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Record W2100816156 · doi:10.5815/ijitcs.2012.12.01

Analogue Wavelet Transform Based the Solution of the Parabolic Equation

2012· article· en· W2100816156 on OpenAlexaff
Jean-Bosco Mugiraneza, Amritasu Sinha

Bibliographic record

VenueInternational Journal of Information Technology and Computer Science · 2012
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHarmonic wavelet transformWaveletSecond-generation wavelet transformDiscrete wavelet transformWavelet transformComputer scienceFast Fourier transformStationary wavelet transformAlgorithmConvolution (computer science)Fourier transformWavelet packet decompositionContinuous wavelet transformMathematical analysisMathematicsComputer visionArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper we have proved that the solution of parabolic equation and its Fast Fourier Transform generate continuous wavelet transforms.Indeed, we have solved the parabolic equation using PDETool, exported its solution and coefficients to Matlab workspace.We have then imported the solution fro m workspace to signal processing tool.We have sampled the imported solution with the sampling frequency of 8192Hz and applied the band pass filter with that frequency.The convolution of the sampled PDE solution with the impu lse response of the band pass filter has generated wavelet transform.This algorith m co mputes the wavelet transform either directly of via Faster Fourier Transform.The computation of the FFT of the PDE solution has produced complex wavelet.

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)0.002
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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