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Record W2795455553 · doi:10.1109/tap.2018.2823865

Efficient Millimeter-Wave Antenna Based on the Exploitation of Microstrip Line Discontinuity Radiation

2018· article· en· W2795455553 on OpenAlexaff
Yazan Al-Alem, Ahmed A. Kishk

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2018
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsPhysicsBandwidth (computing)MicrostripComputer scienceCenter frequencyExtremely high frequencyMicrostrip antennaClassification of discontinuitiesOpticsAntenna (radio)TelecommunicationsMathematicsMathematical analysisBand-pass filter

Abstract

fetched live from OpenAlex

In this paper, a new design perspective utilizes microstrip line discontinuities radiation losses to create an efficient, high-gain millimeter-wave low-profile antenna at 60 GHz. A very compact structure with dimensions of 1.14λo×0.82λoand a substrate thickness of 0.0508λois proposed. A gain of 11.5 dBi boresight and a 10 dB return loss relative bandwidth of 3.66% (equivalent to 2.2 GHz of bandwidth) have been achieved. Qualitative analysis with simple transmission line theory was used to model the structure. An increase in the relative bandwidth to 11.67%, covering the 60 GHz ISM band (57-64 GHz), has been achieved by tuning the antenna to resonate at multiple resonances within the band of interest. A very high efficiency of 98% has been achieved. The proposed antenna possesses a significant advantage, where it can be employed in a linear antenna array, with a center-to-center distance between adjacent elements greater than half of the free-space wavelength, without producing grating lobes, this reflects positively on reducing the mutual coupling between elements, and gives higher flexibility in the design of feeding network.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.218
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations40
Published2018
Admission routes1
Has abstractyes

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