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Record W2912285864 · doi:10.1109/icecs.2018.8617960

Hardware Implementation of A Chaotic Pseudo Random Number Generator Based on 3D Chaotic System without Equilibrium

2018· article· en· W2912285864 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
TopicChaos-based Image/Signal Encryption
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsChaoticComputer scienceCryptographyRandom number generationOverhead (engineering)Field-programmable gate arraySynchronization of chaosNonlinear systemComputer hardwareControl theory (sociology)AlgorithmPhysics

Abstract

fetched live from OpenAlex

Deterministic chaotic systems have been studied and developed in various fields of research. Dynamical systems with chaotic dynamics have different applications in communication, security and computation. Chaotic behaviors can be created by even simple nonlinear systems which can be implemented on low-cost hardware platforms. This paper presents a high-speed and low-cost hardware of three-dimensional chaotic flows without equilibrium. The proposed chaotic hardware is able to reproduce the main mechanism and dynamical behavior of the 3D chaotic flows observed in simulation, then a Chaotic Pseudo Random Number Generator is designed based on a 3D chaotic system. The proposed hardware is implemented with low computational overhead on an FPGA board, as a proof of concept. This low-cost chaotic hardware can be utilized in embedded and lightweight systems for a variety of chaotic based digital systems such as digital communication systems, and cryptography systems based on chaos theory for Security and IoT applications.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.833
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.278
Teacher spread0.262 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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