A Low-Intensity Pulsed Ultrasound Interface ASIC for Wearable Medical Therapeutic Device Applications
Bibliographic record
Abstract
Low-intensity pulsed ultrasound (LIPUS) is a non-invasive medical therapy that has attracted recent research interest due to its therapeutic effects. However, most LIPUS driver systems currently available are large and expensive. We have proposed a LIPUS interface application-specific integrated circuit (ASIC) for use in wearable medical devices to address some of the challenges related to the size and cost of the current technologies. The proposed ASIC is a highly integrated system, incorporating a DCDC module based on a charge pump architecture, a high voltage level shifter, a half-bridge driver, a voltage-controlled oscillator, and a corresponding digital circuit module. Consequently, the functional realization of this ASIC as a LIPUS driver system requires only a few passive components. Experimental tests indicated that the chip is capable of an output of 184.2 mW or 107.2 mW with a power supply of 5 V or 3.7 V, respectively, and its power conversion efficiency is approximately 30%. This power output capacity allows the LIPUS driver system to deliver a spatial average temporal average (SATA) of 29.5 mW/cm2 or 51.6 mW/cm2 with a power supply of 3.7 V or 5 V, respectively. The total die area, including pads, is 4 mm2. The ASIC does not require inductors, improving its magnetic resonance imaging (MRI) compatibility. In summary, the proposed LIPUS interface chip presents a promising solution for the development of MRI-compatible and cost-effective wearable medical therapy devices.
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 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 | 0.001 |
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".