Bibliographic record
Abstract
Many embedded systems require hard or soft real-time execution. To ensure the requirements are met, it is necessary to measure the execution time of individual tasks, as well as establish the overall real-time performance of the system. The traditional software-only methods for measuring the execution times of real-time codes are easy to use and low cost, but provide lower resolution and greater overhead than hardware ones, which impedes using it for analyzing real-time performance, such as identifying whether a specific task set is schedulable. In order to overcome the main two obstacles which cause lower accuracy of software-only methods, based on the information fusion idea of multi-source data and using the improved software-only method for measuring the execution times of real-time codes, which also takes the information fusion idea of multi-source data to improve the accuracy of the measurements, while it reserves the good features owned by traditional ones, this paper presents a fusion mechanism for analyzing the realtime performance of embedded systems. Using the mechanism, the designers and developers can pinpoint the timing problems, find the code to be optimized so as to avoid the missed deadlines, and identify the schedulability of a realtime task set under a specific scheduling environment.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".