Derivation and Design of In-Loop Filters in Phase-Locked Loop Systems
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Bibliographic record
Abstract
This paper addresses the concept of in-loop filters in phase-locked loop (PLL) systems. The in-loop filters are derived from an optimization perspective, and an analytical method to design the controlling parameters of a PLL with in-loop filters is also presented. Such filters can also be selected as conventional window functions in which case they can be tuned to reject certain frequency components similar to the discrete Fourier transform. In this paper, a rigorous method to introduce the concept of in-loop filters and window functions into PLL systems is presented. This method enables smoother estimation of the signal parameters such as phase angle, frequency, and amplitude in the presence of noise and harmonics. The in-loop filters can be adjusted to completely remove specific harmonics. The method is first developed for a single-phase enhanced PLL system and is then extended to three-phase PLLs including the well-known synchronous-reference-frame PLL. Simulation and experimental results are also included.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it