An Ultra-Low-Power Non-Uniform Derivative-Based Sampling Scheme With Tunable Accuracy
Bibliographic record
Abstract
This paper presents an ultra-low-power non-uniform sampling scheme using a derivative-based algorithm that can maintain a comparable accuracy to other non-uniform sampling schemes but with less complexity and lower power consumption. In this method, the change in the derivative of the signal above certain threshold values is used to identify high signal activity for retention of the significant points of the signal. The scheme is implemented using simple building blocks that calculate and compare the change in approximate real-time derivative to a tunable threshed value that can be adjusted to obtain the desired Compression Factor (CF) and Post-Reconstruction Signal-to-Noise plus Distortion Ratio (PR-SNDR) for different signal types. Fabricated in TSMC’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.13 \mu \text{m}$ </tex-math></inline-formula> CMOS technology and tested with real-world biomedical signals, the proposed Derivative Dependent Sampling (DDS) system consumes a maximum power of 155 nW while achieving a CF of more than 6 for an Electrocardiography (ECG) signal. By adding the proposed DDS block to a data acquisition and processing system, the non-uniform sampling can reduce the power dissipation of the entire system.
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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 | 0.001 |
| 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".