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Record W2554506032 · doi:10.1109/piers.2016.7735763

Beam-tilting antenna with metamaterial loading

2016· article· en· W2554506032 on OpenAlexaff
Jinxin Li, Tayeb A. Denidni, Ruizhi Liu, Qingsheng Zeng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsPolytechnique MontréalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMetamaterialMetamaterial antennaOpticsAntenna (radio)ResonatorRefractive indexFractal antennaSplit-ring resonatorDielectric resonator antennaPhysicsBeam (structure)Directional antennaOptoelectronicsMicrostrip antennaCoaxial antennaComputer scienceSlot antennaTelecommunications

Abstract

fetched live from OpenAlex

In this work, we proposed a beam-tilting antenna based on metamaterial loading. The proposed antenna consists of a dielectric resonator antennas (DRA) and 1 × 4 array of negative refractive index metamaterial (NRIM) unit-cells fixed above the DRA. The proposed NRIM unit-cell comprises of a fractal cross ring resonator structure which performs a negative refractive index during band of 5-5.5 GHz. The direction of propagation and phase can be changed when EM waves entering a media of negative refractive index. Hence, beam-tilting can be implemented by using proposed NRIM unit-cells with DRA. The simulation and experimental results show that DAR can steer the main beam by 38° in the xoz-plane over 5-5.5 GHz band. The S11of antenna is better than -10 dB from 5 GHz to 5.5 GHz. A good agreement is found between the simulated and measured results.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.182
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 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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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