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Record W2966849687 · doi:10.1109/imbioc.2019.8777737

Design of 1:4 Power Divider Using Artificial Magnetic Conductor Packaging for Millimeter-wave Application

2019· article· en· W2966849687 on OpenAlexfundno aff
Jun Jin, Feng Xu

Bibliographic record

Venue2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Jiangsu Province
KeywordsMicrostripPower dividers and directional couplersConductorExtremely high frequencyTransmission lineElectric power transmissionInsertion lossPower (physics)Electrical engineeringMillimeterRouting (electronic design automation)Electrical conductorMaterials scienceElectronic engineeringEngineeringOpticsTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

A novel design of 1:4 power divider with equal amplitude and phase using artificial magnetic conductor (AMC) packaging is proposed for millimeter-wave signal distribution and routing application. The prototype of the novel design has been fabricated and simulated. Furthermore, the performance of the proposed design is compared with the conventional microstrip line (MSL) 1:4 power divider. The results show meaningful improvement that the design can effectively minimize the insertion loss of output ports since the transmission line is surrounded by mushroom-like AMC periodic structure which generates a stop band, suppressing cavity modes and radiated, leaky, and surface wave.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.039
GPT teacher head0.252
Teacher spread0.213 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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