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Record W2990419078 · doi:10.1109/tmtt.2019.2948856

Quasi-Elliptic Waveguide Dual-Band Bandpass Filters

2019· article· en· W2990419078 on OpenAlexaff
Li Zhu, Raafat R. Mansour, Ming Yu

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBand-pass filterMulti-band deviceWaveguide filterResonatorm-derived filterPhysicsTopology (electrical circuits)Bandwidth (computing)Prototype filterSpurious relationshipFilter (signal processing)Insertion lossCoupling (piping)PassbandElectronic filter topologyComputer scienceOpticsEngineeringLow-pass filterTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

This article presents a novel folded configuration of dual-band filter employing TE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11m</sub> dual-mode elliptical cavity resonators. The proposed side-coupled configuration leads to a significant reduction of filter footprint compared with previously reported in-line dual-band filter structures. Such configuration also facilitates cross couplings to realize advanced quasi-elliptic filter functions. The structure employs irises for both sequential and cross couplings. The limitations of bandwidth realization and spurious-free window are analyzed in detail, and new coupling structures are proposed to improve the overall in-band and out-of-band responses. All the proposed dual-band designs have low insertion loss and allow for independent control of each frequency and coupling parameter. To demonstrate the concept, two 8-pole C-band elliptical cavity dual-band filters are designed, manufactured, measured, and compared. To the best of the authors' knowledge, this is the first reported folded dual-band filter configuration based on metallic waveguide cavities.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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