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Record W2399944956 · doi:10.1109/lawp.2016.2571280

A New Aperture Antenna Using Substrate Integrated Waveguide Corrugated Structures for 5G Applications

2016· article· en· W2399944956 on OpenAlexafffund
Mohammad Mahdi Honari, Rashid Mirzavand, Jordan Melzer, Pedram Mousavi

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsTelus (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsMaterials scienceAntenna (radio)Aperture (computer memory)OpticsWaveguideRadiation patternSuperposition principleAntenna apertureAntenna gainOptoelectronicsSubstrate (aquarium)Transmission lineAcousticsElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

In this letter, two new high-gain aperture antennas are presented using substrate integrated waveguide (SIW) corrugated structures. The antenna is excited by a transmission line at the bottom layer. Power is coupled to the patch through a cavity in the middle layer. Due to constructive superposition of the electric field by the grooves and patch, a relatively high-gain and narrow radiation pattern is achieved that depends on the number of grooves. In the proposed structure, three layers of boards for fabricating a resonant transmission line and specific SIW structures are employed instead of a waveguide feeder and grooves on a metallic plate as in conventional corrugated antennas. This technique removes the need to use a metallic layer and thus reduces the weight of the antenna structure. Two samples with two and four grooves have been designed, fabricated, and tested for 5G applications to show the validity of this approach. The results show that these grooves increase the gain of the antenna significantly.

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: none
Teacher disagreement score0.703
Threshold uncertainty score0.816

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 head0.218
Teacher spread0.205 · 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

Citations35
Published2016
Admission routes2
Has abstractyes

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