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Record W2013816854 · doi:10.1109/fpt.2007.4439290

Compound Uniform Random Number Generators with On-Chhip Correlation and Distribution Measurements

2007· article· en· W2013816854 on OpenAlexaff
Amirhossein Alimohammad, Saeed Fouladi Fard, B.F. Cockburn, Christian Schlegel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRandomnessRandom number generationField-programmable gate arrayRange (aeronautics)Computer scienceNonlinear systemDistribution (mathematics)Parallel computingAlgorithmMathematicsComputer hardwareEngineeringPhysicsStatistics

Abstract

fetched live from OpenAlex

A fast and compact implementation of nonlinear pseudo-random number generators (PNGs) on a field-programmable gate array (FPGA) is presented. The distribution and correlation properties of PNGs are efficiently, and very rapidly, estimated within the FPGA. It is shown that the pseudo-random output of nonlinear PNGs have significantly improved randomness properties compared to linear PNGs. To the best of our knowledge, this paper presents the first long-range correlation and distribution measurements in hardware for PNGs on FPGAs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.242
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2007
Admission routes1
Has abstractyes

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