A Low-Voltage Low-Power Implantable Telemonitoring System with Application to Endo-Hyperthermia Treatment of In-Stent Restenosis
Bibliographic record
Abstract
This paper presents a low-voltage low-power implantable telemonitoring system in the context of a smart stent that uses wireless endo-hyperthermia for the treatment of in-stent restenosis. More specifically, an application specific integrated circuit (ASIC) is designed and implemented that senses the ambient temperature and wirelessly transmits the sensory information to a nearby hub. A customized “smart” stent is used as an antenna for wireless data and power transfer over the unlicensed industrial, scientific, and medical (ISM) 915 MHz and 2.4 GHz bands, respectively. For the prototype design, the ASIC is embedded on the small platform at the end of the custom-made stent that also serves as an antenna and the circuit functions without requiring any off-chip components. The proposed fully integrated solution has the following functionalities: radio-frequency (RF) telemetry, power management unit (RF -to-DC converter and voltage regulation), and temperature sensing. The proof-of-concept prototype ASIC is designed and fabricated in a 0.13-μm CMOS process and has a chip area of 1.56 mm2. The device can detect and response to the temperature variations in the range of 30 to 50 °C. The remote power link is established when the power received by the implantable device is about -8 dBm. The data can be transmitted from the ASIC to an external hub at the power level of -28.38 dBm, with the total power consumption of 109.6 μW.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".