Methods of Improving Technical and Functional Characteristics of Serial Budget Microprocessor Platforms
Bibliographic record
Abstract
The relevance of the research is due to the fact that one of the prerequisites for the implementation of Internet of things and Wireless sensor network technologies is the reliable operation of the involved information and measurement systems as parts of the automation and digitalization systems on the rights of subsystems. Thus, the main purpose of the article is to substantiate scientific approaches to increasing the reliability of budget serial microcontroller boards as parts of information and measurement systems by developing methods of improving their technical and functional characteristics. Basic research methods are: methods of critical analysis and logical generalization; methods of computerized monitoring of electrical parameters; methods of experiment planning; methods of computer analysis of measurement results; approaches to experimental testing of information and measuring equipment. The main results of the article are as follows: the current state of scientific research and practical developments on ways to improve the technical and functional characteristics of microprocessor platforms has been analysed; the stabilization characteristics of the output voltage of the microprocessor platform have been investigated; the temperature dependences of the microcontroller at different denominations and types of load have been obtained; the priority areas for further research have been substantiated.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".