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Record W2902339676 · doi:10.1109/mwp.2018.8552866

Programmable Fiber-Optics Microwave Photonic Filter based on Temporal Talbot Effects

2018· article· en· W2902339676 on OpenAlexaff
Reza Maram, Daniel Onori, José Azaña, Lawrence R. Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsInstitut National de la Recherche ScientifiqueMcGill University
Fundersnot available
KeywordsPassbandArbitrary waveform generatorOpticsTalbot effectBand-pass filterOptical filterPhase modulationIntensity modulationPulse shapingPhotonicsFilter (signal processing)PhysicsWaveformMaterials scienceComputer scienceTelecommunicationsPhase noiseRadar

Abstract

fetched live from OpenAlex

We introduce and experimentally demonstrate a reconfigurable microwave photonic filter based on temporal Talbot effects. An optical pulse source is employed to sample the microwave signal through intensity modulation. The sampled signal is then propagated through the Talbot-based microwave photonic filter, involving temporal phase modulation and chromatic dispersion. The microwave photonic filter exhibits a periodic transfer function whose passband frequency and frequency periodicity (free spectral range) can be programmed electrically by adjusting the phase-modulation profile, e.g., using an arbitrary waveform generator, without the need for manual adjustment of the optical components and with the potential for fast tuning of the filter's response. The bandwidth of the filter passband can also be easily customized by adjusting the sampling pulse width using an optical bandpass filter.

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 score0.004

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 communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.237
Teacher spread0.225 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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