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Record W2522729713 · doi:10.1002/mmce.21053

Analytic study on CP enhancement of millimeter wave DR and patch subarray antennas

2016· article· en· W2522729713 on OpenAlexaff
M. Akbari, Shraman Gupta, Mohammadmahdi Farahani, A. Sebak, Tayeb A. Denidni

Bibliographic record

VenueInternational Journal of RF and Microwave Computer-Aided Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à MontréalConcordia University
Fundersnot available
KeywordsBandwidth (computing)Extremely high frequencyImpedance matchingSplit-ring resonatorPatch antennaResonatorDielectricElectrical impedanceMaterials scienceOpticsAntenna (radio)AcousticsComputer scienceOptoelectronicsPhysicsEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

This article presents a comparative analysis for the performance of single, 2 × 2, and 4 × 4 dielectric resonator (DR) and patch circularly polarized (CP) antenna subarrays at 30 GHz. In order to enhance the CP bandwidth, the subarray elements are fed by two kinds of sequential feeding techniques using parallel and hybrid ring feeds. The 4 × 4 patch antenna subarrays fed by parallel and hybrid ring feeding networks are fabricated and tested. Measurements show acceptable agreement with simulation results. The experimental results show a bandwidth of 36.9% for both (−10 dB) impedance matching and (3 dB) axial ratio CP patterns for the patch subarray antenna with hybrid ring feeding. For the parallel feeding, the corresponding bandwidth is 28.81%. The proposed antennas combine desirable features such as wide impedance and AR bandwidths, low profile, and easiness of fabrication and therefore is a good candidate for millimeter wave systems around 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 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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

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