A Review of Non-Uniform Sampling Schemes for Power-Efficient Data Acquisition Systems [Feature]
Why this work is in the frame
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Bibliographic record
Abstract
Non-uniform sampling techniques enhance the efficiency of data acquisition systems by operating at a sub-Nyquist sampling rate while maintaining a comparable output quality. These techniques aid in building data acquisition systems that are aware of the signal characteristics so that the limited power budget can be consumed only on certain valuable sampling points at certain signal events, rather than a fixed set of points uniformly sampled at the Nyquist rate. This paper provides a tutorial review of various proposed non-uniform sampling schemes detailing their underlying mechanisms, potential analog circuitry implementations, and the impact of non-idealities on their performance. The paper presents a comprehensive performance comparison between these methods focusing on key metrics such as power consumption, accuracy, and design complexity. A thorough comparison is achieved through analysis of reported performance in the literature and the conducting of simulations. This review aims to guide readers on choosing the appropriate non-uniform sampling scheme that best fits the application requirements, and on their analog implementations and limitations.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 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 it