Edge Intelligence (EI) has been instrumental in delivering real-time, localized services by
leveraging the computational capabilities of edge networks. The integration of Large …

Localization is a critical technology for various applications ranging from navigation and
surveillance to assisted living. Localization systems typically fuse information from sensors …

The increasing popularity of virtual reality (VR) has stressed the importance of authenticating
VR users while preserving their privacy. Behavioral biometrics, owing to their robustness …

The demand for fully on-device health monitoring is huge and urgent. However, deploying
Large Foundation Models conventionally relies on cloud-based computing services, which …

Multimodal sensing systems are increasingly prevalent in various real-world applications.
Most existing multimodal learning approaches heavily rely on training with a large amount of …

Despite the extensive use of sensors enabling intelligent applications, the complementary
potential of co-existing sensor systems is often not fully utilized, limiting more advanced …

Many mobile sensing applications utilize data from various modalities, including motion and
physiological sensors in mobile and wearable devices. Federated Learning (FL) is …

With the increasing demand for smart health applications that emphasize privacy and
efficiency, we introduce AD-CLIP, a synthetic data generation framework using CLIP-based …

Federated learning enables decentralized model training without sharing raw data,
preserving data privacy. However, its vulnerability towards critical security threats, such as …

The emergence of advanced data analysis techniques has revolutionized patient healthcare
by enabling the early and efficient detection of diseases. Traditionally, disease identification …