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Record W2111939328 · doi:10.1109/aps.2010.5562302

A dual band Wi-Fi antenna using a metamaterial CSRR matching particle

2010· article· en· W2111939328 on OpenAlexaff
Michael Selvanayagam, Debraj Choudhury, George V. Eleftheriades

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMulti-band deviceBroadbandMetamaterialComputer scienceAntenna (radio)Matching (statistics)Radio spectrumSplit-ring resonatorMicrostripFrequency bandMulti bandPhysicsElectronic engineeringTelecommunicationsOpticsEngineeringMathematics

Abstract

fetched live from OpenAlex

Dual band antennas are important for many different communication protocols such as Wi-Fi which works at two frequency bands, 2.4-2.5 GHz and 5.15-5.85 GHz. While there are many techniques published to design a dual band antenna, one possible way is to use a matching network. To achieve the required broadband matching at two different bands a novel double-tuned, dual-band, matching network is herein proposed. First the theory of double-tuned, dual-band matching is described. Subsequently, the method is applied to match a compact planar monopole to the WiFi bands using a compact Complementary Split-Ring-Resonator(CSRR) microstrip line as the matching network. By integrating this dual-band matching network with the monopole, the antenna as a whole is designed to cover the two different frequency bands.

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 score0.005

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 communication0.0010.000
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 head0.014
GPT teacher head0.220
Teacher spread0.206 · 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
Published2010
Admission routes1
Has abstractyes

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