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Tunable Multiband Devices Based on ON/OFF Switches in Metamaterials SOR for WIFI Application

2019· article· en· W2982646293 on OpenAlexaff
B. Belkadi, Zoubir Mahdjoub, Mohamed Lamine Seddiki, Mourad Nedil

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsStopbandResonatorMetamaterialCoplanar waveguideMicrowaveSplit-ring resonatorAntenna (radio)PassbandWaveguide filterPrototype filterFilter (signal processing)AcousticsElectronic engineeringMaterials scienceBand-pass filterWidebandBand-stop filterTransmission (telecommunications)Center frequencyOptoelectronicsComputer sciencePhysicsElectrical engineeringLow-pass filterEngineeringTelecommunications

Abstract

fetched live from OpenAlex

In this paper, a new design of stopband filters, based on a coplanar waveguide loaded with resonator elements, is presented. The structure is composed of four split octagonal resonators (SOR), with three cases according to the angular position of the gaps created by ON/OFF switches in the inner and outer resonators. The design is based on the use of a metamaterial to create notch filters for microwave applications. From the simulation results, the filters exhibit high frequency selectivity via the presence of transmission zeros. To confirm the generation of the rejection bands, an ultra-wideband (UWB) circular monopole antenna is added to the filter. The extracted data are also compared with filter simulation results, revealing good agreement.

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 score0.003

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.218
Teacher spread0.207 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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