A Saturation-Free 3.6V/1.8V DM/CM Input Range 46.6mV/μs Artifacts Recovery Sensor Interface using CT Track-and-Zoom
Bibliographic record
Abstract
Artifacts in ExG recordings resulting from motion, environment interferences and stimulation can manifest as common mode (CM), differential mode (DM) or hybrid of CM/DM signals with up to hundreds of mV. To mitigate these artifacts and minimize information loss, a sensor interface (SI) with high input range and fast response speed is required. In contrast to the use of power-hungry high dynamic range (DR) ADCs, various artifact recovery approaches including adaptive filter [1], track-and-zoom [2], adaptive gain control [3] and common-mode charge pump [4] have been developed demonstrating impressive power efficiency, but at the cost of reduced signal resolution or limited recovery speed. This paper presents a continuous-time track-and-zoom (CT-TAZ) technique to address large artifact events with a saturation-free SI with 3.6V/1.8V DM/CM full-scale input range, owing to a hybrid analog-digital asynchronous signal folder (ASF) that constrains the signal within the predefined constant window in current domain. The signal can be recovered within 73 μs under 3.6V stimulation artifacts, achieving 46.6mV/μs artifact recovery speed, which is 2.25~47.3X larger and 3.56~311X faster than the state-of-the-art.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".