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Record W2913985120 · doi:10.1049/iet-map.2018.5902

Compact cross‐coupled filters based on isosceles right triangular and equilateral triangular SIW cavities

2019· article· en· W2913985120 on OpenAlexaff
Senshen Deng, Feng Xu, Peng Chu, Ke Wu

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsIsosceles triangleEquilateral triangleComputer sciencePhysicsTopology (electrical circuits)EngineeringMathematicsGeometryElectrical engineering

Abstract

fetched live from OpenAlex

Two kinds of classical triangular metric resonant cavities including the isosceles right triangular and equilateral triangular metric cavities are investigated carefully. A comprehensive and rigorous derivation process of these two resonant cavities is proposed. Based on this, the mode function and the transverse field components for the modes in the triangular substrate‐integrated waveguide (SIW) cavities can be provided. Then the resonant frequency of both kinds of triangular SIW cavities can be calculated. Combined it with the coupling matrix, a fourth‐degree filter implemented of isosceles right triangular SIW cavities and a sixth‐degree filter formed by equilateral triangular SIW cavities are designed, fabricated and measured in detail. The measured results are in line with the simulated ones. The frequency responses of both filters show a pair of transmission zeroes at each side of the pass band due to the cross‐coupling structure. It also verifies the validity of the derivation in this study. Compared with the rectangular and circular resonant ones, the construct of the triangular cavity has the advantage of much more compact. Besides, the proposed filters based on the triangular cavity also have their own advantages of low cost, good selectivity and easy integration with other planar circuits.

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: Empirical
Teacher disagreement score0.738
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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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