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Record W2021594309 · doi:10.1109/tmtt.2007.910059

Finite-Difference Time-Domain Modeling of Periodic Guided-Wave Structures and Its Application to the Analysis of Substrate Integrated Nonradiative Dielectric Waveguide

2007· article· en· W2021594309 on OpenAlexaff
Feng Xu, Ke Wu, Wei Hong

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2007
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsFinite-difference time-domain methodDielectricWaveguideResonatorWave propagationOpticsMaterials sciencePhysicsElectronic engineeringOptoelectronicsEngineering

Abstract

fetched live from OpenAlex

The finite-difference time-domain (FDTD) method incorporating an equivalent resonant cavity model is presented for the modeling and analysis of guided-wave propagation characteristics of complex periodic structures. By transforming electromagnetic field variables into a new set of periodic variables, which can also be resolved from the Maxwell's equations, one can convert a periodic guided-wave problem into an equivalent resonator problem. Thus, the FDTD method used for a resonant cavity problem can be adopted to simulate periodic guided-wave structures. In addition, the proposed FDTD algorithm can be extended to model lossy periodic propagation problems. In this study, the substrate integrated nonradiative dielectric waveguide, which is a special type of periodic guided-wave structure subject to a potential leakage loss due to its periodic gaps, is investigated as a showcase. The proposed method is first validated and is then used to analyze the guided-wave characteristics of substrate integrated nonradiative dielectric waveguides. It is shown that the substrate integrated nonradiative dielectric waveguide structure, which can easily be fabricated in planar form, has a well-behaved propagation property suitable for high-performance millimeter-wave circuit design.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.579
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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

Citations28
Published2007
Admission routes1
Has abstractyes

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