A Three-Point Interpolation DFT-Based Frequency Estimation Algorithm for Temperature-Stable System in MEMS Oscillators
Bibliographic record
Abstract
Microelectromechanical system (MEMS) oscillators are widely used in navigation systems because of their reliability. Real-time temperature changes will have a major impact on the stability of these systems, so calculating the output frequency of the MEMS resonator at different temperatures is particularly important for the stability of the system. Therefore, we propose a three-point interpolated discrete Fourier transform (IpDFT)-based frequency estimation algorithm for a temperature-stable system in MEMS oscillators. Because the temperature-stable technique can provide an unbiased estimate of the frequency of the signal that is more accurate and efficient for temperature compensation, it can effectively mitigate the frequency shift of the MEMS resonator due to temperature variation. The simulated result of the absolute temperature error is less than$0.01~^{\circ } $C when the signal-to-noise ratio (SNR) is 40 dB. The absolute temperature error result is less than$0.01~^{\circ } $C in the amplitude jitter and frequency jitter tests. The temperature-stable technique is applied to the MEMS resonator temperature compensation system. The measured frequency shift ratio of the signal processed by the temperature compensation system is less than 30 ppm, indicating that the oscillation frequency can be effectively stabilized at different temperatures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".