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Record W1997022610 · doi:10.1163/156939300x00798

Using the Complex Images Method To Analyze Printed Antennas in Multilayer Dielectric Media

2000· article· en· W1997022610 on OpenAlexaff
Ashraf Badawi, A. Sebak

Bibliographic record

VenueJournal of Electromagnetic Waves and Applications · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMicrostripElectrical conductorComputer scienceDielectricTransformation (genetics)Integral equationMicrostrip antennaBoundary (topology)Numerical analysisSTRIPSMathematical analysisAcousticsAntenna (radio)OpticsMathematicsAlgorithmTelecommunicationsPhysicsElectrical engineeringOptoelectronicsEngineering

Abstract

fetched live from OpenAlex

This paper presents an efficient full wave numerical analysis method for three dimensional microstrip structures inside a stratified media. The method can analyze both vertical and horizontal components of the currents. The conducting structures are modeled using a mixed potential integral equation. The boundary conditions on the interfaces between dielectric layers are satisfied through the use of appropriate dyadic Green's functions in the spectral domain. The numerical evaluation of the Sommerfeld integrals that are encountered during the transformation of the Green's function to the spatial domain is avoided using the complex images method (CIM). A modified CIM is proposed for efficient solution of vertical conductors currents. The modification maintains the rigorous nature of the full-wave analysis method while achieving the same efficiency for both vertical and horizontal components of currents. The resulting algorithm is found to be a versatile and efficient numerical analysis tool for microstrip antennas. The accuracy of the method is tested against commercially available full wave analysis packages. Good agreement is obtained for a wide class of stacked microstrip antennas.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.532

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 head0.017
GPT teacher head0.306
Teacher spread0.289 · 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

Citations5
Published2000
Admission routes1
Has abstractyes

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