In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor
data, an increasing number of actuators, and data-intensive algorithms, the development of …

Intelligent edge vision tasks encounter the critical challenge of ensuring power and latency
efficiency due to the typically heavy computational load they impose on edge platforms. This …

Safety, low-cost, small size, and Artificial Intelli-gence (AI) capabilities of drones have led to
the proliferation of autonomous tiny Unmanned Aerial Vehicles (UAVs) in many applications …

With the evaluation of Artificial Intelligence (AI), there has been a resurgence of interest in
how to use AI algorithms on low-power embedded systems to broaden potential use cases …

Resource optimization, timely data capture, and efficient unmanned aerial vehicle (UAV)
operations are of utmost importance for mission success. Latency, bandwidth constraints …

Nano-sized unmanned aerial vehicles (UAVs) are ideal candidates for flying Internet-of-
Things smart sensors to collect information in narrow spaces. This requires ultra-fast …

Artificial intelligence (AI) on the edge has emerged as an important research area in the last
decade to deploy different applications in the domains of computer vision and natural …

Edge Point of Care (POC) devices are crucial for human activity recognition (HAR) and fall
detection because they enable real-time analysis and fast intervention, which can greatly …

Edge intelligence has emerged as a result of the merging of edge computing and artificial
intelligence (AI) technologies. Nowadays, there is a lot of discussion on how to efficiently …

First responders (FRs) are the first to arrive after a disaster. It is dangerous for them to enter
the disaster area without knowing anything about the situation. In order to improve FRs' …