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Record W4229045885 · doi:10.13052/2022.aces.j.370108

Compact Cauliflower-Shaped Antenna for Ultra-Wideband Applications

2022· article· en· W4229045885 on OpenAlexaff
Boumediene Guenad, Abdelhalim Chaabane, Djelloul Aissaoui, Abdelhafid Bouacha, Tayeb A. Denidni

Bibliographic record

VenueThe Applied Computational Electromagnetics Society Journal (ACES) · 2022
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsInstitut National de la Recherche Scientifique
FundersDirection Générale de la Recherche Scientifique et du Développement Technologique
KeywordsAntenna measurementRadiation patternAntenna efficiencyOpticsAnechoic chamberAntenna factorDipole antennaReflection coefficientFractal antennaAntenna (radio)AcousticsPhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

A compact coplanar waveguide (CPW) fed cauliflower-shaped antenna is presented and discussed in this paper. To extend the impedance bandwidth and to improve the impedance matching, fractal geometry having a cauliflower shape is introduced along the edges of the radiator. To validate the simulated results by experimental ones, a prototype of the designed antenna was fabricated on the RO-4350B substrate having a compact size of 0.3623λ0 × 0.41λ0 × 0.01524λ0 at 3 GHz. An Agilent 8722ES vector network was used for the reflection coefficient measurement revealing that the –10 dB bandwidth of the fabricated antenna offers an impedance bandwidth of 113% extending from 3.05 to 10.96 GHz. Besides, the antenna’s radiation patterns are measured in an anechoic chamber showing consistent radiation patterns characteristic over the entire working band. Furthermore, the proposed antenna has a peak gain of around 6 dBi and an average radiation efficiency almost over 90% across the entire operating band. Thus, the proposed antenna could be useful in many modern ultra-wideband (UWB) communication systems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations8
Published2022
Admission routes1
Has abstractyes

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