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Record W3008973557 · doi:10.1109/tmtt.2020.2973377

Single-Layer Mode Composite Coplanar Waveguide Dual-Band Filter With Large Frequency Ratio

2020· article· en· W3008973557 on OpenAlexaff
Yihong Su, Yong Fan, Xian Qi Lin, Ke Wu

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsMulti-band devicem-derived filterConstant k filterWaveguide filterFilter (signal processing)Band-stop filterCoplanar waveguideElectronic engineeringMaterials scienceFilter designLow-pass filterImpedance matchingFrequency bandHarmonicsBand-pass filterHigh-pass filterPassbandPrototype filterElectrical impedanceOpticsComputer sciencePhysicsTelecommunicationsEngineeringAntenna (radio)Electrical engineeringMicrowaveVoltage

Abstract

fetched live from OpenAlex

A dual-band filter of a large frequency ratio is proposed, studied, and developed in this article, which is based on mode composite coplanar waveguide (MCCPW). Both low and high bands of the dual-band filter can be designed independently with a high degree of design freedom. The filter can achieve more than ten times the frequency difference between the two bands, which is usually hard to accomplish by conventional dual-band filter design techniques. All harmonics between the two bands can be suppressed by a cascaded low-pass filter and impedance matching structure to form a qualified dual-band frequency response. The characteristic of the proposed MCCPW dual-band filter makes it a promising candidate for multiband systems, especially in 5G communication systems. The demonstrated MCCPW dual-band filter is fabricated with a single-layer substrate. Measured results are in good agreement with simulated results, which validate the feasibility and superiority of the proposed filtering technique.

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 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 score0.876
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.012
GPT teacher head0.213
Teacher spread0.201 · 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

Citations34
Published2020
Admission routes1
Has abstractyes

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