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Record W2096193456 · doi:10.2528/pier07092505

QUASI-METALLIC-WALL TECHNIQUE FOR INCREASING THE EFFICIENCY OF CB-CPW ANTENNAS

2007· article· en· W2096193456 on OpenAlexafffund
Mourad Nedil, M.A. Habib, Tayeb A. Denidni, Halim Boutayeb

Bibliographic record

VenueElectromagnetic waves · 2007
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaNational Science Council
KeywordsAntenna efficiencyAntenna (radio)Antenna measurementAntenna factorMaterials scienceRadiation patternDipole antennaCoaxial antennaOptoelectronicsOpticsAcousticsElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract—This paper presents a new quasi-metallic-wall technique for improving the gain of CB-CPW single antenna and arrays. This technique allows reducing the surface wave losses of the CB-CPW antennas, which decreases the antenna radiation efficiency. It consists on including pins as quasi-metallic wall between the upper and lower ground planes in the CB-CPW antenna structure. To validate the proposed approach, a CB-CPW-slot antenna fed through an inductive coupled CPW-line operating at 5.8 GHz is considered. This approach allows to increase the antenna efficiency from 70 % to 95 % around the operating frequency. The antenna gain achieves then an improvement of 2 dBi. Also, an antenna array is designed and the pins technique is also applied to prove its applicability for the array case. An efficiency increase from 64 % to 95 % was achieved. Both single antenna and antenna array with pins were fabricated and measured. A good agreement between numerical and experimental results was obtained. 1.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.007
GPT teacher head0.218
Teacher spread0.210 · 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

Citations8
Published2007
Admission routes2
Has abstractyes

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