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Record W2243939907 · doi:10.1117/12.469579

Properties of wavelength converters based on semiconductor optical amplifiers in the arms of a Mach-Zehnder interferometer

2002· article· en· W2243939907 on OpenAlexaff
John C. Cartledge, Shaochun Cao

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsOpticsChirpSIGNAL (programming language)WavelengthOptical amplifierWaveformInterferometryPhysicsModulation (music)Materials sciencePhase modulationOptoelectronicsPhase noiseLaserAcousticsVoltageComputer science

Abstract

fetched live from OpenAlex

The results of a detailed experimental characterization of a three-port, all-active SOA-MZI wavelength converter are presented. It is shown that for counter-propagating input signals, the ASE noise is intensity modulated due to gain saturation in the output SOA. Consequently, the waveform for the wavelength-converted signal is determined by both cross- phase modulation and self-gain modulation. Time- and frequency-domain measurements are used to characterize the properties of the modulated ASE noise, wavelength-converted signal and total signal. For both co- and counter- propagating signals, the small-signal chirp properties of the wavelength converted along the conversion curve are considered. The measured results for the small-signal chirp and optical conversion are incorporated into a device model that can be used to obtain the large-signal pulse response. Based on this model, good agreement is demonstrated between calculated and measured results for the time dependence of the intensity and chirp of the wavelength-converted signal.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
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 head0.021
GPT teacher head0.206
Teacher spread0.185 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Network TechnologiesFrench-language works237,207