Simulated photorealistic deep learning framework and workflows to accelerate computer vision and unmanned aerial vehicle research
Deep learning (DL) is producing state-of-the-art results in a number of unmanned aerial
vehicle (UAV) tasks from low level signal processing to object detection, 3D mapping …
vehicle (UAV) tasks from low level signal processing to object detection, 3D mapping …
Two time-scale joint service caching and task offloading for uav-assisted mobile edge computing
The emergence of unmanned aerial vehicles (UAVs) extends the mobile edge computing
(MEC) services in broader coverage to offer new flexible and low-latency computing …
(MEC) services in broader coverage to offer new flexible and low-latency computing …
Mixture of pre-processing experts model for noise robust deep learning on resource constrained platforms
Deep learning on an edge device requires energy efficient operation due to ever
diminishing power budget. Intentional low quality data during the data acquisition for longer …
diminishing power budget. Intentional low quality data during the data acquisition for longer …
UAV-enhanced intelligent offloading for Internet of Things at the edge
With the explosive growth of diverse Internet of Things (IoT) applications, mobile edge
computing (MEC) has been brought to settle the conflict between computation-intensive …
computing (MEC) has been brought to settle the conflict between computation-intensive …
[HTML][HTML] Resource allocation and offloading strategy for UAV-assisted LEO satellite edge computing
In emergency situations, such as earthquakes, landslides and other natural disasters, the
terrestrial communications infrastructure is severely disrupted and unable to provide …
terrestrial communications infrastructure is severely disrupted and unable to provide …
A mobile edge computing framework for task offloading and resource allocation in UAV-assisted VANETs
In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial
vehicle (UAV)-assisted vehicular ad hoc networks (VANETs) architecture, based on which a …
vehicle (UAV)-assisted vehicular ad hoc networks (VANETs) architecture, based on which a …
Two-Stage Self-Adaptive Task Outsourcing Decision Making for Edge-Assisted Multi-UAV Networks
S Jung, C Park, M Levorato, JH Kim… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
This paper proposes a two-stage novel algorithm for intelligent edge-assisted multiple
unmanned aerial vehicles (UAVs) surveillance services. In the first stage, multiple UAVs …
unmanned aerial vehicles (UAVs) surveillance services. In the first stage, multiple UAVs …
A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones
Accurate navigation is of paramount importance to ensure flight safety and efficiency for
autonomous drones. Recent research starts to use Deep Neural Networks (DNN) to …
autonomous drones. Recent research starts to use Deep Neural Networks (DNN) to …
[HTML][HTML] Task unloading strategy of multi UAV for transmission line inspection based on deep reinforcement learning
H Shen, Y Jiang, F Deng, Y Shan - Electronics, 2022 - mdpi.com
Due to the limitation of the computing power and energy resources, an unmanned aerial
vehicle (UAV) team usually offloads the inspection task to the cloud for processing when …
vehicle (UAV) team usually offloads the inspection task to the cloud for processing when …
Energy-efficient UAV-enabled computation offloading for industrial internet of things: a deep reinforcement learning approach
Abstract Industrial Internet of Things (IIoT) has been envisioned as a killer application of 5G
and beyond. However, due to the shortness of computation ablility and batery capacity, it is …
and beyond. However, due to the shortness of computation ablility and batery capacity, it is …