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Miniaturized Diplexer Using Dual Half-Mode SIW Cavity for 5G Sub-6 GHz Communications This paper is dedicated to the Memory of Professor Mojgan Daneshmand and her family

2020· article· en· W3129375824 on OpenAlexaff
Kang Zhou, Ke Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDiplexerMiniaturizationPassbandDual modeComputer scienceSubstrate (aquarium)Channel (broadcasting)OptoelectronicsWaveguideScheme (mathematics)Multi-band deviceElectronic engineeringElectronic circuitElectrical engineeringResonatorBand-pass filterTelecommunicationsPhysicsEngineeringMathematics

Abstract

fetched live from OpenAlex

Prof. Mojgan Daneshmand made seminal contributions to the advancements of substrate integrated waveguide (SIW) technology together with her students and colleagues. In this paper, we present our recent work on miniaturized SIW diplexer in memory of Mojgan and her family. The proposed diplexer is centered at 3.45 and 4.9 GHz for 5G sub-6 GHz communications, which is enabled by a common dual half-mode substrate integrated rectangular cavity (HMSIRC) coupled with singlemode cavities in each channel. Besides the elimination of T-junction, HMSIRCs are also introduced for the miniaturization of sub-6 GHz circuits. Additionally, by employing the first two modes in the dual-HMSIRC, a large frequency ratio of 1.42 between 3.45 and 4.9 GHz can be realized, which is difficult to implement with the original whole cavity scheme because of spurious passband problems. A 2nd-order example with ripple fractional bandwidths of 2% and 1.6% for the two channels is synthesized, designed, fabricated, and tested for demonstration.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.036
GPT teacher head0.265
Teacher spread0.230 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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