A SC rectification and bin-integration circuit for nerve signal processing: experimental results
Bibliographic record
Abstract
In this paper, we describe a low-voltage CMOS switched-capacitor rectification and bin-integration (RBI) circuit dedicated to sensor electronic interfaces. The applications of these interfaces are among others, biomedical and more particularly the implantable devices. RBI is the most common signal processing function applied to the nerve signals. Since the frequency of these signals is below 10 kHz, a switched-capacitor architecture has been used. The circuit comprises an always-valid sample and hold circuit followed by a full wave rectifier. The bin-integration is then performed with three resettable integration stages. The third stage is reset in such a way to use the maximum range of the ADC. The resulting RBI signal is then converted to digital and transferred to the implant central processor where information about bladder could be extracted. The circuit has been realized in CMOS 0.35 /spl mu/m, 3.3 V technology. The design, simulation and measurement results of the proposed interface are presented. At 1.3 V supply, the measured circuit obtains an RBI error of less than -45 dB for a sinewave input of 7.2 kHz that is the main component of the nerve signal and a dynamic range of /spl plusmn/1.1 V while dissipating 578 /spl mu/W and occupying a chip area of 5.83 mm/sup 2/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".