What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Operating system
Wireless sensor network, Computer network, Artificial intelligence, Real-time computing and Key distribution in wireless sensor networks are his primary areas of study.
His Wireless sensor network study incorporates themes from Image sensor, Visual sensor network, Wireless network, Node and Algorithm.
His Computer network research incorporates themes from Energy consumption, Budget constraint, Distributed computing and Service quality.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Computer vision and Pattern recognition.
He combines subjects such as Simulation and Software deployment with his study of Real-time computing.
His biological study deals with issues like Scheduling, which deal with fields such as Source-specific multicast, Multicast address, IP multicast, Protocol Independent Multicast and Reliable multicast.
His most cited work include:
- Opportunities in mobile crowd sensing (322 citations)
- How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint (284 citations)
- Internet of things: objectives and scientific challenges (185 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer network, Wireless sensor network, Computer vision and Distributed computing.
His biological study spans a wide range of topics, including Machine learning and Pattern recognition.
In the field of Computer network, his study on Network packet and Node overlaps with subjects such as Mobile telephony.
His Wireless sensor network study also includes fields such as
- Real-time computing and related Scheduling,
- Wireless network and related Electronic engineering.
His Computer vision research incorporates elements of Frame and Process.
His Distributed computing study combines topics in areas such as Cloud computing and Efficient energy use.
He most often published in these fields:
- Artificial intelligence (34.43%)
- Computer network (23.77%)
- Wireless sensor network (22.95%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (34.43%)
- Computer network (23.77%)
- Feature extraction (9.56%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computer network, Feature extraction, Convolutional neural network and Real-time computing.
His studies deal with areas such as Machine learning, Computer vision and Pattern recognition as well as Artificial intelligence.
Huadong Ma works mostly in the field of Machine learning, limiting it down to topics relating to Benchmark and, in certain cases, Focus.
His work in Computer network tackles topics such as Enhanced Data Rates for GSM Evolution which are related to areas like Distributed computing.
As a part of the same scientific study, Huadong Ma usually deals with the Real-time computing, concentrating on Robot and frequently concerns with Relay and Wireless.
His research integrates issues of Wireless sensor network, Bottleneck, Bandwidth and Transmission in his study of Wireless.
Between 2017 and 2021, his most popular works were:
- PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance (141 citations)
- T-C3D: Temporal Convolutional 3D Network for Real-Time Action Recognition. (54 citations)
- A Progressive Search Paradigm for the Internet of Things (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Operating system
Huadong Ma mainly focuses on Artificial intelligence, Feature extraction, Convolutional neural network, Computer vision and Deep learning.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, License and Pattern recognition.
His work on Discriminative model as part of general Pattern recognition study is frequently connected to Gait and Gait, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The Computer vision study combines topics in areas such as Timestamp, Visualization, Indexer and Search engine.
His Deep learning research is multidisciplinary, incorporating perspectives in Annotation, Information retrieval, Metric and Benchmark.
As a member of one scientific family, Huadong Ma mostly works in the field of Real-time computing, focusing on Trajectory and, on occasion, Wireless.
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