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Record W2161091631 · doi:10.1109/temc.2009.2020297

Mapping of Equivalent Currents on High-Speed Digital Printed Circuit Boards Based on Near-Field Measurements

2009· article· en· W2161091631 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Electromagnetic Compatibility · 2009
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsRegularization (linguistics)Equivalent circuitElectronic engineeringField (mathematics)Inverse problemPrinted circuit boardComputer scienceElectromagnetic compatibilityAlgorithmInverseElectrical engineeringEngineeringMathematicsMathematical analysisGeometryVoltageArtificial intelligence

Abstract

fetched live from OpenAlex

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, a method to build equivalent models of radiating printed circuit boards based on complex E-field measurements taken in the close vicinity of the device under test is explored. It is shown that the inverse problem to be solved to retrieve the currents from the field data is ill-posed. An innovative regularization approach implementing a penalty on abrupt spatial variations of the currents is proposed to alleviate this difficulty. Various schemes to mesh the equivalent current distribution are also explored. Combining these with measurements, it is shown that accurate estimation of equivalent current models can be achieved, thereby allowing the identification of the emission sources. The method is tested for different circuit configurations with both synthetic and real data. Obtained results demonstrate the efficiency of the method. </para>

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.242
Teacher spread0.208 · 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