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Record W2593410341 · doi:10.1049/el.2017.0380

Design of improved ring Wilkinson power divider for millimetre wave applications

2017· article· en· W2593410341 on OpenAlexaff
D. Hammou, Mourad Nedil, Serioja Ovidiu Tatu

Bibliographic record

VenueElectronics Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsWilkinson power dividerPower dividers and directional couplersResistorMicrostripFrequency dividerElectrical engineeringMillimetre waveCurrent dividerInsertion lossBandwidth (computing)Electronic engineeringEngineeringMaterials scienceOptoelectronicsTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

An improved millimetre wave microstrip power divider is presented. This circuit enhanced the conventional Wilkinson power divider (WPD) performance by adding transmission lines between the transformer arms and the resistor to reduce inherent undesirable mutual coupling. The proposed power divider design parameters show more stability over a wider frequency band compared to other valid design solutions. For comparison purposes, the proposed power divider along with the WPD are designed at 61 GHz using high permittivity thin ceramic substrate. The measurement results of the proposed circuit demonstrate improved bandwidth with high input/output matching and isolation of −15 and −24 dB, respectively, in the whole frequency band of 10 GHz. Furthermore, the excess insertion loss is reduced by 20% compared to the conventional one.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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 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

Citations6
Published2017
Admission routes1
Has abstractyes

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