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Record W2327032656 · doi:10.1109/jlt.2015.2433176

A DSP-Based Control Method For a Nonlinear Mach–Zehnder Interferometer DPSK Regenerator

2015· article· en· W2327032656 on OpenAlexafffund
Shu Zhang, John C. Cartledge

Bibliographic record

VenueJournal of Lightwave Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRegenerative heat exchangerInterferometryMach–Zehnder interferometerDigital signal processingNonlinear systemOpticsNonlinear opticsPhysicsElectronic engineeringComputer scienceEngineeringLaser

Abstract

fetched live from OpenAlex

A frequency offset and phase control method is described for a differential-phase-shift-keying (DPSK) 2R (reamplification and reshaping) regenerator based on a nonlinear Mach-Zehnder interferometer. The controller is comprised of a coherent receiver, analog-to-digital converters (ADCs), a digital signal processor, and an optical frequency/phase shifter. For a 40 Gb/s DPSK signal, simulation results demonstrate that an ADC sampling rate of only 335.94 MSa/s is needed to adequately estimate the frequency offset and phase noise, and adjust the pump signal accordingly. The cascadability of the 2R regenerator is evaluated in recirculating loop simulations for different regenerator spacings, using both ideal DPSK signals and captured experimental DPSK signals. The influence of residual span dispersion on the performance is investigated.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.418
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.270
Teacher spread0.250 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2015
Admission routes2
Has abstractyes

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