frequency tunable range incorporating a tunable microwave photonic filter implemented based on phase-modulation to intensity-modulation conversion using a phase-shifted fiber Bragg grating (PS-FBG) is proposed and experimentally demonstrated. The PS-FBG in conjunction with two optical phase modulators in the OEO loop form a high-Q, wideband and frequency-tunable microwave photonic bandpass filter, to achieve simultaneously single-frequency selection and frequency tuning. Since the tuning of the microwave filter is achieved by tuning the wavelength of the incident light wave, the tunability can be easily realized at a high speed. A theoretical analysis is performed, which is verified by an experiment. A microwave signal with a frequency tunable from 3 GHz to 28 GHz is generated. To the best of our knowledge, this is the widest frequency tunable range ever achieved by an OEO. The phase noise performance of the OEO is also investigated.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.019
GPT teacher head0.275
Teacher spread0.257 · 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

Citations206
Published2012
Admission routes1
Has abstractyes

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