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Record W1906933180 · doi:10.1109/aps.2015.7304513

Beamforming bow-tie antenna for millimeter-wave applications using metamaterial lens

2015· article· en· W1906933180 on OpenAlexaff
Abdolmehdi Dadgarpour, Behnam Zarghooni, Tayeb A. Denidni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBow tieMetamaterialAntenna (radio)OpticsMetamaterial antennaReflection coefficientExtremely high frequencyRadiation patternAntenna measurementVivaldi antennaAntenna efficiencyBeamformingMaterials sciencePhysicsAcousticsCoaxial antennaComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a new approach to achieve a beamforming network for 60 GHz applications. The proposed technique consists of a printed bow-tie antenna with end-fire radiation type integrated by an array of metamaterial lens. Embedded in the elevation-plane of the printed bow-tie antenna, an array of metamaterial unit-cells with low-index is proposed to obtain a beam-tilted angle of 22 degrees with respect to the end-fire direction. Furthermore, the antenna gain is enhanced by 6 dBi, when the proposed unit-cells are integrated compared to a conventional bow-tie antenna. The simulation results indicate that with this technique a scanning angle of 22 degree can be achieved across the frequency band of 57-64 GHz while the reflection coefficient is below -10 dB.

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 score0.005

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 communication0.0000.001
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 head0.082
GPT teacher head0.255
Teacher spread0.173 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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