Sequential design of FIR digital filters for low-power DSP applications
Bibliographic record
Abstract
A method for the design of FIR digital filters with low power consumption is proposed. In this method, the digital filter is implemented as a cascade arrangement of low-order sections. The first section is designed through optimization so as to satisfy as far as possible, the overall required specifications. The first section is then fixed and a second section is added, which is designed so that the first two sections in cascade satisfy again as far as possible the overall required specifications. This process is repeated until a multisection filter is obtained that would satisfy the required specifications under the most critical circumstances imposed by the application at hand. In multisection filters of this type, the minimum number of sections required to process the current input signal can be switched in through the use of a simple adaptation mechanism and, in this way, the power consumption can be minimized. This design strategy is achieved by formulating the design of the k-th section as a weighted least-squares minimization problem, assuming that an optimum (k-1)-section design is available.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".