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Record W2130784653 · doi:10.1109/lmwc.2005.859007

A broadband series power divider using zero-degree metamaterial phase-shifting lines

2005· article· en· W2130784653 on OpenAlexaff
Marco A. Antoniades, George V. Eleftheriades

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2005
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWilkinson power dividerPower dividers and directional couplersMetamaterialElectric power transmissionReturn lossBroadbandBandwidth (computing)Current dividerTransmission lineElectrical engineeringMetamaterial antennaPhysicsInsertion lossFrequency dividerTelecommunicationsOpticsEngineeringDipole antennaSlot antenna

Abstract

fetched live from OpenAlex

A metamaterial 1:4 series power divider that provides equal power split to all four output ports over a large bandwidth is presented, which can be extended to an arbitrary number of output ports. The divider comprises four nonradiating metamaterial lines in series, incurring a zero insertion phase over a large bandwidth, while simultaneously maintaining a compact length of /spl lambda//sub 0//8. Compared to a series power divider employing conventional one-wavelength long meandered transmission lines to provide in-phase signals at the output ports, the metamaterial divider provides a 165% increase in the input return-loss bandwidth and a 155% and 154% increase in the through-power bandwidth to ports 3 and 4, respectively. In addition, the metamaterial divider is significantly more compact, occupying only 2.6% of the area that the transmission line divider occupies. The metamaterial and transmission line dividers exhibit comparable insertion losses.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.282
Teacher spread0.226 · 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 designSimulation or modeling
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

Citations183
Published2005
Admission routes1
Has abstractyes

Explore more

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