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Record W2258139555 · doi:10.1049/iet-map.2014.0514

High‐gain end‐fire bow‐tie antenna using artificial dielectric layers

2015· article· en· W2258139555 on OpenAlexaff
Abdolmehdi Dadgarpour, Behnam Zarghooni, Bal S. Virdee, Tayeb A. Denidni

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2015
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsMicrostrip antennaAntenna (radio)Antenna gainPatch antennaAntenna measurementAntenna factorBow tieMaterials scienceCoaxial antennaAntenna efficiencyOptoelectronicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

This study presents a high‐gain bow‐tie antenna for applications in next generation base‐stations operating from 2.5 to 3.9 GHz. The proposed structure consists of three circular sector patches that are attached laterally to the microstrip feed‐line, which is etched on both sides of the common dielectric substrate to create symmetrical bow‐tie antenna. By loading the antenna with a 2 × 5 array of enhanced end‐coupled split‐ring (EECSR) unit‐cells results in significant enhancement in the antenna gain performance. This is because the EECSR unit‐cells behave as parasitic radiators, which is analogous to Yagi–Uda antennas. The EECSR unit‐cells provide a medium of high effective permittivity that effectively reduces the spacing between parasitic directors. As a consequence, a compact and miniature structure is achieved compared with conventional quasi‐Yagi–Uda planar designs. The dimension of EECSR unit‐cell is 33 × 36 mm 2 with inter‐element spacing of 0.075 λ 0 at 3.5 GHz. A prototype of the antenna was fabricated and its performance was measured to validate the simulation results. The measured peak gain of the antenna with 4 × 5 array of EECSR is 12.65 dBi at 3.73 GHz, constituting a peak gain enhancement of 7.45 dBi in the Worldwide Interoperability for Microwave Access band compared with an equivalent conventional bow‐tie antenna.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.230
Teacher spread0.204 · 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 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

Citations17
Published2015
Admission routes1
Has abstractyes

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