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Record W1675233263 · doi:10.1109/pn.2015.7292540

Symmetric dual polarization diverse digital optical coherent receiver

2015· article· en· W1675233263 on OpenAlexaff
Neda Nabavi, Sawsan Abdul-Majid, Trevor J. Hall

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBeam splitterDetectorPolarization (electrochemistry)Orthogonal polarization spectral imagingPhysicsOpticsSignal processingComputer scienceElectronic engineeringDigital signal processingEngineeringComputer hardware

Abstract

fetched live from OpenAlex

Digital coherent receivers are an essential building block of optical coherent communication systems. Improved traffic capacity, increased spectral efficiency, and support for advanced modulation formats are amongst their attractive features. The two electrical outputs of a coherent detector may be considered to provide the real and imaginary parts of a complex beat signal representing the value of a sesqui-linear form that combines the analytic signals representing its two optical input ports. An interchange of signal and reference may therefore be compensated by complex conjugation of the beat signal. Conventional polarization diverse coherent receivers use a polarizing beam splitter to provide two orthogonal linearly polarized components and then the polarization of one component is rotated so both components align with two copies of the linearly polarized local oscillator provided by a conventional beam splitter. This arrangement destroys the symmetry of the coherent detector to interchange of signal and reference.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.022
GPT teacher head0.210
Teacher spread0.188 · 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
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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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