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Record W3013875832 · doi:10.1109/access.2020.2982937

Dual Complementary Source Magneto-Electric Dipole Antenna Loaded With Split Ring Resonators

2020· article· en· W3013875832 on OpenAlexaff
Mehri Borhani Kakhki, Abdolmehdi Dadgarpour, Marco A. Antoniades, Abdel-Razik Sebak, Tayeb A. Denidni

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsToronto Metropolitan UniversityConcordia UniversityInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsDipole antennaPhysicsBandwidth (computing)OptoelectronicsImpedance matchingOpticsAntenna (radio)Electrical engineeringElectrical impedanceComputer scienceTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper, a magneto-electric (ME) dipole antenna excited by two slots through a printed ridge gap waveguide (PRGW) is presented. This antenna, which operates in the Ka-band, is considered as a dual complementary source (DCS), and exhibits a higher gain and a wider impedance bandwidth, compared to conventional ME dipoles. The proposed antenna has an impedance matching bandwidth with |S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> | <; -10 dB from 24.5 to 40 GHz with a stable gain of 10±1 dBi over the 25-35 GHz band. Moreover, by integrating horizontally three layers, each with an array of 4 × 10 split-ring resonator (SRR) unit cells on top of the DCS-ME dipole antenna, the realized gain is increased while maintaining the impedance bandwidth of the antenna. The antenna was fabricated and measured to confirm the simulation results. The fabricated prototype has a size of 1.1 × 1 × 0.58 λ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> (at 30 GHz) and exhibits a measured impedance bandwidth in excess of 50%, from 24 to 40 GHz, a maximum measured gain of 14.2 dBi at 35 GHz, and a measured radiation efficiency of 93% at 30 GHz.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.236
Teacher spread0.212 · 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 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

Citations19
Published2020
Admission routes1
Has abstractyes

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