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Record W2085505985 · doi:10.1364/ol.38.004256

Photonic generation of pulsed microwave signals with tunable frequency and phase based on spectral-shaping and frequency-to-time mapping

2013· article· en· W2085505985 on OpenAlexaff
Fangzheng Zhang, Xiaozhong Ge, Shilong Pan, Jianping Yao

Bibliographic record

VenueOptics Letters · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOpticsMicrowavePolarizerOptical filterMaterials sciencePulse shapingSIGNAL (programming language)Polarization (electrochemistry)Group delay and phase delayPhotonicsPhase modulationPulse compressionOptoelectronicsDispersion (optics)Optical Carrier transmission ratesFilter (signal processing)PhysicsPhase noiseOptical fiberBirefringenceTelecommunicationsLaserRadio over fiberComputer science

Abstract

fetched live from OpenAlex

A scheme for photonic generation of pulsed microwave signals with tunable frequency and phase based on optical spectral-shaping and frequency-to-time mapping is proposed and experimentally demonstrated. The spectral shaping is realized by a tunable optical comb filter consisting of a differential group delay (DGD) element, a polarization modulator (PolM), and a polarizer. By passing a short optical pulse through the tunable comb filter and a dispersive element (DE), a pulsed microwave signal is generated after optical-to-electrical conversion. The phase of the generated microwave signal can be continuously tuned by tuning the voltage applied to the PolM. The frequency of the microwave signal can be tuned by changing the DGD and/or the dispersion of the DE. An experiment is performed. The generation of a pulsed microwave signal with tunable frequency and phase 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)0.000
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 score0.001
Threshold uncertainty score0.003

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.225
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 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

Citations27
Published2013
Admission routes1
Has abstractyes

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