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Record W1972238080 · doi:10.2528/pier08062804

APPLICATION OF ARTIFICIAL GROUND PLANES IN DUAL-BAND ORTHOGONALLY-POLARIZED LOW-PROFILE HIGH-GAIN PLANAR ANTENNA DESIGN

2008· article· en· W1972238080 on OpenAlexaff
Alireza Foroozesh, Malcolm Ng Mou Kehn, L. Shafai

Bibliographic record

VenueElectromagnetic waves · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPlanarMulti-band deviceGround planeAntenna (radio)OpticsDual (grammatical number)High-gain antennaPhysicsEngineeringElectrical engineeringComputer scienceArtComputer graphics (images)

Abstract

fetched live from OpenAlex

Application of artificial ground planes in design of compact cavity-resonance dual-band high-gain antennas is presented.The artificial ground plane consists of periodic strip grating on grounded dielectric slab.A code based on method of moment (MoM) is developed to analyze and design such artificial ground planes.The reflection parameters obtained using the MoM code are employed to characterize the surface impedance of the artificial ground plane for different incident angles and both TE and TM polarizations.Then, this impedance surface is used in transverse equivalent network (TEN) model of the cavity-resonance antenna with high-permittivity dielectric superstrate.Using TEN model radiation properties of such antennas are analyzed.Finally, the antenna with the compact size is designed to demonstrate the maximum directivity.An interesting characteristic of this antennas is that when the antenna ground plane acts as an artificial magnetic conductor the height of the antenna is almost reduced by a factor of two, while its directivity is increased by about 1 dB compared to the conventional antennas of this class having PEC ground plane.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.000
Threshold uncertainty score0.001

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 communication0.0000.000
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 head0.012
GPT teacher head0.204
Teacher spread0.193 · 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 designSimulation or modeling
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

Citations18
Published2008
Admission routes1
Has abstractyes

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