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New behavioral-level simulation technique for RF/microwave applications. Part II: Approximation of nonlinear transfer functions

2000· article· en· W2023112027 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

VenueInternational Journal of RF and Microwave Computer-Aided Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsNonlinear systemTransfer functionElectronic engineeringRepresentation (politics)MicrowaveComputer scienceSeries (stratigraphy)Filter (signal processing)Noise (video)Electronic circuitControl theory (sociology)AlgorithmEngineeringTelecommunicationsPhysicsElectrical engineering

Abstract

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The instantaneous quadrature technique is an efficient tool for nonlinear behavioral-level simulation of RF/microwave circuits or systems over wide frequency and dynamic ranges. In order to obtain accurate simulation results, accurate approximation/representation of the nonlinear transfer functions (or factors) as well as accurate measurement (or circuit-level simulation) of the amplitude (AM–AM) and phase (AM–PM) nonlinearities are required. In this paper, we consider how to approximate these transfer functions (factors) using splines, orthogonal and nonorthogonal series expansions, and evolutionary programming techniques (genetic algorithm and neural networks) with viewpoint of the simulation accuracy. The influence of AM–AM and AM–PM measurement (or simulation) inaccuracy and noise on the entire simulation accuracy is also discussed. Series expansion methods are proposed as a tool to filter out the measurement noise. © 2000 John Wiley & Sons, Inc. Int J RF and Microwave CAE 10: 238–252, 2000.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.701
Threshold uncertainty score1.000

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)0.0000.000

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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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