Due to the ever-growing powers in sensing, computing, communicating and storing, mobile
devices (eg, smartphone, smartwatch, smart glasses) become ubiquitous and an …

Human activity recognition (HAR) is a key enabling technology for a wide range of emerging
applications. Although multimodal sensing systems are essential for capturing complex and …

Human Activity Recognition (HAR) is a rapidly evolving field with the potential to
revolutionise how we monitor and understand human behaviour. This survey paper provides …

Recent developments in Large Language Models (LLMs) have demonstrated their
remarkable capabilities across a range of tasks. Questions, however, persist about the …

Existing inertial measurement unit (IMU) based human activity recognition (HAR)
approaches still face a major challenge when adopted across users in practice. The severe …

Low-density parity-check (LDPC) codes have been widely used for Forward Error Correction
(FEC) in wireless networks because they can approach the capacity of wireless links with …

Multi-modal sensing systems are increasingly prevalent in real-world applications such as
health monitoring and autonomous driving. Most multi-modal learning approaches need to …

Self-supervised learning (SSL) has emerged as a promising alternative to purely supervised
learning, since it can learn from labeled and unlabeled data using a pre-train-then-fine-tune …

Natural language processing (NLP) sees rich mobile applications. To support various
language understanding tasks, a foundation NLP model is often fine-tuned in a federated …

Deep Learning (DL) models have been widely deployed on IoT devices with the help of
advancements in DL algorithms and chips. However, the limited resources of edge devices …