A new architecture of RRNS error-correcting QC encoder/decoder and its FPGA implementation
Bibliographic record
Abstract
In this paper, a new architecture of a redundant residue number system (RRNS) quasi-chaotic (QC) encoder/decoder with an error-correcting ability is proposed for secure communication and is tailored to FPGA implementation. In the proposed architecture, binary subencoders and subdecoders are used so that a number of modulo operations required in the existing design of D. R. Frey (IEEE Trans. Circuits Syst. II, vol. 40, pp. 660-666, Oct. 2000) are replaced by a simple truncation. Furthermore, a size-reduced design of the R/B (residue-to-binary) converter with error-correcting function, which is the crucial part of the system, is proposed. As a case study, the proposed architecture is implemented in FPGA for the case of the 16-bit input and some simulation results obtained.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".