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Record W3015043126 · doi:10.1049/iet-map.2020.0047

Dirac leaky wave antenna for millimetre‐wave applications

2020· article· en· W3015043126 on OpenAlexaff
Sina Rezaee, Mohammad Memarian, George V. Eleftheriades

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOpticsBeam steeringBandwidth (computing)Materials scienceOptoelectronicsPhysicsBeam (structure)EngineeringTelecommunications

Abstract

fetched live from OpenAlex

Dirac dispersion cones enable remarkable wave phenomena in electronics as well as electromagnetic systems. In this work, the authors experimentally demonstrate for the first time the Dirac leaky wave antennas (DLWAs) at millimetre‐wave (mm‐wave) frequencies. The demonstrated DLWAs are implemented in the substrate integrated waveguide technology, delivering unprecedented features at high frequencies such as radiation at, and continuous beam scanning through broadside, with ease of fabrication, making these designs well suited for mm‐wave applications such as emerging fifth generation and Internet of Things, radar and imaging. It is shown that a planar Dirac photonic crystal can be realised composed of air columns inside a host SIW waveguide, exhibiting a closed bandgap and linear dispersion around broadside. Phase and attenuation constants are controlled to obtain directive beam and scanning in a wide range of angles (from −30° to 20°). The presented DLWAs have a wide impedance bandwidth around 28 GHz with high efficiency, and operate with peak gains of about 16 dBi with <1 dB gain variation throughout the frequency range from 25 to 31 GHz. Several designs have been proposed, and their prototypes were fabricated using standard substrates. Measured results show excellent agreement with the simulated results, validating the proposed concepts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.024
GPT teacher head0.217
Teacher spread0.194 · 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 teacher head, not a consensus.

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

Citations17
Published2020
Admission routes1
Has abstractyes

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