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Record W2552395611 · doi:10.1109/tmtt.2016.2623618

Advanced Synthesis Technique for Unified Extracted Pole Filters

2016· article· en· W2552395611 on OpenAlexafffund
Yimin Yang, Ming Yu, Qiuyi Wu

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsCOM DEV InternationalUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsFilter (signal processing)Filter designTopology (electrical circuits)NotationNetwork synthesis filtersMathematicsBand-pass filterNode (physics)AlgorithmComputer scienceElectronic engineeringEngineeringCombinatoricsPhysicsArithmeticAcoustics

Abstract

fetched live from OpenAlex

This paper proposes a novel and unified synthesis approach and design procedure for microwave filters with extracted pole and nonresonating node (NRN) technique. By introducing a general circuit topology, the traditional extracted pole and NRN filters can be unified to a general prototype. With the new design process, the exact mapping relationship between the electromagnetic (EM) model of the pole producing element and the circuit model can be achieved. All the physical dimensions of the filter structure can be obtained by a fully analytical approach. No full EM optimization or adjustment is needed in the filter design. A three pole TE$_{\mathrm { {101}}}$mode waveguide filter with one finite transmission zero is designed to illustrate the new procedure. In addition, a seven-pole TE$_{\mathrm { {011}}}$mode filter with four finite transmission zeros covering all three possible types of arrangements is designed and fabricated to demonstrate the versatility of the proposed method. The simulated and measured results verified the efficiency and the accuracy of the proposed design method.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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

Citations74
Published2016
Admission routes2
Has abstractyes

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