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Record W4401991754 · doi:10.3390/electronics13173434

On the Behavior of a Non-Linear Bandpass Filter with Self Voltage-Controlled Resistors

2024· article· en· W4401991754 on OpenAlexafffund
Ahmed S. Elwakil, Mohamed B. Elamien, Anis Allagui, Brent Maundy, Costas Psychalinos

Bibliographic record

VenueElectronics · 2024
Typearticle
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsMcMaster UniversityUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsResistorBand-pass filterVoltageControl theory (sociology)Materials scienceElectronic engineeringElectrical engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

In this work, we explore the behavior of a classical RLC resonance-based bandpass filter, which includes two resistors (one of which is associated with a non-ideal inductor), when either of these resistors is self voltage-controlled. In particular, self-feedback control is achieved by using the voltage developed across the inductor or the capacitor to dynamically change the value of the controlled resistor. This results in a multiplication-type non-linearity, which transforms the linear filter into a non-linear filter described by a set of non-linear differential equations. When gradually increasing the strength of the non-linearity, a notch-like behavior is observed at twice the resonance frequency. However, the non-linear filter can lose its stability with excessive feedback. Simulations and experimental results are provided to support the theory.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes2
Has abstractyes

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