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Record W3041826191 · doi:10.1117/1.oe.59.7.076105

Design and performance analysis of GPON-employed two-dimensional multidiagonal OCDMA code

2020· article· en· W3041826191 on OpenAlexaff
Kehkashan A. Memon, Ahmad Atieh, A. W. Umrani, Mukhtiar Ali Unar, Wajiha Shah

Bibliographic record

VenueOptical Engineering · 2020
Typearticle
Languageen
FieldEngineering
Topicgraph theory and CDMA systems
Canadian institutionsOptiwave Systems (Canada)
Fundersnot available
KeywordsComputer sciencePassive optical networkOptical engineeringCode division multiple accessCode (set theory)TelecommunicationsOpticsWavelength-division multiplexingPhysicsProgramming language

Abstract

fetched live from OpenAlex

Two-dimensional (2-D) spectral/spatial codes are utilized for optical code division multiple access (OCDMA)-based passive optical network (PON). In this work, 2-D multidiagonal (2D-MD) codes are used for the first time to the best of our knowledge in OCDMA-PON at data rates up to 15 Gbps. 2D-MD codes are very easy to construct and offer zero cross correlation. We showed a simpler implementation compared to similar published work. A complete PON system addressing downstream as well as upstream communication link is demonstrated at data rates up to 15 Gbps. Four downstream wavelengths (1546, 1546.3, 1547.8, and 1548.1 nm) and four upstream wavelengths (1310, 1310.3, 1311.8, and 1312.1 nm) are used to carry the modulated signal over single-mode fiber length of 25 km. The system performance is analyzed using bit error rate and Q-factor parameters for different data rates.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.013
GPT teacher head0.193
Teacher spread0.181 · 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 designSimulation or modeling
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

Citations4
Published2020
Admission routes1
Has abstractyes

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