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Record W2982313829 · doi:10.1109/tap.2019.2948682

Leaky-Wave Antenna Featuring Stable Radiation Based on Multimode Resonator (MMR) Concept

2019· article· en· W2982313829 on OpenAlexaff
Dongze Zheng, Ke Wu

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2019
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsResonatorAntenna (radio)RadiationPhysicsOpticsComputer scienceElectronic engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this article, a scheme based on the multimode resonator (MMR) concept is proposed and demonstrated for the design and development of leaky-wave antennas (LWAs) to realize stable radiation properties (e.g., stable radiation efficiency and gain). Firstly, two kinds of traditional LWA unit cells possessing typical single-mode resonator and monotonic changing radiation behaviors are theoretically modeled and analyzed. The condition for LWAs featuring stable radiation properties is then derived, based on which the MMR concept is proposed as a solution for radiation stability. For demonstration purposes, two types of MMR-based unit cells and their associated 1-D periodic LWAs are studied and developed for millimeter-wave applications. One type is based on the magnetoelectric dipole, while the other originates from the aperture-coupled patch antenna. To facilitate the implementation, a detailed design procedure for this class of LWAs is provided in a general manner. The proposed two MMR-based periodic LWAs are modeled, fabricated, and measured. The simulated and measured results are in a reasonable agreement and both illustrate the stability of radiation efficiency and gain performances, thereby demonstrating the correctness and effectiveness of the proposed MMR design concept.

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 score0.002

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 communication0.0010.001
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 head0.011
GPT teacher head0.203
Teacher spread0.191 · 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".

Quick stats

Citations35
Published2019
Admission routes1
Has abstractyes

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