This $300,000 Project Grant awarded by the National Science Foundation's Engineering program (CFDA 47.041) aims to develop batteryless technology for Internet of Things (IoT) devices, particularly in wearable applications such as fitness trackers, smartwatches, and medical devices. The project will integrate artificial intelligence (AI) and deep learning techniques to harness the potential of batteryless sensors for personalized data analytics. Key objectives include: Developing new deep learning algorithms tailored for the unstructured data generated by batteryless sensors, and optimizing energy usage strategies of batteryless sensors with the requirements of machine learning models. Creating a novel approach for selecting sensors specifically for batteryless systems, and building real prototypes using kinetic energy harvesting to validate simulations. Establishing an interdisciplinary, research-based curriculum that combines machine learning and batteryless system design to foster a new generation of innovators. The project, awarded on September 1, 2024, will be executed by the University of Texas at Austin over a 3-year period ending on August 31, 2027. No subawards are planned for this grant.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $300.0k | 8/13/24 |