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Record W3158820529 · doi:10.1109/tmtt.2021.3074617

Planar Dual-Mode Bandpass Filters Using Perturbed Substrate-Integrated Waveguide Rectangular Cavities

2021· article· en· W3158820529 on OpenAlexaff
Fang Zhu, Guo Qing Luo, Bin You, Xiaohong Zhang, Ke Wu

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
FundersNational Natural Science Foundation of China
KeywordsBand-pass filterResonatorOffset (computer science)PlanarStriplineBandwidth (computing)PassbandPhysicsDual modeWaveguideTopology (electrical circuits)Pole–zero plotInsertion lossElectronic engineeringComputer scienceOptoelectronicsTransfer functionOpticsElectrical engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This article presents a novel class of dual-mode substrate-integrated waveguide (SIW) bandpass filters exploiting perturbed SIW rectangular cavities. By introducing metallized via-holes along the central line of an SIW rectangular cavity, the resonant frequency of TE101mode can be shifted close to that of TE201mode, thus creating a dual-mode resonator. This structure implements a doublet, which provides two poles and one or two transmission zeros (TZs). The pass bandwidth, spurious-free bandwidth, and positions of the TZs can be fully controlled by modifying the length of the perturbation, the side ratio of the rectangular cavity, and the offset of the input/output ports, respectively. Eight two-pole SIW filters are designed and investigated to demonstrate the high design flexibility. Moreover, a four-pole SIW filter with the asymmetric response and a five-pole SIW filter with quasi-elliptic response are designed, fabricated, and measured to describe the extension of the proposed approach to higher order filters.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.228
Teacher spread0.218 · 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 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

Citations72
Published2021
Admission routes1
Has abstractyes

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