Sai Manoj Pudukotai Dinakarrao, Assistant Professor, Electrical and Computer Engineering, received funding for the project: “CLAWS: Continuous and Lightweight Authentication for Wearable and Portable Embedded Systems.”
Sai Manoj Pudukotai Dinakarrao, Assistant Professor, Electrical and Computer Engineering, received funding for the project: “CLAWS: Continuous and Lightweight Authentication for Wearable and Portable Embedded Systems.”
“The target of this funding is to accelerate the transition of technology,” Pudukotai Dinakarrao said.
Using this proposed authentication technique, Pudukotai Dinakarrao will collect the gait signal of a user continually using a lightweight always-on sensing methodology. The collected gait signal will be analyzed through resource-aware dynamic early-exit neural networks (EENets) for authentication.
The proposed technique can be seamlessly integrated with existing devices, including smartwatches, earbuds, and other portal (electronic) devices, due to inherent processing capabilities and hardware sensors that enable extraction of gait signals. Furthermore, as the gait signals are continuously collected, the analysis can be performed continually as opposed to via one-time authentication.
Unlike traditional convolutional neural networks (CNNs) and deep neural networks (DNNs), the deployed EENets terminate the computations as soon as a specified confidence in authentication is obtained.
Pudukotai Dinakarrao received $50,000 from the Virginia Innovation Partnership Authority for this project. Funding began in May 2024 and will end in late June 2025.
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