A two-stage genetic algorithm for the design and optimization of resonator/integrator based sigma-delta A/D and D/A converters
Bibliographic record
Abstract
In preceding papers, a genetic algorithm was developed for the design and optimization of the noise transfer function in higher-order /spl Sigma/-/spl Delta/ A/D and D/A converters having cascade-of-resonators (COR), cascade-of-integrators (COI), and combined cascade-of-integrators-cum-resonators (CRI) configurations. This paper presents a two-stage genetic-algorithm for the optimization of the constituent multiplier coefficient values in COR, COI, and CRI /spl Sigma/-/spl Delta/ D/A converter structures after a corresponding noise transfer function optimization. The underlying genetic algorithm for the optimization of the noise transfer function guarantees that after the application of the operations of crossover and mutation to the parent noise transfer functions, the resulting offspring noise transfer functions are (inherently) BIBO stable. The usefulness of the proposed technique is demonstrated through its application to the design of a sixth-order lowpass CRI, COR and COI /spl Sigma/-/spl Delta/ A/D converters employing a 64 time oversampling ratio for a final realization incorporating 32-bit multiplier coefficient values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".