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Record W2172208619 · doi:10.1109/iscas.2009.5118265

Efficient hardware implementation of hybrid cosine-fourier-wavelet transforms on a single FPGA

2009· article· en· W2172208619 on OpenAlexafffund
Khan A. Wahid, Samia Shimu, Md. Ashraful Islam, D. Teng, Moon Ho Lee, Seok‐Bum Ko

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsField-programmable gate arrayComputer scienceDiscrete cosine transformDiscrete wavelet transformParallel computingThroughputFast Fourier transformDiscrete Fourier transform (general)Discrete Hartley transformMultiplication (music)Computer hardwareTransformation matrixTransformation (genetics)Matrix multiplicationWaveletAlgorithmWavelet transformFourier transformMathematicsFractional Fourier transformArtificial intelligenceImage (mathematics)Fourier analysis

Abstract

fetched live from OpenAlex

This paper presents an efficient hardware implementation of a hybrid architecture to compute three 8-point transforms - the Discrete Cosine Transform, the Discrete Fourier Transform, and the Discrete Wavelet Transform on a single FPGA. The architecture is based on an element-wise matrix factorization and row-permutation algorithm, where the forward basis transformation matrices are decomposed into multiple sub-matrices and the common units are shared among them. The hardware implementation is parallel, pipelined and multiplication-free; it costs only 2,073 logic cells, 1,476 registers and runs at maximum frequency of 118 MHz with a very high process throughput of 944 Megabits/sec when synthesized onto an Altera FPGA device. The synthesized results for other FPGA technologies are also presented for performance assessment.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.417

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 head0.024
GPT teacher head0.298
Teacher spread0.274 · 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 designBench or experimental
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

Citations6
Published2009
Admission routes2
Has abstractyes

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