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Record W2792349362 · doi:10.1002/mmce.21270

A state-of-art review on performance improvement of dielectric resonator antennas

2018· review· en· W2792349362 on OpenAlexaff
Sounik Kiran Kumar Dash, Taimoor Khan, Yahia M. M. Antar

Bibliographic record

VenueInternational Journal of RF and Microwave Computer-Aided Engineering · 2018
Typereview
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsHigh-gain antennaDielectric resonatorDielectric resonator antennaDielectricAntenna gainBandwidth (computing)Microstrip antennaPolarization (electrochemistry)Computer scienceElectronic engineeringEngineeringAntenna (radio)Electrical engineeringTelecommunicationsAntenna efficiency

Abstract

fetched live from OpenAlex

This article outlines a compressive review on investigation carried out targeting to gain, circular polarization (CP), and mutual coupling reduction in dielectric resonator antenna (DRA). The DRA has already been created a separate position in antenna engineering domain because of its adept characteristics, such as wide bandwidth, high efficiency, low-loss, and mainly 3D-design flexibility which is rarely available in conventional antennas. In this context, the research on gain, circular polarization, and mutual coupling are quite interesting and being carried out from the last two decades. The ultimate aim of this article is to (i) give an overview of different techniques adopted in context to gain, CP, and mutual coupling reduction; (ii) give a compressive review of notable research carried out targeting to these three characteristics; and (iii) find out the research gap concentration for furtherance of the same.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations51
Published2018
Admission routes1
Has abstractyes

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