Study of algorithms for synthesis of self-checking digital devices based on Boolean correction of signals using weighted Bose – Lin codes
Bibliographic record
Abstract
When synthesizing self-checking digital devices based on Boolean correction of signals, it is proposed to use weight-based Bose – Lin codes, the construction principles of which imply preliminary weighting of data symbols by natural numbers. Two “basic” structures are proposed for the synthesis of built-in control circuits for groups of six outputs of the diagnostic object. The structures are based on weight-based Bose – Lin codes with summation in the residue ring modulo M=4. There are 15 such noise-protected codes with the number of data symbols m=4, which allows to select the best option as a base code in the builtin control circuit according to various criteria, including achieving self-checking properties even in cases where this cannot be achieved using traditional approaches, including duplication. Two algorithms for the synthesis of built-in control circuits based on Boolean signal correction have been developed, allowing the use of correction of only two of the six functions in the basic structure. For basic structures, there are 720 ways to construct an integrated control circuit based on Boolean correction of signals using each weight-based Bose – Lin code, which makes it possible to choose the best way to implement a self-checking device, considering various indicators (structural redundancy, testability, etc.). The operation of the algorithms is demonstrated on simple examples. The results of experiments with test digital circuits from the MCNC Benchmars set confirming the efficiency of the developed algorithms are given. It is shown that with a large number of outputs, there is an astronomical number of ways to organize built-in control circuits, which makes it possible to build self-checking devices with various characteristics. The use of Boolean correction of signals using weight-based Bose – Lin codes can be used in the development and design of self-checking digital devices on various element bases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".