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Record W2735587232 · doi:10.1109/access.2017.2711958

High-Q Substrate Integrated Waveguide Resonator Filter With Dielectric Loading

2017· article· en· W2735587232 on OpenAlexafffund
Farouk Grine, Tarek Djerafi, Mohamed Taoufik Benhabiles, Ke Wu, Mohamed Lahdi Riabi

Bibliographic record

VenueIEEE Access · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique MontréalInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsResonatorQ factorPlanarMaterials scienceBandwidth (computing)Waveguide filterPermittivityWaveguideMicrowaveOptoelectronicsFilter (signal processing)Microwave cavityDielectricHelical resonatorBand-pass filterSubstrate (aquarium)Prototype filterElectronic engineeringComputer scienceLow-pass filterElectrical engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

A planar cavity is proposed, designed, and implemented with a hybrid substrate integrated waveguide (SIW) and periodically drilled SIW (PDSIW) structure. Air holes are added to synthesize a lower effective permittivity. The periodicity can increase the stored energy and improve the quality factor. The optimal ratio between the two permittivities is investigated. The measured results show that the proposed resonator has an unloaded quality factor (Qu) of 815, which is 53% higher than its standard SIW counterpart. To demonstrate the potential of the proposed PDSIW cavity, a third-order filter is designed and implemented through the proposed cavity, and it is then compared with filters of different orders. The new filter with a cavity shows performance enhancements in terms of both loss and bandwidth. The SIW resonator can be a promising element for use in the design of high-performance microwave filters and oscillators.

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 score0.326
Threshold uncertainty score0.916

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 communication0.0010.001
Open science0.0010.000
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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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

Citations22
Published2017
Admission routes2
Has abstractyes

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