Design and optimization of a class of oversampled sigma-delta D/A converters by employing genetic algorithms
Bibliographic record
Abstract
In a preceding paper, a genetic algorithm was developed for the design and optimization of higher-order oversampled Sigma-Delta digital-to-analog (D/A) converters having cascade-of-resonators and cascade-of-integrators configurations. This paper is concerned with the genetic-algorithm optimization of the corresponding combined cascade-of-resonators/cascade-of-integrators (CRI) Sigma-Delta D/A converters. The salient feature of CRI Sigma-Delta D/A converters is that the constituent signal and noise transfer functions become complimentary by the converter configuration proper, simplifying the design process to the realization of the constituent noise transfer function. Moreover, the configuration proper places the noise transfer function zeros on the unit-circle, making it possible to obtain high signal-to-quantization-noise ratios. 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 Sigma-Delta D/A converter employing a 64 time oversampling ratio for a final realization incorporating 16-bit multiplier values
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".