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Record W2105690800 · doi:10.1007/s12243-012-0351-6

90° SOI optical hybrid for Radio-over-fibre links

2013· article· fr· W2105690800 on OpenAlexafffund
Sawsan Abdul-Majid, Imad Hasan, Qi Zheng, Ramón Maldonado-Basilio, Serge Bidnyk, Trevor Hall

Bibliographic record

VenueAnnals of Telecommunications · 2013
Typearticle
Languagefr
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
FundersCanada Research ChairsCMC Microsystems
KeywordsFootprintOpticsTransmitterOptoelectronicsPhysicsElectronic engineeringComputer scienceTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Radio-over- fibre (RoF) technology is receiving large attention due to its ability to provide simple antenna front ends, increased capacity and increased wireless access coverage. Coherently detected RoF systems would enable the information to be carried in both the amplitude and phase or in different states of the polarisation of the optical field. Additionally, the selectivity of coherent receiver is very well suited for access networks. We present a 90° optical hybrid built on a silicon-on-insulator planar light-wave circuit, which can be used as the optical front end of the digital coherent receiver in a digitised RoF link and will lead to reduced receiver footprint and cost. The optical hybrid circuit includes 2 × 2 and 4 × 4 multimode interference (MMI) splitters, in a polarisation diversity configuration. The simulation results at vacuum wavelength 1,550 nm show polarisation independence and phase errors between the ports of less than 0.03°. The properties of the prototyped 4 × 4 MMI were measured over a wide range of wavelengths. The 2 × 2 and 4 × 4 MMI showed nearly equal splitting ratios. Measurements of the relative phase relationship between the ports for Transverse Electric mode polarisation are shown to match the simulation results.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.664
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.061
GPT teacher head0.333
Teacher spread0.273 · 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.

Study designNot applicable
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

Citations1
Published2013
Admission routes2
Has abstractyes

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