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Record W4323767281 · doi:10.1109/tmtt.2023.3251567

Substrate Integrated Waveguide Filter With Flexible Mixed Coupling

2023· article· en· W4323767281 on OpenAlexaff
Peng Chu, Peng Zhu, Jianguo Feng, Lei Guo, Long Zhang, Fang Zhu, Leilei Liu, Guo Qing Luo, Ke Wu

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
FundersNanjing University of Posts and TelecommunicationsNational Natural Science Foundation of China
KeywordsCoupling (piping)MicrowaveWaveguideWaveguide filterFilter (signal processing)Substrate (aquarium)Electronic engineeringBand-pass filterElectronic circuitMaterials scienceComputer scienceTopology (electrical circuits)Prototype filterOptoelectronicsFilter designEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

This article investigates flexibly using mixed coupling in substrate integrated waveguide (SIW) filters to efficiently improve selectivity. To this end, a mixed coupling structure is proposed, which can be used as a universal coupling module to efficiently produce transmission zeros (TZs) in an inline/conventional SIW filter without degrading the superiority of SIW. On this basis, a trisection with mixed cross-coupling is further proposed, which encloses all the structures inside two general SIW cavities and can be used as a universal cavity module to efficiently produce a quasi-elliptic response in an inline/conventional SIW filter without degrading the superiority of SIW. Three prototypes are designed, fabricated, and measured, and the results agree with the expectations well. The proposed techniques should facilitate the development of high-performance SIW filters in microwave/wireless circuits and systems.

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 score0.003

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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

Citations54
Published2023
Admission routes1
Has abstractyes

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