A novel technique for DCGA optimization of guaranteed BIBO stable IIR-based FRM digital filters over the CSD multiplier coefficient space
Bibliographic record
Abstract
This paper presents a novel diversity-controlled (DC) genetic algorithm (GA) for the design and rapid optimization of frequency-response masking (FRM) digital filters over the CSD multiplier coefficient space. The resulting FRM digital filters incorporate bilinear-LDI IIR interpolation subfilters realized as a parallel combination of a pair of allpass digital networks. A novel LUT scheme is developed to ensure that the FRM digital filters under consideration are automatically BIBO stable throughout the course of DCGA optimization. The salient feature of the proposed LUT scheme is that it makes no recourse to slack variables for referencing the values of the CSD multiplier coefficients. The DCGA optimization fitness function includes not only the magnitude but also the group-delay frequency-response of FRM digital filters so as to minimize phase distortion caused by the IIR interpolation subfilters. An example is given to illustrate the application of the proposed DCGA optimization to the design of a lowpass FRM digital filter incorporating a seventh-order bilinear-LDI interpolation subfilter.
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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 | 0.001 |
| 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) | 0.001 | 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".