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Record W4249292489 · doi:10.1002/9781118684207.ch11

Digital Signal Processing

2014· other· en· W4249292489 on OpenAlexaff
M. Jamal Deen

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDigital signal processingPolarization mode dispersionNonlinear systemDispersion (optics)Signal processingElectronic engineeringModal dispersionFiber-optic communicationComputer sciencePolarization (electrochemistry)SIGNAL (programming language)OpticsOptical fiberPhysicsTelecommunicationsDispersion-shifted fiberEngineeringFiber optic sensor

Abstract

fetched live from OpenAlex

The key component that revived coherent fiber communications in the mid-2000 was high-speed digital signal processing (DSP). With the advances in high-speed DSP, phase alignment and polarization management can be done in the electrical domain. Linear impairments such as chromatic dispersion (CD) and polarization mode dispersion can be compensated using equalizers. It is also possible to compensate for the interplay between dispersion and nonlinearity by using digital back propagation (DBP), in which the nonlinear Schrödinger equation is solved for a virtual fiber whose signs of dispersion, loss, and nonlinear coefficients are opposite to those of the transmission fiber.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0530.047

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations14
Published2014
Admission routes1
Has abstractyes

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