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Study of algorithms for synthesis of self-checking digital devices based on Boolean correction of signals using weighted Bose – Lin codes

2024· article· en· W4392890930 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

VenueTransport automation research · 2024
Typearticle
Languageen
FieldComputer Science
TopicMathematical Control Systems and Analysis
Canadian institutionsInternational Air Transport Association
Fundersnot available
KeywordsComputer scienceAlgorithmRedundancy (engineering)Digital electronicsBoolean functionTestabilityElectronic circuitBoolean circuitWeightingArithmeticMathematics

Abstract

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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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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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.371
Teacher spread0.291 · 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