MétaCan
Menu
Back to cohort
Record W2113875394 · doi:10.1109/ccece.2004.1345319

An efficient FFT algorithm based on the radix-2/4 DIF approach for computing 3D DFT

2004· article· en· W2113875394 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
TopicDigital Filter Design and Implementation
Canadian institutionsConcordia University
Fundersnot available
KeywordsFast Fourier transformSplit-radix FFT algorithmRadix (gastropod)Computer sciencePrime-factor FFT algorithmAlgorithmKronecker productComputational complexity theoryTwiddle factorParallel computingDecimationMatrix multiplicationKronecker deltaMathematicsFourier transformFilter (signal processing)Fourier analysis

Abstract

fetched live from OpenAlex

We propose a 3D split vector-radix decimation-in-frequency (DIF) FFT algorithm for computing the 3D DFT, based on a mixture of radix-(2/spl times/2/spl times/2) and radix-(4/spl times/4/spl times/4) index maps. It is shown that the proposed algorithm reduces the computational complexity significantly in comparison to the existing 3D vector radix FFT algorithms as well as algorithms that are based on row-column decomposition. In addition, since the proposed algorithm is expressed in a simple matrix form using the Kronecker product, it facilitates easy software or hardware implementation of the algorithm.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.787
Threshold uncertainty score0.421

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 science0.0010.000
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 head0.033
GPT teacher head0.274
Teacher spread0.241 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

Explore more

Same topicDigital Filter Design and ImplementationFrench-language works237,207