An Energy Harvesting Roadside Unit communication load prediction and energy scheduling based on graph convolutional neural networks for spatial‐temporal vehicle data

Stochastic Routing and Scheduling Policies for Energy Harvesting Communication Networks

In this paper, we study the joint routing-scheduling problem in energy harvesting communication networks. Our policies, which are based on stochastic subgradient methods on the dual domain, act as an energy harvesting variant of the stochastic family of backpresure algorithms. Specifically, we propose two policies: (i) the Stochastic Backpressure with Energy Harvesting (SBP-EH), in which a node...

Service Scheduling of Vehicle-Roadside Data Access

As vehicular networks become popular, more and more people want to access data from their vehicles. When many vehicles want to access data through a roadside unit, service scheduling becomes an important issue. In this paper, we identify some challenges in vehicle-roadside data access. As vehicles move pretty fast, the requests should be served quickly. Also, vehicles may upload data to the roa...

Energy Propagation in Deep Convolutional Neural Networks

Many practical machine learning tasks employ very deep convolutional neural networks. Such large depths pose formidable computational challenges in training and operating the network. It is therefore important to understand how many layers are actually needed to have most of the input signal’s features be contained in the feature vector generated by the network. This question can be formalized ...

Graph Convolutional Neural Networks for ADME Prediction in Drug Discovery

ADME in-silico methods have grown increasingly powerful over the past twenty years, driven by advances in machine learning and the abundance of high-quality training data generated by laboratory automation. Meanwhile, in the technology industry, deep-learning has taken o↵, driven by advances in topology design, computation, and data. The key premise of these methods is that the model is able to...

Developing an Artificial Neural Networks Model for Predicting Output Energy and GHG Emission of Strawberry Production