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Record W2020023303 · doi:10.1155/2014/105625

Performance Investigations of Quasi-Yagi Loop and Dipole Antennas on Silicon Substrate for 94 GHz Applications

2014· article· en· W2020023303 on OpenAlexaff
Osama M. Haraz, Mohamed Abdel‐Rahman, Najeeb Al‐Khalli, Saleh A. Alshebeili, A. Sebak

Bibliographic record

VenueInternational Journal of Antennas and Propagation · 2014
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsConcordia University
FundersKing Abdulaziz City for Science and TechnologyKing Saud University
KeywordsDipole antennaCoplanar waveguideAntenna (radio)Directional antennaLoop antennaDipoleOptoelectronicsReflector (photography)Electronic engineeringPhysicsOpticsMaterials scienceEngineeringElectrical engineeringMicrowaveTelecommunicationsCoaxial antenna

Abstract

fetched live from OpenAlex

This paper introduces the design and implementation of two high gain Quasi-Yagi printed antennas developed on silicon substrate for 94 GHz imaging applications. The proposed antennas are based on either driven loop or dipole antennas fed by a coplanar waveguide (CPW) feeding structure. For better matching with the driven antennas, a matching section has been added between the CPW feedline and the driven antenna element. To improve the gain of either loop or dipole antennas, a ground reflector and parasitic director elements have been added. Two Quasi-Yagi antenna prototypes based on loop and dipole antenna elements have been fabricated and experimentally tested using W-band probing station (75–110 GHz). The measured results show good agreement with simulated results and confirm that the proposed antennas are working. In addition, a feed and matching configuration is proposed to enable coupling a microbolometer element to the proposed Quasi-Yagi antenna designs for performing radiation pattern measurements.

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: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.370

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 head0.013
GPT teacher head0.234
Teacher spread0.220 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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