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Record W1970587913 · doi:10.1155/2015/209430

Study the Effect of Using Low-Cost Dielectric Lenses with Printed Log-Periodic Dipole Antennas for Millimeter-Wave Applications

2015· article· en· W1970587913 on OpenAlexaff
Osama M. Haraz, A. Sebak, Saleh A. Alshebeili

Bibliographic record

VenueInternational Journal of Antennas and Propagation · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsOpticsRadiation patternDipole antennaAntenna measurementMicrostrip antennaAntenna gainPhysicsAntenna (radio)Extremely high frequencyAntenna efficiencyOptoelectronicsMaterials scienceElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Design of V-band high-gain printed log periodic dipole array (PLPDA) antenna loaded with a low-cost spherical dielectric lens is introduced. The proposed antenna consists of microstrip-line-fed log-periodic dipole antenna designed to operate in the V-band with a peak gain of 12.64 dBi at 60 GHz. To enhance the antenna gain, a dielectric lens is installed. The antenna prototype is fabricated and then tested experimentally using Agilent E8364B PNA Network Analyzer. Experimental results agree well with the simulated ones. The simulated results show that the proposed antenna can work from 42 GHz up to 82 GHz with a fractional impedance bandwidth of 64.5% covering the whole V-band (50–75 GHz). At 60 GHz, the proposed antenna has peak gain of 26.79 dBi with a gain variation of 3.5 dBi across the whole V-band with stable radiation patterns over the operating band. The proposed PLPDA antenna achieves good side-lobe suppression, excellent front-to-back ratio in both E - and H -planes, and low cross-polarization levels over the entire frequency range. These unique features will make this antenna suitable for different interesting applications such as millimeter-wave radar and imaging applications.

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 categoriesnone
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 score0.514
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

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

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 head0.025
GPT teacher head0.262
Teacher spread0.237 · 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.

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

Citations11
Published2015
Admission routes1
Has abstractyes

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