Frequency Calibration for On-chip RC Oscillator of AVR Using Genetic Algorithms
Bibliographic record
Abstract
This paper described a method to use Genetic Algorithms to calibrate the on-chip RC oscillator of AVR real-time.Take the high precision on-chip RTC(real time clock) as the time base control unit,using Genetic Algorithms to search the best OSCCAL register value which will make the on-chip RC oscillator to generate a high precision clock for the MCU.This method can be used fast and real-time,so that the CPU will get a stable clock source.The initial population of OSCCAL register participation digital was randomly generated by the function of rand() from C language library.They get all generations of fitness through genetic manipulation such as reproduction,crossover,mutation and so on.Among them,the population reproduce according to the principle that close to the optimal solution,cross-matching pair exchanged code randomly at the method of operation,mutation probability values of 0.01 to ensure the stability of genetic algorithm.Given the basic operating procedures of real time genetic algorithm and 10 iterations of search results,show that the algorithm optimization effect is very obvious.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".