MétaCan
Menu
Back to cohort
Record W2997621578 · doi:10.1109/access.2019.2962737

Analytical and Numerical Investigation of Radiation Enhancement by Anisotropic Metamaterial Shells

2019· article· en· W2997621578 on OpenAlexafffund
R. Aneesh Kumar, Justin G. Pollock, Chinmoy Saha, Ashwin K. Iyer

Bibliographic record

VenueIEEE Access · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMetamaterialMetamaterial antennaPhysicsIsotropyOpticsAnisotropyEffective radiated powerAntenna (radio)Computational physicsHyperboloid modelRadiationRadiation patternTelecommunicationsSlot antenna

Abstract

fetched live from OpenAlex

This paper presents analytical and numerical investigations of a 3D cylindrical metamaterial shell possessing a cylindrically anisotropic permeability that is excited by a finite-sized electric line source. A comprehensive field analysis of the system reveals that the compact metamaterial shell exhibits resonances akin to those observed in isotropic 2D cylindrical metamaterial structures, which may be used to enhance the radiated power of a nearby antenna. An analytical resonance condition that relates the dimensions of the cylindrical shell to its anisotropic effective-medium parameters is shown to be accurate in predicting the resonances of 2D and 3D metamaterial shells obtained using full-wave simulations. The effects of anisotropy and finite shell/antenna height on the system's near-fields, radiation patterns, and power-ratio enhancements are explored. It is shown through both theory and simulations that the condition for resonance is largely independent of shell/antenna height but that the quality factor reduces dramatically as these heights approach electrically small values. Also, a dispersion and loss analysis assuming Lorentz model is carried out, which indicates that practical metamaterial losses do not significantly degrade the power enhancement.

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 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: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.276

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 communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.255
Teacher spread0.241 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueIEEE AccessSame topicAdvanced Antenna and Metasurface TechnologiesFrench-language works237,207