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Record W2472044101 · doi:10.1109/tcsii.2016.2584091

Low-Cost Architecture of Modified Daubechies Lifting Wavelets Using Integer Polynomial Mapping

2016· article· en· W2472044101 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2016
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsUniversity of Saskatchewan
FundersWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsWaveletInteger (computer science)MathematicsPolynomialArchitectureInteger programmingComputer scienceMathematical optimizationArtificial intelligenceMathematical analysisGeography

Abstract

fetched live from OpenAlex

This brief proposes a modified version of the popular lifting algorithm of Daubechies-4 (D4) and Daubechies-6 (D6) wavelets and its efficient implementation using integer polynomial mapping (IPM). At first, an improved polyphase matrix for D4 is presented that eliminates one filter coefficient completely without losing any accuracy. Then, IPM is applied to encode the remaining irrational coefficients. As a result, computation error due to irrational numbers in the conventional method is significantly reduced, resulting in better image reconstruction. For D6, a two-level optimization scheme combined with the resource sharing of coefficients is applied that results in simplified hardware architecture with much fewer resources.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.035
GPT teacher head0.260
Teacher spread0.226 · 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