Stability analysis of multiple-feedback oversampled Σ-Δ A/D converter configurations
Bibliographic record
Abstract
This paper is concerned with the estimation of the maximum DC input signal level for five different practical feedforward and multiple-feedback oversampled /spl Sigma/-/spl Delta/ A/D converters. This technique is based on replacing the constituent quantizer by a uniformly distributed additive white noise source and a variable gain element, assuming the following conditions are satisfied: a) the quantizer input signal and quantization error are uncorrelated, b) the quantizer input signal is Gaussian distributed, and c) the quantization noise is white. It is shown that the statistical estimation technique is quite accurate for /spl Sigma/-/spl Delta/ A/D converters with small noise power gains, but that the accuracy tends to decrease for A/D converters having larger noise power gains. It is further shown that this reduction in accuracy stems from the fact that the assumptions listed under a), b), and c) are not exactly satisfied. It is finally shown that the probability density function of the input signal to the quantizer may be modelled more accurately using the Gram-Charlier series.
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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) | 0.000 | 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".