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Record W2615309685 · doi:10.1109/access.2017.2704523

A Novel Low-Loss Millimeter-Wave 3-dB 90° Ridge-Gap Coupler Using Large Aperture Progressive Phase Compensation

2017· article· en· W2615309685 on OpenAlexaff
Mohammadmahdi Farahani, M. Akbari, Mourad Nedil, Tayeb A. Denidni, A. Sebak

Bibliographic record

VenueIEEE Access · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia UniversityInstitut National de la Recherche ScientifiqueUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à Montréal
Fundersnot available
KeywordsHybrid couplerOpticsReturn lossExtremely high frequencyMaterials scienceInsertion lossPhase (matter)Rat-race couplerAperture (computer memory)WaveguideMicrostripCompensation (psychology)Power dividers and directional couplersPhysicsTelecommunicationsAcousticsComputer scienceAntenna (radio)

Abstract

fetched live from OpenAlex

A novel millimeter-wave (mm-wave) 90° phase-compensated hybrid coupler using the ridge-gap waveguide (RGW) technology is studied and developed. The coupler is low-loss at mm-wave frequency bands, whereas using conventional transmission lines, such as microstrip or substrate integrated waveguides, yields relatively a high amount of insertion loss. The proposed coupler is designed using large coupling apertures, which with their phase variation over the apertures can compensate for the progressive phase of the coupler and achieve a low output-phase error. The ridge-gap 3-db 90° hybrid coupler is suitable for designing mm-wave beamforming networks, such as the Butler matrix beamformer, where the cross-over transmission lines present a difficulty in designing such networks. In this paper, the dispersion diagram of the RGW is extracted, and a multicoupling aperture technique is formulated for designing the coupler. The coupler is fabricated and measured. The simulated and measured results show a good 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.067
GPT teacher head0.324
Teacher spread0.257 · 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 designBench or experimental
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

Citations45
Published2017
Admission routes1
Has abstractyes

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