A Power-Efficient Successive Approximation Algorithm for Low-Activity Signals
Bibliographic record
Abstract
This paper presents a new successive approximation algorithm that can digitize the second-order difference of signal samples rather than each sample point individually or plain difference of sample points. This method is able to drastically reduce the number of comparisons required to convert a new signal sample to digital numbers, from a fixed N comparisons commonly used in a conventional successive-approximation-register (SAR) analog-to-digital converter (ADC), to a number in between 2 and N for almost all the signal samples in an N-bit ADC. This proposed algorithm is implemented in MATLAB and tested on electrocardiogram (ECG) signals in this work. The experimental results show that our algorithm can reduce the number of comparisons by 58.75% compared to the conventional SAR ADC, and by 17.78% or more compared to several other state-of-the-art methods. In addition, it is able to reduce the DAC updating time by the same percentages, leading to lower power consumption in both DAC and digital parts of SAR ADC.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".