Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Currently, the world is experiencing the rapid spread of Coronavirus Disease 2019 (COVID-
19). Since the epidemic continues to take a devastating impact on the society, economy, and …

Deep neural networks (DNN) are the de-facto solution behind many intelligent applications
of today, ranging from machine translation to autonomous driving. DNNs are accurate but …

Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed
intelligence of industrial automation thanks to its capability of distributed machine learning …

For many decades, progress in computing hardware has been closely associated with
CMOS logic density, performance, and cost. As such, slowdown in 2-D scaling, frequency …

5G networks and Internet of Things (IoT) offer a powerful platform for ubiquitous
environments with their ubiquitous sensing, high speeds and other benefits. The data …

As digitization advances, the question of its sustainability arises. In response, the concept of
Digitization Footprint (DF) was introduced to quantify aspects like data volume, time, effort …

Implementing either Federated learning (FL) or split learning (SL) over clients with limited
computation/communication resources faces challenges on achieving delay-efficient model …

Inference carried out on pretrained deep neural networks (DNNs) is particularly effective as
it does not require retraining and entails no loss in accuracy. Unfortunately, resource …

Fog networks offer computing resources of varying capacities at different distances from end
users. A Fog Node (FN) closer to the network edge may have less powerful computing …