What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Statistics
Computer network, Wireless, Quality of service, Real-time computing and Wireless network are his primary areas of study.
His Computer network study combines topics in areas such as Wireless ad hoc network and Distributed computing.
The concepts of his Wireless study are interwoven with issues in Directional antenna and Mobile computing.
His Quality of service study integrates concerns from other disciplines, such as Service quality, The Internet, Wireless broadband and Channel capacity.
The various areas that Dapeng Wu examines in his Real-time computing study include Image quality, Vehicle dynamics, Encoder and Online search.
His Wireless network research incorporates elements of Broadband networks, Mobile identification number and Mobile device.
His most cited work include:
- Effective capacity: a wireless link model for support of quality of service (1289 citations)
- Streaming video over the Internet: approaches and directions (650 citations)
- SORI: a secure and objective reputation-based incentive scheme for ad-hoc networks (385 citations)
What are the main themes of his work throughout his whole career to date?
Dapeng Wu mainly investigates Computer network, Artificial intelligence, Communication channel, Wireless and Real-time computing.
The study incorporates disciplines such as Wireless network, Throughput and Wireless ad hoc network in addition to Computer network.
Dapeng Wu has researched Artificial intelligence in several fields, including Machine learning, Computer vision and Pattern recognition.
His Communication channel research includes themes of Transmitter and Algorithm.
His research on Wireless often connects related topics like Computer security.
His research in Real-time computing intersects with topics in Energy consumption, Encoder, Scalable Video Coding and Wireless sensor network.
He most often published in these fields:
- Computer network (37.31%)
- Artificial intelligence (24.16%)
- Communication channel (16.51%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (24.16%)
- Distributed computing (10.40%)
- Pattern recognition (7.34%)
In recent papers he was focusing on the following fields of study:
Dapeng Wu spends much of his time researching Artificial intelligence, Distributed computing, Pattern recognition, Efficient energy use and Wireless network.
He interconnects Computer vision and Natural language processing in the investigation of issues within Artificial intelligence.
His Distributed computing research incorporates themes from Regret, Scheduling, Edge computing, Server and Reinforcement learning.
Dapeng Wu has included themes like Resource allocation, Mathematical optimization, Maximization, Power control and Quality of service in his Efficient energy use study.
His study on Wireless network also encompasses disciplines like
- Transmitter power output that intertwine with fields like Mobile broadband,
- Multiplexing and related Queue, Random access, Latency and Computer network,
- Service together with Deep neural networks, Attack model and Computer security.
In his work, Wireless is strongly intertwined with Noise, which is a subfield of Computer network.
Between 2018 and 2021, his most popular works were:
- Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation (39 citations)
- Millimeter-Wave Propagation Modeling and Measurements for 5G Mobile Networks (22 citations)
- Privacy-Preserving Online Task Allocation in Edge-Computing-Enabled Massive Crowdsensing (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Statistics
His scientific interests lie mostly in Artificial intelligence, Reinforcement learning, Convolutional neural network, Differential privacy and Edge computing.
His work investigates the relationship between Artificial intelligence and topics such as Natural language processing that intersect with problems in Image and Artificial neural network.
His Reinforcement learning research includes themes of Energy consumption, Real-time computing and Efficient energy use.
The Edge computing study combines topics in areas such as Distributed computing and Smart city.
His studies deal with areas such as Edge device, Industry 4.0, Wireless sensor network and Server, Mobile edge computing as well as Distributed computing.
His study in Feature extraction is interdisciplinary in nature, drawing from both False alarm, Depth map, Image quality, Cyclopean image and Image segmentation.
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