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Record W4232357354 · doi:10.1109/eumc.2007.4405469

A novel hybrid analysis approach for subharmonic self-oscillating mixers combining volterra series and conversion matrix methods

2007· article· en· W4232357354 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venue2007 European Microwave Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaÖsterreichischen Akademie der Wissenschaften
KeywordsSubharmonicVolterra seriesSeries (stratigraphy)Computer scienceControl theory (sociology)Matrix (chemical analysis)Mixing (physics)Electronic engineeringAlgorithmNonlinear systemEngineeringPhysicsArtificial intelligenceMaterials science

Abstract

fetched live from OpenAlex

In this paper, a novel analysis method for subharmonic self-oscillating mixers (SOMs) is presented. It suggests a hybrid approach that effectively combines a Volterra series analysis describing the oscillating steady-state condition and a conversion matrix method analysing the higher-order mixing phenomena. The proposed scheme eliminates the limitation of increasingly complex analytical description for high orders and is therefore an excellent choice for high-order subharmonic SOMs. This method is applied to a balanced subharmonic SOM circuit and yields an excellent performance prediction.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.702
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.029
GPT teacher head0.283
Teacher spread0.254 · 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