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Record W2916729673 · doi:10.1002/mop.31810

A new RFID monopole antenna using a compact AMC structure

2019· article· en· W2916729673 on OpenAlexaff
Hifa Houssein Elzuwawi, Muhammad M. Tahseen, Ghada Hussain Elzwawi, Tayeb A. Denidni

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMonopole antennaAntenna efficiencyAntenna (radio)Radiation patternAntenna measurementDipole antennaCoaxial antennaReflector (photography)Antenna factorPatch antennaOpticsElectrical engineeringOptoelectronicsElectronic engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract A new compact artificial magnetic conductor (AMC) based coplanar waveguide (CPW) antenna is presented for RFID applications. The antenna is designed with hexagonal ring and patch elements. The proposed AMC with an electrical size of 0.02 λ0 is designed as a reflector for a monopole antenna which improves the antenna gain from 2 to 6.7 dBi. The monopole antenna is designed using cascaded hexagonal patch and ring elements. The simulated and experimental performance of the proposed antenna are presented in terms of return loss, gain and radiation patterns, with and without the presence of AMC. The performance of the antenna is evaluated at 868 MHz (European RFID Band). The obtained results exhibit good agreement. The proposed antenna provides compactness, cost efficiency, higher gain, and higher radiation efficiency (at 868 MHz).

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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