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Record W3116866699 · doi:10.1002/jnm.2853

Chebyshev polynomials for the numerical modeling of non‐uniform substrate integrated waveguides

2020· article· en· W3116866699 on OpenAlexaff
Karima Rabaani, Maha Added, Noureddine Boulejfen, Ammar B. Kouki, Fadhel M. Ghannouchi

Bibliographic record

VenueInternational Journal of Numerical Modelling Electronic Networks Devices and Fields · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of CalgaryÉcole de Technologie Supérieure
Fundersnot available
KeywordsChebyshev polynomialsChebyshev filterLossy compressionMethod of moments (probability theory)Convergence (economics)Matrix (chemical analysis)Scattering parametersTransmission lineFinite element methodBasis functionAlgorithmComputer scienceTopology (electrical circuits)Mathematical analysisMathematicsElectronic engineeringMaterials sciencePhysicsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract In this paper, a new method‐of‐moments‐based approach is proposed for the analysis of non‐uniform lossy substrate integrated waveguides (SIW) transmission lines. The approach incorporates Chebyshev expansion in the frequency domain to compute the scattering parameter matrix of the line. To validate the proposed approach of non‐uniform structures are analyzed where two of them have been fabricated and measured. The analytical and measured S parameters were compared to those obtained through electromagnetic finite element‐based simulations. The good match observed between the two sets of results for a relatively reasonable number of basis functions confirms the accuracy and the fast convergence of the proposed approach. This makes it the most suitable for integration into computer‐aided design tools.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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Same venueInternational Journal of Numerical Modelling Electronic Networks Devices and FieldsSame topicMicrowave Engineering and WaveguidesFrench-language works237,207