Digital LMS adaptation of analog filters without gradient information.
Bibliographic record
Abstract
The least mean square (LMS) algorithm has practical problems in the analog domain mainly due to DC offset effects. If digital LMS adaptation is used, a digitizer (analog-to-digital converter or comparator) is required for each gradient signal as well as the filter output. Furthermore, in some cases the state signals are not available anywhere in the analog signal path necessitating additional analog filters. Here, techniques for digitally estimating the gradient signals required for the LMS adaptation of analog filters are described. The techniques are free from DC offset effects and do not require access to the filter's internal state signals. Digitizers are required only on the input and error signal. The convergence rate and misadjustment are identical to traditional LMS adaptation, but an additional matrix multiplication is required for each iteration. Hence, analog circuit complexity is reduced but digital circuit complexity is increased with no change in overall performance making it an attractive option for mixed-signal integrated systems in digital CMOS. Signed and subsampled variations of the adaptive algorithm can provide a further reduction in analog and digital circuit complexity, but with a slower convergence rate. Theoretical analyses, behavioral simulations, and experimental results from an integrated filter are all presented.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".