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Record W2990160195 · doi:10.23919/eumc.2019.8910700

Dual-Band Bandpass SIW Resonator Filter with Flexible Frequency Ratio

2019· article· en· W2990160195 on OpenAlexaff
Wentao Lin, Tae‐Hak Lee, Ke Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsResonatorBand-pass filterMulti-band deviceRealization (probability)Coupling (piping)Flexibility (engineering)Coupling coefficient of resonatorsMaterials scienceFilter (signal processing)AcousticsOptoelectronicsElectronic engineeringPhysicsElectrical engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

A technique for the design of dual-band bandpass filters (DB-BPFs) having flexible frequency ratio is presented through the use of SIW resonators. This flexibility of frequency ratio is enabled by selecting appropriate length-over-width ratios (LoWR) of rectangular resonators. With a choice of two of the three resonant modes generated in the same cavity, named TE110, TE210 and TE120, DB-BPFs can be realized with the minimum LoWR, which is more suitable for a physical realization. To accurately achieve the desired couplings between the two modes over different frequency bands, a multilayer technique is adopted in this work. The coupling slots etched between layers can independently control the internal coupling coefficients. For experimental verification, a SIW resonators-based DB-BPF exhibiting 1.15:1 frequency ratio was prototyped. Two bands operate at 6.80 GHz and 7.80 GHz, respectively, and measured results are found to agree well with simulated ones.

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)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.179
Teacher spread0.173 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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