Quantization noise spectrum of double-loop sigma-delta converter with sinusoidal input
Bibliographic record
Abstract
An exact formula for the output noise spectrum of a double-loop sigma-delta modulator with a sinusoidal input is derived without the use of a white-noise model. In the case of a sinusoidal input with irrational input amplitude and digital frequency, the result agrees with the exact formula derived by ergodic theory for two-stage modulators. In addition, the present method also provides an exact formula for a sinusoidal input with rational frequency and amplitude. Furthermore, the period of the output with rational initial conditions and DC input is also calculated. All results assume that the quantizer does not overload, and hence apply only to multi-bit coders. The ergodic theory method for calculating the exact noise spectrum involves explicitly determining the autocorrelation of the internal quantization error with ergodic theory techniques, and then determining the noise spectrum from the correlation function. The present method, however, directly determines the quantization noise spectrum by using an open-loop model for the coder and applying a Fourier series representation of the quantization error function. The result of both of these methods is that the output noise spectrum for a sinusoidal input is composed of discrete spectral lines shaped by a sin/sup 4/(/spl omega2) envelope.>
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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 | 0.000 |
| 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) | 0.002 | 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".