His scientific interests lie mostly in Computer network, Distributed computing, Wireless, Wireless network and Real-time computing. His Computer network study incorporates themes from Wireless ad hoc network and Video quality. His Distributed computing research incorporates themes from MIMO, Scheduling, Mobile computing and Greedy algorithm.
His work deals with themes such as Quality of service, Cognitive radio, Destination-Sequenced Distance Vector routing, Testbed and Coding, which intersect with Wireless network. His work carried out in the field of Real-time computing brings together such families of science as Channel state information, Orthogonal frequency-division multiplexing and Implementation. His research in Smart grid intersects with topics in Data center, Data science and Big data.
Shiwen Mao focuses on Computer network, Distributed computing, Wireless network, Real-time computing and Wireless. His Computer network research includes themes of Cognitive radio, Communication channel, Wireless ad hoc network and Throughput. His biological study spans a wide range of topics, including Wireless mesh network, Resource allocation and Multipath routing.
His studies deal with areas such as Wireless sensor network and Power control as well as Wireless network. The concepts of his Real-time computing study are interwoven with issues in Scheduling, Channel state information and Mobile device. His studies in Scheduling integrate themes in fields like Mathematical optimization and Smart grid.
Shiwen Mao mainly focuses on Real-time computing, Artificial intelligence, Deep learning, Wireless network and Wireless. Shiwen Mao has included themes like Software deployment, Channel state information, Mobile device and Respiration rate in his Real-time computing study. His study in Wireless network is interdisciplinary in nature, drawing from both Scalability, Distributed computing, Neighbor Discovery Protocol, Optimization problem and Reinforcement learning.
The study incorporates disciplines such as Enhanced Data Rates for GSM Evolution and Logical reasoning in addition to Distributed computing. His study on Wireless also encompasses disciplines like
His primary scientific interests are in Real-time computing, Artificial intelligence, Mobile device, Artificial neural network and Deep learning. His Real-time computing research includes elements of Respiration rate and Ultra high frequency. His Mobile device study combines topics in areas such as RSS, Phase, Software deployment and Channel state information.
As part of one scientific family, Shiwen Mao deals mainly with the area of Phase, narrowing it down to issues related to the Orthogonal frequency-division multiplexing, and often Convolutional neural network and Wireless. Wireless network is the focus of his Wireless research. His Deep learning research is multidisciplinary, relying on both Signal, Communications system and Pattern recognition.
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Big Data: A Survey
Min Chen;Shiwen Mao;Yunhao Liu.
Mobile Networks and Applications (2014)
CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach
Xuyu Wang;Lingjun Gao;Shiwen Mao;Santosh Pandey.
IEEE Transactions on Vehicular Technology (2017)
Big Data: Related Technologies, Challenges and Future Prospects
Min Chen;Shiwen Mao;Yin Zhang;Victor C. M. Leung.
Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning
Xianfu Chen;Honggang Zhang;Celimuge Wu;Shiwen Mao.
IEEE Internet of Things Journal (2019)
Multiobjective Optimization for Computation Offloading in Fog Computing
Liqing Liu;Zheng Chang;Xijuan Guo;Shiwen Mao.
IEEE Internet of Things Journal (2018)
Video transport over ad hoc networks: multistream coding with multipath transport
Shiwen Mao;Shunan Lin;S.S. Panwar;Yao Wang.
IEEE Journal on Selected Areas in Communications (2003)
CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach
Xuyu Wang;Lingjun Gao;Shiwen Mao.
IEEE Internet of Things Journal (2016)
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
Yaohua Sun;Mugen Peng;Yangcheng Zhou;Yuzhe Huang.
IEEE Communications Surveys and Tutorials (2019)
DeepFi: Deep learning for indoor fingerprinting using channel state information
Xuyu Wang;Lingjun Gao;Shiwen Mao;Santosh Pandey.
wireless communications and networking conference (2015)
Performance Evaluation of Cognitive Radios: Metrics, Utility Functions, and Methodology
Youping Zhao;Shiwen Mao;J.O. Neel;J.H. Reed.
Proceedings of the IEEE (2009)
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