An All-Digital Spread Spectrum Method With Distortion Correction for Filterless Digital Class-D Amplifiers
Bibliographic record
Abstract
Filterless digital class-D amplifiers (CDAs) are very attractive for audio-visual devices thanks to their high efficiency, small size and low cost. However, their high-frequency switching-mode operation will cause electromagnetic interference (EMI) problems. Spread spectrum techniques are often used to solve the EMI problems but will cause baseband distortions in amplifiers. In this article, an all-digital spread spectrum method with distortion correction is proposed to make filterless digital CDAs achieve low EMI emissions and high signal to noise and distortion ratio (SNDR). This method mainly utilizes the random numbers generated by a pseudo-random number generator to randomize the pulse position and pulse repetition frequency (PRF) of the uniform-sampling pulse width modulation (UPWM) generator output signal to achieve the effect of spread spectrum. Moreover, the high open-loop gain characteristic of the digital sigma-delta modulator in the baseband and the state-space reconstruction technique are utilized to correct the baseband distortions caused by UPWM and spread spectrum. The feasibility and effectiveness of the proposed method are verified by simulation and experimental results.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".