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Record W2261128250 · doi:10.1155/2015/141807

A Fast Geometric Optics-Based Design Approach for Dielectric Rod Antennas

2015· article· en· W2261128250 on OpenAlexafffund
Jinping Liu, Safieddin Safavi‐Naeini, Ying Wang, Aidin Taeb

Bibliographic record

VenueInternational Journal of Antennas and Propagation · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsOntario Tech UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsAntenna gainAntenna measurementRadiation patternBandwidth (computing)Dipole antennaPhysicsAntenna (radio)Directional antennaAntenna apertureAcousticsEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

A simple and effective dielectric rod antenna design approach based on geometric optics and modal analysis is presented. The tapered antennas from microwave to terahertz can be readily designed with the equations presented in this paper. The radius of antenna is determined by the desired traveling wave mode inside the antenna. The field inside the antenna consists of the fundamental mode and the second mode. For the end-fire operation, there is only the fundamental mode. The length of the antenna is designed based on geometric optics theory so that most of the traveling wave will be converted to the radiating field towards the output plane, avoiding reflection inside the antenna. Such antennas can achieve wide bandwidth. The gain increases with the length of the antenna as long as the diameters and length satisfy the conditions given in this paper. A number of antenna design examples with high gain and wide bandwidth are presented. The measurement results of a 130 GHz rectangular antenna with a length of 4λshow a bandwidth of 60 GHz and a gain of 12 dB.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.240
Teacher spread0.211 · 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
GenreMethods

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

Citations0
Published2015
Admission routes2
Has abstractyes

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