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Record W2040645736 · doi:10.5539/cis.v6n3p118

Application of Wavelet Transform Analysis to ADCs Harmonics Distortion

2013· article· en· W2040645736 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueComputer and Information Science · 2013
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHarmonic wavelet transformComputer scienceWavelet transformWaveletTotal harmonic distortionDistortion (music)HarmonicsDiscrete wavelet transformFourier transformAlgorithmHarmonicComputationSecond-generation wavelet transformStationary wavelet transformConstant Q transformPower (physics)MathematicsArtificial intelligenceAcousticsTelecommunicationsPhysicsElectrical engineeringMathematical analysisEngineering

Abstract

fetched live from OpenAlex

This paper presents a new method of detecting Analog to Digital Converter harmonic distortion. The new method is based on Wavelet multi-resolution process to identify instantaneous harmonic components. In classical testing, Fourier transform algorithm was long adopted to estimate Total Harmonic Distortion by obtaining signal power spectrum. While the conventional method of Fourier transform tend to be complicated and lengthy, the new investigated algorithms of Wavelet transform has shown less computations process and instantaneous testing of ADCs harmonic distortions. By shorten testing times and reduced computation complexity, Wavelet transform can be particularly appropriate for developing ADCs low-cost, and fast testing procedure.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.004
GPT teacher head0.232
Teacher spread0.228 · 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