The modern smart digital healthcare system (SDHS) is leaning towards automation of
patient disease monitoring and treatment with the advent of wireless body sensor networks …

Physiological computing uses human physiological data as system inputs in real time. It
includes, or significantly overlaps with, brain-computer interfaces, affective computing …

Deep learning (DL) models are frequently employed to extract valuable features from
heterogeneous and high-dimensional healthcare data, which are used to keep track of …

Smart healthcare systems are gaining popularity with the rapid development of intelligent
sensors, the Internet of Things (IoT) applications and services, and wireless …

In recent years, the combination of wireless body sensor networks (WBSNs) and the Internet
ofc Medical Things (IoMT) marked a transformative era in healthcare technology. This …

Physiological computing uses human physiological data as system inputs in real time. It
includes, or significantly overlaps with, brain-computer interfaces, affective computing …

In autonomous driving, millimeter wave (mmWave) radar has been widely adopted for object
detection because of its robustness and reliability under various weather and lighting …

Deep learning and additive manufacturing have progressed together in the previous couple
of decades. Despite being one of the most promising technologies, they have several flaws …

Modern cyber-physical systems (CPSs) use the Internet of Things (IoT) to collect and
exchange data efficiently, monitor device/sensor level interaction effectively, and adopt new …

In the past decades, the rise of artificial intelligence has given us the capabilities to solve the
most challenging problems in our day-to-day lives, such as cancer prediction and …