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Record W2129442048 · doi:10.1109/ccece.1995.526569

Index mapping approach of deriving the PM DFT algorithms

2002· article· en· W2129442048 on OpenAlexaff
D. Sundararajan, M. Omair Ahmad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsConcordia University
Fundersnot available
KeywordsSignal-flow graphAlgorithmComputer scienceDiscrete Fourier transform (general)ComputationConvolution (computer science)Signal processingGraphCyclotomic fast Fourier transformTheoretical computer scienceFourier transformMathematicsFourier analysisDigital signal processingFractional Fourier transformArtificial intelligenceArtificial neural network

Abstract

fetched live from OpenAlex

The DFT is of fundamental importance in many areas of science and engineering due to its efficient evaluation of frequently used operations such as signal analysis, convolution, and correlation. It has been shown that radix-2 DFT (discrete Fourier transform) algorithms can be designed based on vector representation of data providing several advantages. These algorithms provide more efficient solution for the problem of DFT computation. An index mapping approach is used to derive these algorithms. This approach makes the derivation simpler and provides a better insight into the functioning of the algorithms. The signal flow graph of the algorithm is also presented.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.153

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 science0.0000.000
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 head0.070
GPT teacher head0.242
Teacher spread0.172 · 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 designSimulation or modeling
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

Citations0
Published2002
Admission routes1
Has abstractyes

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