Particle swarm optimization of FRM FIR digital filters over the CSD multiplier coefficient space
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Bibliographic record
Abstract
This paper presents a novel technique for particle swarm optimization (PSO) of FRM FIR digital filters over the CSD multiplier coefficient space. In this technique, a FRM FIR digital filter is represented as a point in a multidimensional CSD multiplier coefficient space. In order to limit the search space, a CSD LUT is generated to include promising points in the multidimensional multiplier coefficient space. Candidate CSD FRM FIR digital filters generated in the course of particle swarm optimization are guaranteed to remain automatically within the CSD LUT boundaries during the constituent PSO move operation without any recourse to backtracking. This is achieved by augmenting the LUT with barren regions. An example is given to illustrate the application of the proposed PSO to the design of a lowpass FRM FIR digital filter.
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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