A Diversity Controlled Genetic Algorithm for Optimization of FRM Digital Filters over DBNS Multiplier Coefficient Space
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Bibliographic record
Abstract
This paper presents a novel diversity controlled (DC) genetic algorithm (GA) for the optimization of frequency-response masking (FRM) FIR digital filters over the double base number system (DBNS) multiplier coefficient space. The use of DBNS multiplier coefficients reduces the complexity and power consumption in the hardware implementation of the resulting FRM FIR digital filters. A direct application of GAs to the design of FRM FIR digital filters may result in chromosomes which do not conform to the DBNS format due to the underlying crossover and mutation operations. The proposed algorithm uses a DBNS based indexed look-up table (LUT) to ensure generation of valid DBNS multiplier coefficients through out the course of genetic optimization. An application example is given for the design of an FRM FIR lowpass digital filter. The resulting FIR digital filter outperforms a corresponding infinite-precision digital filter obtained by using the Parks-McClellan technique.
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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 | 0.001 |
| 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 it