What is he best known for?
The fields of study he is best known for:
- Computer network
- The Internet
- Artificial intelligence
Yusheng Ji mainly investigates Computer network, Distributed computing, Wireless, Network packet and Vehicular ad hoc network.
The study of Computer network is intertwined with the study of Wireless ad hoc network in a number of ways.
His studies in Distributed computing integrate themes in fields like Cluster analysis, Mobile computing, Mobile device and Mobile edge computing.
In general Wireless, his work in Throughput is often linked to Noise measurement linking many areas of study.
His biological study spans a wide range of topics, including Multiprotocol Label Switching, Control reconfiguration and Logical topology.
His studies deal with areas such as Mobile radio and Real time video as well as Vehicular ad hoc network.
His most cited work include:
- RF-Sensing of Activities from Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals (182 citations)
- Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning (171 citations)
- Multi-User Computation Partitioning for Latency Sensitive Mobile Cloud Applications (136 citations)
What are the main themes of his work throughout his whole career to date?
Yusheng Ji mainly focuses on Computer network, Distributed computing, Network packet, Real-time computing and Wireless.
The concepts of his Computer network study are interwoven with issues in Wireless ad hoc network and Vehicular ad hoc network.
Yusheng Ji interconnects Wireless Routing Protocol, Routing protocol, Scheduling, Edge computing and Server in the investigation of issues within Distributed computing.
His Real-time computing research is multidisciplinary, incorporating elements of Transmission and Video quality.
Wireless and Communication channel are frequently intertwined in his study.
His work investigates the relationship between Resource allocation and topics such as Throughput that intersect with problems in Orthogonal frequency-division multiplexing.
He most often published in these fields:
- Computer network (62.95%)
- Distributed computing (25.68%)
- Network packet (15.37%)
What were the highlights of his more recent work (between 2018-2021)?
- Distributed computing (25.68%)
- Computer network (62.95%)
- Edge computing (4.84%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Distributed computing, Computer network, Edge computing, Real-time computing and Mobile edge computing.
The Distributed computing study combines topics in areas such as Markov decision process, Enhanced Data Rates for GSM Evolution, Server and Reinforcement learning.
His Computer network study frequently intersects with other fields, such as Vehicular ad hoc network.
The various areas that Yusheng Ji examines in his Edge computing study include Wireless, Optimization problem and Resource allocation.
His work carried out in the field of Real-time computing brings together such families of science as Point cloud and Video quality.
His work deals with themes such as Scheduling, Mobile device and Mobile computing, which intersect with Mobile edge computing.
Between 2018 and 2021, his most popular works were:
- Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning (171 citations)
- Mobile Edge Computing for the Internet of Vehicles: Offloading Framework and Job Scheduling (57 citations)
- Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing (55 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Artificial intelligence
The scientist’s investigation covers issues in Computer network, Mobile edge computing, Distributed computing, Vehicular ad hoc network and Wireless.
His Computer network study typically links adjacent topics like Edge computing.
His research investigates the connection between Mobile edge computing and topics such as Scheduling that intersect with issues in Cellular network.
His Distributed computing research is multidisciplinary, incorporating elements of Time complexity, Quality of service, Mobile device and Reinforcement learning.
His IEEE 802.11p study, which is part of a larger body of work in Vehicular ad hoc network, is frequently linked to Collaborative learning, Drone and Terrain, bridging the gap between disciplines.
His studies in Wireless integrate themes in fields like Wireless sensor network, CHAOS, Communication channel and Big data.
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