Low-power high-accuracy compact implementation of analog wavelet transforms
Bibliographic record
Abstract
Implementing wavelet transform using analog circuits is of great interest when low power consumption and chip area become important issues. In this case, the complexity of circuits depends on the accuracy of the wavelet approximation. First, an optimized procedure based on a Hankel-norm model reduction is applied to approximate the transfer function of a linear steady- state system whose impulse response implements the required wavelet. The proposed approach significantly improves the accuracy of approximated wavelet. Next, the approximation result is implemented using a low- power low-voltage second order log domain filter as a design example in 0.18 mum CMOS technology. The implemented filter based on the presented method features compact chip area, improved linearity, and ultra low-power consumption. Moreover, it presents a tunable gain, which allows for filters bands configurability. Finally, the design of a complete log domain filter bank, based on the proposed second order filter as main building block is detailed. The filter bank implements a rational approximation of a Gaussian wavelet function following the presented approximation method.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".