A statistical technique for the determination of the noise power gain in higher-order Σ - Δ converters excited by DC input signals
Bibliographic record
Abstract
The existing techniques available for the statistical determination of the noise power gain (NPG) in general-order /spl Sigma/-/spl Delta/ A/D converters are based on the assumption that the quantizer input signal has a Gaussian distribution. However, empirical investigations reveal that this assumption holds true for the special case of the conventional first-order /spl Sigma/-/spl Delta/ A/D converters only. This paper presents an alternative technique for a more accurate determination of NPG for higher-order /spl Sigma/-/spl Delta/ A/D converters excited by DC input signals. This is achieved by employing the Gram-Charlier series for a (quasi-linear) modelling of the quantizer input signal. The proposed technique is based on the practical assumption that the constituent quantizer operates in its overload-free region. A typical practical application example is given to illustrate the main 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".