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Record W2611688586 · doi:10.24200/tjer.vol7iss2pp59-68

Mutual Coupling Reduction Between Axial-Mode Helical Antennas using Single-Negative (SNG) Magnetic Metamaterials

2010· article· en· W2611688586 on OpenAlexafffund
Mohammed M. Bait‐Suwailam, Omar M. Ramahi

Bibliographic record

VenueThe Journal of Engineering Research [TJER] · 2010
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaSultan Qaboos University
KeywordsMetamaterialResonatorCoupling (piping)Materials scienceHelical antennaAntenna (radio)Coupling coefficient of resonatorsSplit-ring resonatorAcousticsPhysicsOptoelectronicsAntenna efficiencyEngineeringDipole antennaElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

This paper investigates the mutual coupling effects between axial-mode helical antenna arrays. Single-negative (SNG) magnetic metamaterials are developed and used in this work in order to reduce mutual coupling between axial-mode helical antennas used in multiple-input multiple-output systems. The inclusions considered here are composed of broadside coupled split-ring resonators. The magnetic permeability of those SNG inclusions have complex effective magnetic permeability response over a frequency band with high negative real part above the resonance and positive real part below the resonance. The SNG resonators are inserted between closely-spaced axial-mode helical antenna elements. Effective responses of the constitutive parameters of the developed magnetic inclusions are incorporated within the numerical models. It is shown that mutual coupling between the antenna elements can be reduced significantly by incorporating such magnetic inclusions. The SNG magnetic resonators work as antenna decoupler, and thus can be applied in a variety of antenna applications.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.752
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.065
GPT teacher head0.323
Teacher spread0.259 · 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.

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

Citations5
Published2010
Admission routes2
Has abstractyes

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