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Record W2126908656 · doi:10.1109/pacrim.1999.799510

A systolic array architecture for 2-D inverse wavelet transform

2003· article· en· W2126908656 on OpenAlexaff
Jitender Singh, A. Antoniou, D.J. Shpak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDiscrete wavelet transformWaveletWavelet transformInverseLifting schemeSecond-generation wavelet transformComputer scienceSystolic arrayArchitectureStationary wavelet transformWavelet packet decompositionAlgorithmMathematicsArtificial intelligenceVery-large-scale integrationEmbedded systemGeometry

Abstract

fetched live from OpenAlex

A systolic array architecture for computing the 2-D inverse discrete wavelet transform is proposed. The architecture is derived by performing a thorough data dependence and localization analysis for the 2-D inverse discrete wavelet transform. The design is modular, and can easily be scaled for different levels of wavelet decomposition and filter lengths. The derived architecture for an 8/spl times/8 image has been functionally verified by coding the design in VHDL and simulating it using Mentor Graphics.

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 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)0.001
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 score0.378
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.267
Teacher spread0.245 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations3
Published2003
Admission routes1
Has abstractyes

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