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Record W2536601924 · doi:10.1109/tap.2016.2620522

Wideband Low-Loss Magnetoelectric Dipole Antenna for 5G Wireless Network With Gain Enhancement Using Meta Lens and Gap Waveguide Technology Feeding

2016· article· en· W2536601924 on OpenAlexaff
Abdolmehdi Dadgarpour, Milad Sharifi Sorkherizi, Ahmed A. Kishk

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsWidebandDipole antennaAntenna (radio)OptoelectronicsMaterials scienceWirelessWaveguideOpticsAntenna gainAntenna efficiencyPhysicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

In this paper, a wideband magnetoelectric (ME) dipole antenna excited by a slot coupled to a fork-shaped printed ridge gap waveguide is proposed. To enhance the antenna gain, a meta lens consists of three layers of split ring resonators (SRRs) are integrated horizontally in front of the ME dipole antenna. Each slab contains a 3×7 array of SRR unit cells. The matching bandwidth is improved as a secondary effect of the lens. A 1 × 4 array is designed to provide more gain by adjusting the lens between the edge elements of the four-element array. The antenna is fabricated and tested. The measured results show that -10 dB S11is obtained between 26.5 and 38.3 GHz. The measure realized gain is better than 15 dBi over the frequency range of 28-38 GHz. A 90% radiation efficiency at 30 GHz is achieved.

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: Methods · 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)0.0000.000
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)0.0010.001

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 head0.018
GPT teacher head0.215
Teacher spread0.197 · 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
GenreMethods

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

Citations103
Published2016
Admission routes1
Has abstractyes

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