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Record W2594530774

CMRR analysis for widedand passive monolithic differential quadrature coupler

2006· article· en· W2594530774 on OpenAlexaff
Karim W. Hamed, Alois P. Freundorfer, Yahia M. M. Antar

Bibliographic record

VenueInternational Symposium on Antenna Technology and Applied Electromagnetics · 2006
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsHybrid couplerBroadbandWidebandReturn lossPower dividers and directional couplersInsertion lossElectronic engineeringCommon-mode rejection ratioRat-race couplerFrequency bandAmplifierOptoelectronicsMaterials sciencePhysicsElectrical engineeringComputer scienceEngineeringBandwidth (computing)OpticsTelecommunicationsCMOSOperational amplifier
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the performance characteristics along with the common mode rejection ratio (CMRR) analysis of a broadband passive quadrature differential coupler implemented using GaAs technology. The coupler utilizes a multi-dielectric layer structure to achieve a broadband performance from 15 to 45 GHz in a simple coplanar configuration. A return loss better than 12 dB with isolation better than 15 dB and CMRR of 20 dB have been achieved over the entire frequency band. Also phase and amplitude mismatch within +5°and 1.5 dB, respectively have been realized over the same frequency range. This performance is especially important to the intended use with wideband double balanced mixers, and amplifiers. A slow-wave mechanism was also introduced to the coupler and has led to 14% reduction in the coupled length.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.003
GPT teacher head0.188
Teacher spread0.185 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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