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Record W2776710665 · doi:10.1109/access.2017.2784801

Printed Ridge Gap Waveguide 3-dB Coupler: Analysis and Design Procedure

2017· article· en· W2776710665 on OpenAlexaff
Mohamed Mamdouh M. Ali, Shoukry I. Shams, Abdel-Razik Sebak

Bibliographic record

VenueIEEE Access · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsPlanarHybrid couplerMicrowaveComputer scienceWaveguideFrequency bandElectronic engineeringMaterials scienceTelecommunicationsOpticsBandwidth (computing)Power dividers and directional couplersOptoelectronicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Communication systems are witnessing an outstanding revolution that has a clear impact on all aspects of life. The world technology is drifting towards high frequency and data rate solutions to accommodate the future expansion in applications such as 5G communications. The 5G technology will offer advanced features in the mm-Wave frequency band which requires intelligent subsystems such as beam switching. Therefore, the microwave components, especially couplers, still need a significant improvement to follow the rapid variations in future technologies. One of the most recent and promising guiding technologies for mm-Wave applications is the printed ridge gap waveguide (PRGW). In this paper, a design of 3-dB planar quadrature hybrid coupler based on PRGW is presented. The proposed design has superior characteristics such as compactness, low loss, and low dispersion device. The prototype of the proposed coupler is fabricated and tested, where the measured and simulated results show an excellent agreement.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.284
Teacher spread0.245 · 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
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".

Quick stats

Citations92
Published2017
Admission routes1
Has abstractyes

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