What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Electromyography, Pattern recognition, Speech recognition and Feature extraction.
His Artificial intelligence study combines topics in areas such as Middle finger and Computer vision.
His Electromyography research includes themes of Time domain, Feature, Biomedical engineering, Hand motion and Robustness.
His research in Pattern recognition intersects with topics in Covariance matrix and Brain–computer interface.
His work deals with themes such as Rehabilitation, Open-loop controller, Pattern recognition system and Prosthesis, which intersect with Speech recognition.
His study looks at the relationship between Feature extraction and topics such as Feature, which overlap with Cluster analysis.
His most cited work include:
- Synthesis of force-closure grasps on 3-D objects based on the Q distance (170 citations)
- A survey on dielectric elastomer actuators for soft robots. (149 citations)
- Soft wall-climbing robots. (98 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Brain–computer interface, Pattern recognition, Electromyography and Control theory are his primary areas of study.
The various areas that Xiangyang Zhu examines in his Artificial intelligence study include Machine learning, Speech recognition and Computer vision.
As part of one scientific family, Xiangyang Zhu deals mainly with the area of Brain–computer interface, narrowing it down to issues related to the Sensation, and often Stimulus.
Xiangyang Zhu usually deals with Electromyography and limits it to topics linked to Forearm and Physical medicine and rehabilitation.
Xiangyang Zhu has included themes like Control engineering and Robot in his Control theory study.
His Electroencephalography research incorporates themes from Simulation and Audiology.
He most often published in these fields:
- Artificial intelligence (34.03%)
- Brain–computer interface (17.36%)
- Pattern recognition (17.36%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (34.03%)
- Robot (11.46%)
- Electromyography (17.01%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Robot, Electromyography, Electroencephalography and Actuator.
His biological study spans a wide range of topics, including Wrist, Computer vision and Pattern recognition.
His research in Robot intersects with topics in Automation, Trajectory, Control theory and Interpolation.
His Electromyography research incorporates elements of Motor unit and Torque.
Many of his research projects under Electroencephalography are closely connected to Amplitude with Amplitude, tying the diverse disciplines of science together.
His Actuator study combines topics from a wide range of disciplines, such as Mechanical engineering and Bending.
Between 2017 and 2021, his most popular works were:
- Soft wall-climbing robots. (98 citations)
- Automatic Design of Soft Dielectric Elastomer Actuators With Optimal Spatial Electric Fields (24 citations)
- Hand Gesture Recognition and Finger Angle Estimation via Wrist-Worn Modified Barometric Pressure Sensing (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Mechanical engineering
Xiangyang Zhu mostly deals with Brain–computer interface, Simulation, Artificial intelligence, Robot and Electroencephalography.
His research integrates issues of Finger joint and Pattern recognition in his study of Artificial intelligence.
His work carried out in the field of Pattern recognition brings together such families of science as Proportional control, Electromyography and Autoencoder.
The Robot study combines topics in areas such as Trajectory, Control theory and Interpolation.
The concepts of his Control theory study are interwoven with issues in Humanoid robot and Motion Mode.
His study in Electroencephalography is interdisciplinary in nature, drawing from both Rehabilitation, Somatosensory system, Sensory stimulation therapy and Audiology.
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