An Analog Low-Power Highly-Accurate Link-Adaptive Energy Storage Efficiency Maximizer for Resonant CM Wireless Power Receivers
Why this work is in the frame
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Bibliographic record
Abstract
Summary form only given. The power delivered wirelessly to implantable neural interfaces supplies two categories of loads with distinct consumption patterns: small-and-continuous (e.g., recording circuits, signal conditioning) or large-and-intermittent (e.g., electrical stimulation, wireless transmission). For the weakly-coupled mm-scale implants, the induced power level at the receiver coil (Rx) is typically far below the required instantaneous power of the large-intermittent loads. Therefore, these loads could only be supplied through storage of excess incoming energy during their off cycles. As such, the energy storage efficiency determines how often and how powerful a high-power event (e.g., data transmission, stimulation) could take place. Motivated by this, a variety of circuit ideas for energy delivery optimization are reported, mostly focused on current-mode (CM) receivers, mainly due to their superior performance in weakly-coupled links (compared to voltage-mode receivers). However, the optimization is either done only for resistive loads (i.e., not optimizing storage efficiency), or done pre-operation (i.e., offline), hence, not adaptive to link variations (e.g., implant movements, media changes, etc.). We present a low-power integrated circuit (IC) that senses the peak voltage at the Rx coil (V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RX(peak)</sub> ), calculates the optimal timing scheme for maximum energy storage efficiency in real time, and operates the CM receiver accordingly. This closed-loop scheme makes the presented work adaptive to any link variation and needless of calibration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it