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Record W2734327298 · doi:10.1109/tap.2017.2724585

Template-Based Dielectric Resonator Antenna Arrays for Millimeter-Wave Applications

2017· article· en· W2734327298 on OpenAlexaff
Aqeel A. Qureshi, David M. Klymyshyn, Matt Tayfeh, Waqas Mazhar, Martin Börner, Jürgen Mohr

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2017
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Saskatchewan
FundersCOMSATS Institute of Information Technology
KeywordsMaterials scienceDielectric resonator antennaOptoelectronicsDielectricAntenna (radio)ResonatorFabricationExtremely high frequencyRadiation patternAntenna arrayOpticsComputer scienceTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

An approach suitable for millimeter-wave dielectric resonator antenna (DRA) arrays is presented. The methodology involves fabricating precise cavities in acrylic templates and filling them with composite dielectric materials to create a monolithic polymer-based DRA (PRA) array layer. The excitation feed lines are fabricated on a separate substrate layer and the two layers are aligned and bonded together to form the PRA antenna array module. The impact of the acrylic frame on the PRA performance is analyzed through simulations. A four-element array operating at 60 GHz is realized to demonstrate the approach. The performance is characterized through simulation and also experimentally verified. The array offers a wide 12% impedance bandwidth at 60 GHz and broadside radiation with 10.5-dBi realized gain and stable radiation patterns. The use of polymer-based materials provides opportunities for cost-effective volume fabrication using molding techniques.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.002

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 head0.026
GPT teacher head0.238
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 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

Citations45
Published2017
Admission routes1
Has abstractyes

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