A Digital Pseudo Random Number Generator Based on a Chaotic Dynamic System
Bibliographic record
Abstract
Chaotic based Pseudo Random Number Generators (PRNGs) have been very popular due to their unpredictable behavior. Hardware implementations of PRNG is used in many applications including cryptography, security and communication systems. Chaotic systems demonstrate an extreme sensitivity to variations in initial conditions and control parameters which makes it ideal for such applications especially cryptography. Due to this severe sensitivity, it is essential to implement the system with a sufficient accuracy while keeping the hardware cost as low as possible. An efficient and compact digital design is proposed for a PRNG based on a low-dimensional chaotic dynamic system. In this design, CORDIC algorithm has been used to reduce the hardware cost while maintaining the accuracy high. Experimental results and error measurements indicate a maximum error of 0.011 and RMSE of 0.0015 between the proposed digital circuits and the original chaotic system. The circuits have been synthesized for an Altera FPGA board. The results indicate that the circuits are successfully able to mimic the mathematical simulation counterpart. In addition, synthesis results verify that the proposed circuits take less than 1% of the target FPGA resources. Furthermore, Static Timing Analysis demonstrate a maximum frequency of 172.09 MHz for the proposed circuits. The proposed circuits can be employed in cryptography and security applications due to their compactness and high accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".