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Record W2368129884

Parameterization of Wavelet Transforms Based on CORDIC and Their Implemention

2006· article· en· W2368129884 on OpenAlex
Shibin Liu

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

VenueMicrocomputer applications · 2006
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Computational Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCORDICComputer scienceWaveletFast wavelet transformRotation (mathematics)Orthogonal waveletAlgorithmWavelet transformLifting schemeStationary wavelet transformOrthonormalityRecursion (computer science)Discrete wavelet transformSecond-generation wavelet transformOrthonormal basisField-programmable gate arrayArtificial intelligenceComputer hardware
DOInot available

Abstract

fetched live from OpenAlex

This paper introduces a method for parameterizing orthogonal wavelet transforms implement in FPGA based on CORDIC algotithm. To implement the parameterization of wavelet transforms, we need to confirm the parameter space. The parameter space is given by the rotation angles of the orthogonal 2×2 rotations used in the lattice filters realizing the stages of the wavelet transforms. And it is restricted to the set of rotation angles given by simple orthogonalμ-rotations. The experiment shows that an orthogonalμ-rotations is essentially one recursion step of the CORDIC algorithm. Wavelet transforms can be implemented simply by the predigested parameter space, only a small number of shift and add operations is required.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.526
Threshold uncertainty score0.486

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 head0.005
GPT teacher head0.227
Teacher spread0.222 · 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