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Record W2091993319 · doi:10.1109/iscas.2010.5537451

A novel approach for FFT data reordering

2010· article· en· W2091993319 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFast Fourier transformComputer scienceComputationSearch engine indexingKey (lock)Signal processingAlgorithmTwiddle factorTable (database)Parallel computingArithmeticFourier transformComputer hardwareDigital signal processingMathematicsData miningFourier analysisArtificial intelligence

Abstract

fetched live from OpenAlex

The Fast Fourier Transform (FFT) is a key role in signal processing applications that is useful for the frequency domain analysis of signals. The FFT computation requires an indexing scheme at each stage to address input/output data and coefficient multipliers properly. Most of these indexing schemes are based on bit-reversal techniques that are boosted by a look-up table requiring extra memory storage. This paper describes a novel data reordering technique based on the vector calculation of size r. FFTs are considered in-place (or in situ) algorithms that transform a data structure by using a constant amount of memory storage. We demonstrate that our proposed method reduces memory usage by eliminating the look-up table traditionally employed in the computation of bit-reversal indexes.

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 score0.930
Threshold uncertainty score0.257

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 communication0.0000.001
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 head0.141
GPT teacher head0.328
Teacher spread0.187 · 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

Citations10
Published2010
Admission routes2
Has abstractyes

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