Design and implementation of low-power IIR digital filter systems
Why this work is in the frame
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Bibliographic record
Abstract
A method for the design of linear-phase IIR digital filters for low-power applications is proposed. In this method, the digital filter is implemented as a cascade arrangement of 2nd-order sections. Each section is designed through optimization techniques so that all sections in cascade satisfy as far as possible the overall required specifications. This process is repeated until a multisection filter is obtained which satisfies the required specifications under the most critical circumstances imposed by the application at hand. The minimum number of sections required to process a particular input signal can then by switched on through the use of a simple adaptation mechanism and, in this way, the power consumption can be minimized. This design structure is achieved by formulating the design of the k-1 sections as constraints. As an example, a low-power filter system is compared with a fixed-order linear-phase IIR filter of the same performance. It is shown that a power reduction of at least 10% can be achieved.
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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 | 0.002 |
| 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