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Record W3102693371 · doi:10.1080/23746149.2020.1838946

RF and microwave photonic temporal signal processing with Kerr micro-combs

2020· article· en· W3102693371 on OpenAlexaff
Mengxi Tan, Xingyuan Xu, Jiayang Wu, Roberto Morandotti, Arnan Mitchell, David Moss

Bibliographic record

VenueAdvances in Physics X · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersAustralian Research Council
KeywordsMicrowaveSignal processingPhotonicsSIGNAL (programming language)PhysicsKerr effectOpticsOptoelectronicsComputer scienceTelecommunicationsRadarNonlinear system

Abstract

fetched live from OpenAlex

Integrated Kerr micro-combs are a powerful source of multiple wavelength channels for photonic radio frequency (RF) and microwave signal processing, particularly for transversal filter systems. They offer significant advantages featuring a compact device footprint, high versatility, large numbers of wavelengths, and wide Nyquist bands. We review progress on photonic RF and microwave high bandwidth temporal signal processing based on Kerr micro-combs with comb spacings from 49 GHz to 200 GHz. We focus on integral and fractional Hilbert transforms, differentiators as well as integrators. The future potential of optical micro-combs for RF photonic applications in terms of functionality and ability to realize integrated solutions is also discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.240
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

Citations335
Published2020
Admission routes1
Has abstractyes

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