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Record W2892415914 · doi:10.1088/2040-8986/aae3fe

Continuously tunable orthogonally polarized RF optical single sideband generator based on micro-ring resonators

2018· article· en· W2892415914 on OpenAlexafffund
Xingyuan Xu, Jiayang Wu, Linnan Jia, Mengxi Tan, Thach G. Nguyen, Brent E. Little, Roberto Morandotti, Arnan Mitchell, David Moss

Bibliographic record

VenueJournal of Optics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada1000 Talents Sichuan Program
KeywordsCompatible sideband transmissionSidebandOpticsResonatorRadio frequencyOptical Carrier transmission ratesMaterials sciencePolarization (electrochemistry)OptoelectronicsSIGNAL (programming language)PhotonicsWidebandBrewster's anglePhysicsRadio over fiberTelecommunicationsOptical fiberComputer science

Abstract

fetched live from OpenAlex

Abstract We demonstrate a continuously RF tunable orthogonally polarized optical single sideband (OP-OSSB) generator based on dual cascaded micro-ring resonators (MRRs). By splitting the input double sideband signal into an orthogonally polarized carrier and lower sideband via TE- and TM-polarized MRRs, an OP-OSSB signal is generated. A large tuning range of the optical carrier to sideband ratio of up to 57.3 dB is achieved by adjusting the polarization angle of the input light. The operation RF frequency of the OP-OSSB generator can be continuously tuned with a 21.4 GHz range via independent thermal control of the two MRRs. Our device represents a competitive approach towards OP-OSSB generation with wideband tunable RF operation, and is promising for photonic RF signal transmission and processing in radar and communication systems.

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.244
Teacher spread0.231 · 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

Citations400
Published2018
Admission routes2
Has abstractyes

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Same venueJournal of OpticsSame topicAdvanced Fiber Laser TechnologiesFrench-language works237,207