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Record W2993122838 · doi:10.24018/ejece.2019.3.6.155

Signal Propagation in Nth LC Ladder Network Using Fibonacci Wave Functions

2019· article· en· W2993122838 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

VenueEuropean Journal of Electrical Engineering and Computer Science · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFractal and DNA sequence analysis
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsFibonacci numberLossless compressionResistive touchscreenResistorPhysicsVoltageElectrical networkMathematicsMathematical analysisTopology (electrical circuits)Electrical engineeringAlgorithmQuantum mechanicsDiscrete mathematicsCombinatoricsEngineering

Abstract

fetched live from OpenAlex

In this paper, new general model for an infinite LC ladder network using Fibonacci wave functions (FWF) is introduced. This general model is derived from a first order resistive-capacitive (RC) or resistive-inductive (RL) circuit. The order Fibonacci wave function of an LC ladder denominator and numerator coefficients are determined from Pascal’s triangle new general form. The coefficients follow specific distribution pattern with respect to the golden ratio. The LC ladder network model can be developed to any order for each inductor current or flux and for each capacitor voltage or charge. Based on this new proposed method, nth order FWF general models were created and their signal propagation behaviors were compared with nth order RC and LC electrical circuits modeled with Matlab-Simulink. These models can be used to represent and analyze lossless transmission lines and other applications such as particles interaction behavior in quantum mechanics, sound propagation model.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.218

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 head0.006
GPT teacher head0.185
Teacher spread0.179 · 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