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Record W2099024697 · doi:10.1109/aps.2008.4619753

A novel variant 60-GHz CPW-fed patch antenna for broadband short range wireless communications

2008· article· en· W2099024697 on OpenAlexaff
Khelifa Hettak, G.Y. Delisle, G.A. Morin, S. Toutain, M.G. Stubbs

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsDefence Research and Development CanadaCommunications Research Centre CanadaUniversity of Ottawa
Fundersnot available
KeywordsAntenna efficiencyMicrostrip antennaPatch antennaCoplanar waveguideStub (electronics)Coaxial antennaAntenna measurementAntenna factorOptoelectronicsMaterials scienceElectrical engineeringElectronic engineeringComputer sciencePhysicsAntenna (radio)TelecommunicationsEngineeringMicrowave

Abstract

fetched live from OpenAlex

A 60 GHz coplanar waveguide (CPW) fed patch antenna is proposed. This antenna is implemented on a high dielectric constant substrate (epsiv <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> = 9:9), which is close to the dielectric constant of commercial GaAs and CMOS process. The antenna structure combines the advantages of CPW with those of the aperture-coupled microstrip antenna and simplifies the structure of the antenna by reducing the number of metallization level, from three down to two. This feed design eliminates the competition for surface space between the antenna elements and the feed network. The proposed antenna demonstrates the efficiency of the approach based upon CPW series stub within a center conductor in order to get better performance and compactness. It has been shown that the use of CPW series stub printed on the center conductor is potentially effective to ensure that the antenna works at high efficiency in millimeter waves. Finally, the proposed antenna shows broad band characteristics such as 10 dB bandwidth of 4.7 GHz, from 58.3 GHz to 63 GHz.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.627

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 head0.041
GPT teacher head0.244
Teacher spread0.203 · 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 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".

Quick stats

Citations11
Published2008
Admission routes1
Has abstractyes

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