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Record W2987606961 · doi:10.1109/vtcfall.2019.8891329

Parasitic Slot Elements for Bandwidth Enhancement of Slotted Waveguide Antennas

2019· article· en· W2987606961 on OpenAlexaff
Maryam Razmhosseini, Roshanak Zabihi, Rodney G. Vaughan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsSierra Wireless (Canada)Simon Fraser University
Fundersnot available
KeywordsWaveguideSlotted waveguideBandwidth (computing)Slot antennaComputer scienceBiconical antennaElectrical impedanceAcousticsDirectional antennaAntenna (radio)Electronic engineeringOpticsPhysicsElectrical engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

The use of extra slots in a slotted waveguide array for improving the impedance bandwidth is investigated. This has not been widely studied previously, probably because the directive pattern is a primary parameter for the waveguide array and extra elements are prone to spoiling the control of the pattern. However, the pattern is not a primary design parameter for most mobile terminals, and here we seek a wide bandwidth for a smaller antenna than the traditional waveguide array, which is suitable for a mobile terminal. The extra slots can be viewed as parasitic elements, analogously to free-space thin wire parasitic, but the designs are not interchangeable with wire antennas. This is because the waveguide - both the internal and external structure - constrains the excitation of the slot elements in a different way to that of the wire antenna analogies. Simple-to-build designs are sought where the slot elements are on only one of the waveguide broad walls. The approach is experimental (simulation) which allows rapid and accurate evaluation of different configurations. We do not optimize a configuration because any optimal design will depend on the specific structure of the mobile terminal supporting the antenna. For the basic stand-alone waveguide, a -6dB impedance bandwidth of about ~30% is demonstrated using four slot elements in a short waveguide.

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 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: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.638

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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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.

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

Citations9
Published2019
Admission routes1
Has abstractyes

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