As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

In this tutorial paper, we look into the evolution and prospect of network architecture and
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …

Machine learning (ML) is powering a rapidly-increasing number of web applications. As a
crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model …

The rapid evolution of multimedia and computer vision technologies requires adaptive visual
model deployment strategies to effectively handle diverse tasks and varying environments …

The proliferation of edge devices has pushed computing from the cloud to the data sources,
and video analytics is among the most promising applications of edge computing. Running …

Deep neural networks (DNNs) have been widely adopted for various mobile inference tasks,
yet their ever-increasing computational demands are hindering their deployment on …

With the rapid development of edge computing in the post-COVID19 pandemic period,
precise workload forecasting is considered the basis for making full use of the edge limited …

Given the wide adoption of multimodal sensors (eg, camera, lidar, radar) by autonomous
vehicle s (AVs), deep analytics to fuse their outputs for a robust perception become …

Deep learning has been used in various domains, including Web services. Convolutional
neural networks (CNNs), which are deep learning representatives, are among the most …

The object inference for augmented reality (AR) requires a precise object localization within
user's physical environment and the adaptability to dynamic communication conditions …