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Record W1495441780 · doi:10.1109/lpt.2015.2450719

Frequency- and Notch-Depth-Tunable Single-Notch Microwave Photonic Filter

2015· article· en· W1495441780 on OpenAlexfundno aff
Enming Xu, Jianping Yao

Bibliographic record

VenueIEEE Photonics Technology Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBand-stop filterOptical filterFiber Bragg gratingSidebandOpticsMaterials scienceCompatible sideband transmissionBand-pass filterBandwidth (computing)PhotonicsOptoelectronicsMicrowaveOptical Carrier transmission ratesWavelengthOptical fiberPhysicsRadio over fiberLow-pass filterTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

A single-notch microwave photonic filter with tunable frequency and tunable notch depth is proposed and experimentally demonstrated. In the proposed filter, two phase-modulated optical signals at two different wavelengths are generated at a phase modulator. One phase-modulated signal is filtered by a phase-shifted fiber Bragg grating with an ultra-narrow notch to remove the upper sideband. The other phase-modulated signal is filtered by a wideband optical bandpass filter to remove the lower sideband. Since the remaining lower and upper sidebands in the two optical signals are out of phase, the combination of the beat signals at a photodetector will lead to the cancelation of the detected microwave signals, to generate an ultranarrow notch. An experiment is performed. A single-notch microwave photonic filter with a 3-dB bandwidth of ~180 MHz, a tunable frequency from 1.5 to 6.6 GHz, and a tunable notch depth from 0 to 42 dB is experimentally 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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.026
GPT teacher head0.228
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

Citations37
Published2015
Admission routes1
Has abstractyes

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