A novel technique for the design and DCGA optimization of guaranteed BIBO stable Jaumann digital IF filters over the CSD multiplier coefficient space
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Bibliographic record
Abstract
Intermediate Frequency (IF) filters find diverse practical applications in modern communication systems. This paper presents a novel technique for the design and discrete optimization of guaranteed BIBO stable digital IF filters over the canonical signed-digit (CSD) multiplier coefficient space. This technique consists of two separate stages. In the first stage, the bilinear-LDI Jaumann digital filter design approach is employed to obtain an infinite-precision digital IF filter satisfying the given design specifications approximately. In the second stage, a diversity-controlled (DC) genetic algorithm (GA) is employed for the (discrete) optimization of the resulting Jaumann digital IF filter over the CSD multiplier coefficient space satisfying the design specifications fully. A novel LUT-based scheme is introduced to ensure that the resulting CSD Jaumann digital IF filter is automatically BIBO stable. The proposed approach is illustrated through its application to a rapid optimization (within 328 DCGA generations) of a three-sectioned eighteenth-order AMPS digital IF filter operating around a center frequency of 455 kHz.
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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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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