Controlling wireless power transfer by tuning and detuning resonance of telemetric devices for rodents
Bibliographic record
Abstract
Telemetry acquisition from rodents is important in biomedical research, where rodent behavior data is used to study disease models. Telemetry devices for such data acquisition require a long-term powering method. Wireless power transfer (WPT) via magnetic resonant coupling can provide continuous power to multiple small telemetric devices. Our loosely coupled WPT (LCWPT) system consists of a stationary primary coil and multiple freely moving secondary coils. Our previous LCWPT system was designed to transfer reasonable power to secondary coils at poor orientations but transfers excessively high amounts of power at favorable orientations. Reasonable power is needed for telemetry and radio electronics, but highly induced voltage on the secondary coil creates excess energy which must be dissipated by previous devices, and caused problems (localized heat damage and variations in component properties) leading to drift in operating frequency. To remedy these two problems, a novel scheme is proposed to automatically tune or detune the resonant frequency of the secondary circuit. Our closed-loop controlled tuning or detuning (CTD) approach can be used to prevent excessive power transfer by detuning, or to improve power transfer by tuning, depending on the need. Furthermore, this novel CTD scheme facilitates the use of multiple telemetric devices.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".