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Dual-band Hybrid Dielectric Resonator-patch Antenna for Microwave and Mm-wave Applications

2024· article· en· W4403024014 on OpenAlexaff
Mohamed Sedigh Bizan, Tayeb A. Denidni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsPatch antennaMicrowaveMicrostrip antennaDielectric resonator antennaResonatorMulti-band deviceMaterials scienceDielectricDielectric resonatorAcousticsAntenna (radio)Dual (grammatical number)OptoelectronicsElectrical engineeringElectronic engineeringComputer scienceTelecommunicationsPhysicsEngineering

Abstract

fetched live from OpenAlex

This paper presents a pioneering approach to advanced antenna design focusing on a dual-band hybrid configuration, combining the strengths of dielectric resonator antennas (DRAs) and serial patch antennas. The study explores the significance of this hybrid architecture in addressing the demanding requirements of both microwave and millimeter-wave (mm-wave) applications in modern communication systems. The paper introduces a meticulously crafted antenna design that seamlessly integrates these technologies for optimal performance. In the microwave band, the proposed antenna achieves a realized gain of 12.54dBi, a match impedance of 10.7%, and operates at a frequency of 5.8GHz. Similarly, in the mm-wave band, the antenna exhibits a realized gain of 12.5dBi, a match impedance of 10.2%, and operates at a frequency of 30GHz. This novel dual-band hybrid antenna design serves as a noteworthy contribution to the evolving landscape of advanced antenna technologies, offering practical solutions to the complex challenges of contemporary communication systems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.012
GPT teacher head0.215
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 designBench or experimental
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

Citations3
Published2024
Admission routes1
Has abstractyes

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