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Record W2018335649 · doi:10.1109/lpt.2013.2244880

Optical Back Propagation With Optimal Step Size for Fiber Optic Transmission Systems

2013· article· en· W2018335649 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Photonics Technology Letters · 2013
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOpticsNonlinear systemDispersion (optics)Optical fiberFiber Bragg gratingTransmission (telecommunications)Nonlinear Schrödinger equationFiberSelf-phase modulationLagrange multiplierPhase (matter)Dispersion-shifted fiberMaterials scienceNonlinear opticsPhysicsMathematicsFiber optic sensorComputer scienceTelecommunicationsMathematical optimizationLaser

Abstract

fetched live from OpenAlex

An optical back propagation scheme consisting of an optical phase conjugator, fiber Bragg gratings (FBGs), and highly nonlinear fibers (HNLFs) is investigated. Transmission fiber dispersion is compensated by the FBGs and the nonlinearity is compensated by HNLFs. Several sections of FBGs and HNLFs are concatenated in a way analogous to the split-step Fourier scheme used for solving the nonlinear Schrödinger equation. The optimum accumulated dispersion of each section of the FBG and the optimum nonlinear phase shift of the each section of the HNLF are calculated by minimizing the mismatch between the area under the exponentially increasing nonlinearity profile and its stepwise approximation. The method of Lagrange multipliers is used for optimization. The proposed optimization technique leads to significant performance improvement and/or reach enhancement as compared to uniformly spaced sections, for the given number of sections.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.188
Teacher spread0.183 · 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