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Record W4385779127 · doi:10.23977/acss.2023.070605

Multi-computer Communication Reliability Evaluation System Based on 8051 Microcontroller

2023· article· en· W4385779127 on OpenAlexvenueno aff
Xiang Tang, Ying Yao, Yu Zheng, Lijie Liu

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsMicrocontrollerReliability (semiconductor)DebuggingComputer scienceEmbedded systemModular designProcess (computing)Key (lock)Computer hardwareData transmissionReliability engineeringTransmission (telecommunications)Operating systemEngineeringTelecommunications

Abstract

fetched live from OpenAlex

With the progress of society, people have higher and higher requirements for the quality of life, and electronic products play an important role in daily life. Among them, single-chip control has become a key factor to measure the quality of products and work. This paper mainly introduces the reliability evaluation system of timing communication based on 8051 microcontroller series, taking 8051 microcontroller as the core to realize the data transmission and processing process, and using modular structure to carry out circuit simulation, hardware welding and other steps to complete the debugging of various chips and related software programs. This paper also displays the experimental results and test results on the physical object. The test results show that the reliability of the system is up to more than 90%. It has been proved by many times that this function can achieve good results with high stability and high transmission efficiency.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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