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Record W4407361735 · doi:10.23919/emc.2002.10879851

Characterizing the Statistics of Electromagnetic Interference in Complex Topologies

2002· article· en· W4407361735 on OpenAlexaff
Joe LoVetri, Serguei Primak, Alan Keen, Elizabeth M. Davenport

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsWestern UniversityUniversity of Manitoba
Fundersnot available
KeywordsInterference (communication)Electromagnetic interferenceNetwork topologyComputer scienceStatisticsTopology (electrical circuits)Electronic engineeringTelecommunicationsMathematicsElectrical engineeringEngineeringComputer network

Abstract

fetched live from OpenAlex

The use of statistical methods to describe the electromagnetic interference in electronic systems with complex topologies is considered. The finite difference time domain (FDTD) method is used to generate a large amount of interference data for various systems. These include cavities with apertures as well as partially open enclosures containing conducting objects and a circuit- board with different sized traces. The interfering source is a Gaussian pulse plane wave having a frequency content from DC to well over 8 GHz. Many different attributes of the interference problem are characterized statistically. Probability density functions are fit to various variables in the problem: termination voltages and currents; the magnitude of the electric field intensity; and the different electric field components. It is shown that the statistical nature of the interference depends on the part of the topology which is used to generate the statistics. It is found that standard distributions do a good job of modelling the data.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.212
Teacher spread0.181 · 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 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

Citations0
Published2002
Admission routes1
Has abstractyes

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