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Record W3133623684 · doi:10.1109/tap.2021.3060893

Millimeter-Wave Integrated Side-Fire Leaky-Wave Antenna and its Application as a Spectrum Analyzer

2020· article· en· W3133623684 on OpenAlexafffund
Daniel J. King, Mohamed K. Emara, Shulabh Gupta

Bibliographic record

VenuearXiv (Cornell University) · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsSpectrum analyzerLeaky wave antennaShielded cableExtremely high frequencyAntenna (radio)BroadbandPhysicsAcousticsMaterials scienceMicrostrip antennaComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

An analog, low-profile and shielded spectrum analyzer is proposed for operation at mm-wave frequencies around the 60 GHz band based on a novel side-fire Leaky-Wave Antenna (LWA) configuration. The proposed side-fire periodic LWA is systematically developed from a conventional 3-port waveguide T-junction which is modified to a LWA unit cell with an internal matching mechanism to suppress the stop-band and enable broadside radiation based on unit cell symmetry considerations. The resulting periodic side-fire antenna radiates in the plane of the antenna, whereby the leakage power be either be allowed to radiate in free-space or kept confined inside a PPW structure. The proposed side-fire structure thus can be completely shielded useful as an analog broadband spectrum analyzer using Substrate Integrated Waveguide (SIW) Technology. Furthermore, a convex side-fire antenna is demonstrated to focus the radiated beams in the near-field of the structure to make the entire system compact. The integrated spectrum analyzer is experimentally demonstrated between 59 GHz - 66 GHz providing 1 GHz frequency resolution. Furthermore, a simple mathematical model consisting of array of line sources is proposed to efficiently model the beam-scanning characteristics of the curved side-fire LWA in the near-field of the structure.

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 categoriesMeta-epidemiology (narrow)
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 score0.526
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.038
GPT teacher head0.157
Teacher spread0.119 · 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.

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

Citations10
Published2020
Admission routes2
Has abstractyes

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