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Record W4323519404 · doi:10.1109/ojap.2023.3253132

High Gain Fan-Beam Pattern Antenna Based on the Utilization of Diffracted Fields From Dielectric Slabs Edges for IoT and Sensing Applications

2023· article· en· W4323519404 on OpenAlexaff
Yazan Al-Alem, Syed M. Sifat, Yahia M. M. Antar, Ahmed A. Kishk

Bibliographic record

VenueIEEE Open Journal of Antennas and Propagation · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia UniversityRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsAntenna (radio)Radiation patternReconfigurable antennaPeriscope antennaComputer scienceTaperingBeam waveguide antennaAntenna gainAntenna efficiencyElectrical engineeringElectronic engineeringAcousticsEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

A high gain, low-cost antenna structure is designed for a smart car parking system. The antenna uses Printed Ridge Gap Waveguide (PRGW) technology, which is fully shielded, and suppresses any parasitic radiation from the feed, and minimizes back-lobe radiation. The proposed antenna uses a single MSL feed point, which eliminates the need for any complex feeding network design, and allows the antenna to be integrated easily with PCB transceiver circuitry. A novel technique for the design procedure is proposed, the technique provides a generalization of using either electric or magnetic excitations with dielectric or magnetic rectangular slabs. A unique physical insight accompanied with a thorough analysis of the propagation mechanism is provided. The technique has a significant impact on reducing the complexity of the feeding network. The realized structure is compact, low profile, and low cost. The beam pattern of the antenna is a Fan-Beam (Elliptical) pattern which is well suited for various sensing and Internet of Things (IoT) 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.269

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 head0.029
GPT teacher head0.247
Teacher spread0.218 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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