DCGA optimization of lowpass FRM IIR digital filters over CSD multiplier coefficient space
Bibliographic record
Abstract
Frequency-response masking (FRM) technique is known to significantly reduce computational complexity of sharp transition-band digital filters. This paper presents a novel technique for the design and optimization of a guaranteed BIBO stable FRM IIR lowpass digital filter, employing an odd-order IIR elliptic minimum Q-factor (EMQF) prototype digital filter. The bilinear-LDI design approach is used to realize the prototype digital filter as a parallel combination of a pair of digital allpass networks. A diversity-controlled genetic algorithm (DCGA) is employed for the optimization of the bilinear-LDI FRM IIR digital filter over the canonical signed-digit multiplier coefficient space. A LUT-based scheme is employed to ensure that the resulting FRM IIR digital filter is automatically BIBO stable during DCGA optimization. The proposed technique is illustrated through its application to the optimization of a fifth-order lowpass elliptic FRM IIR digital filter, resulting in a rapid convergence speed of around 300 iterations.
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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".