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Record W2107552366 · doi:10.1109/iscas.2011.5938026

Lossless implementation of Daubechies 8-tap wavelet transform

2011· article· en· W2107552366 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLossless compressionLifting schemeComputer scienceWavelet transformField-programmable gate arraySecond-generation wavelet transformWaveletDiscrete wavelet transformStationary wavelet transformDaubechies waveletReduction (mathematics)AlgorithmComputer hardwareArtificial intelligenceMathematicsData compression

Abstract

fetched live from OpenAlex

A new mapping scheme and its hardware implementation to error-freely compute the Daubechies 8-tap wavelet transform is presented. The multidimensional technique maps the irrational transform basis coefficients with integers and results in considerable reduction in hardware and power consumption. When implemented in Xilinx FPGA, the scheme costs 518 logic cells, 186 registers and runs at a frequency of 71MHz. While comparing with finite-precision architecture, the proposed scheme yields a reduction of 15% in hardware and 41% in power consumption for similar image reconstruction, and noticeable improvement in image reconstruction quality.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.217

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 head0.048
GPT teacher head0.313
Teacher spread0.265 · 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

Quick stats

Citations21
Published2011
Admission routes1
Has abstractyes

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