Unmanned aerial vehicles (UAVs) have been widely used in military and civilian fields by
carrying out intelligent applications with deep learning technologies, such as battlefield …

History has demonstrated that healthcare and medical systems play a crucial role in
enforcing the development of science and technology. Humans have been seeing an …

Edge intelligence has emerged as a promising paradigm to accelerate DNN inference by
model partitioning, which is particularly useful for intelligent scenarios that demand high …

Mobile devices are becoming increasingly capable of delivering intelligent services by
leveraging deep learning architectures such as deep neural networks (DNNs). However …

Collaborative intelligence (CI) is an approach used to accelerate deep neural network
computations on IoT edge devices by offloading certain DNN computations to high …

Abstract Cloud-Edge-End Collaboration (CEEC) computing architecture inherits many merits
from both edge computing and cloud computing and thus is considered as a promising …

Distributed computing continuum systems (DCCS) make use of a vast number of computing
devices to process data generated by edge devices such as the Internet of Things and …

With the emergence of Industry 5.0, there has been a significant surge in the need for
intelligent services within the realm of smart devices. Currently, deep neural networks …

The substantial computational demands associated with Deep Neural Network (DNN)-
based camera relocalization during the reasoning process impede their integration into …

The recent migration towards Internet of Things determined the rise of a Computing
Continuum paradigm where Edge and Cloud resources coordinate to support the execution …