Sampling Rate Optimization for Improving the Cascaded Integrator Comb Filter Characteristics
Why this work is in the frame
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Bibliographic record
Abstract
The cascaded integrator comb (CIC) filters are characterized by coefficient less and reduced hardware requirement, which make them an economical finite impulse response (FIR) class in many signal processing applications. They consist of an integrator section working at the high sampling rate and a comb section working at the low sampling rate. However, they don’t have well defined frequency response. To remedy this problem, several structures have been proposed but the performance is still unsatisfactory. Thence, this paper deals with the improvement of the CIC filter characteristics by optimizing its sampling rate. This solution increases the performance characteristics of CIC filters by improving the stopband attenuation and ripple as well as the passband droop. Also, this paper presents a comparison of the proposed method with some other existing structures such as the conventional CIC, the sharpened CIC, and the modified sharpened CIC filters, which has proven the effectiveness of the proposed method.
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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 | 0.001 | 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