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Record W1814759949 · doi:10.1109/icassp.1977.1170347

Hardware realization of digital signal processing elements using the residue number system.

2005· article· en· W1814759949 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
TopicNumerical Methods and Algorithms
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsRealization (probability)ArithmeticBinary numberComputer scienceComputer hardwareDigital filterSignal processingSaturation arithmeticDigital signal processingResidue number systemFast Fourier transformArbitrary-precision arithmeticAlgorithmFilter (signal processing)Mathematics

Abstract

fetched live from OpenAlex

In the past, hardware realization of digital signal processing elements have been based upon binary arithmetic concepts. Because of the dependence between digits in binary arithmetic operations, the hardware required to construct arithmetic elements is cumbersome. In the residue number system, arithmetic operations can be performed with complete independence between digits and a corresponding reduction in hardware complexity. In fact, using current technology, arithmetic operations can be carried out using arrays of look-up tables placed in high density ROMs. This paper discusses the application of the residue number system to realizing digital signal processing elements using such arrays and advantages and disadvantages over conventional realizations are discussed. Examples are given of recursive filter and FFT butterfly element realization.

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: none
Teacher disagreement score0.992
Threshold uncertainty score0.184

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 head0.032
GPT teacher head0.315
Teacher spread0.283 · 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

Citations15
Published2005
Admission routes1
Has abstractyes

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