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Record W2057590771 · doi:10.1109/ukricis.2010.5898088

Low cost VLSI discrete wavelet transform and FIR filters architectures for very high-speed signal and image processing

2010· article· en· W2057590771 on OpenAlexaff
Mountassar Maamoun, Rafik Bradai, Abdelhamid Meraghni, Rachid Beguenane

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsFinite impulse responseDiscrete wavelet transformComputer scienceVery-large-scale integrationClock rateField-programmable gate arrayWavelet transformSecond-generation wavelet transformConvolution (computer science)Digital signal processingPipeline (software)Lifting schemeCritical path methodAdderSignal processingWaveletElectronic engineeringComputer hardwareAlgorithmEmbedded systemArtificial intelligenceEngineeringLatency (audio)Chip

Abstract

fetched live from OpenAlex

This paper presents new VLSI architectures for finite impulse response (FIR) filters and discrete wavelet transform, intended for very high-speed signal and image processing. The proposed architectures, based on combining pipeline and parallel arithmetic methods, provide a new and very fast convolution approach with a reduced critical path. The key to this is a clever use of D-latches and multipliers which are efficiently distributed. Furthermore, an advanced discrete wavelet transform (DWT) approach, with an area-efficient architecture, is designed to produce one output in every clock cycle. As a result, a very high-speed is attained. The proposed structure can increase the work frequency (85%) at a low cost of additional hardware elements (55%). The systems are verified, using JPEG2000 coefficients filters, on Xilinx Field Programmable Gate Array (FPGA) devices.

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: Methods · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score0.580

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 communication0.0010.000
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 head0.010
GPT teacher head0.263
Teacher spread0.253 · 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
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

Citations4
Published2010
Admission routes1
Has abstractyes

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