What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
His main research concerns Compressed sensing, Artificial intelligence, Simulation, Transport engineering and Estimation.
His work deals with themes such as Image, Wavelet, Iterative reconstruction, Computer vision and Signal processing, which intersect with Compressed sensing.
His Artificial intelligence study frequently links to related topics such as Pattern recognition.
The Classifier research he does as part of his general Pattern recognition study is frequently linked to other disciplines of science, such as Neuroimaging, therefore creating a link between diverse domains of science.
The concepts of his Simulation study are interwoven with issues in Artificial neural network, Recurrent neural network and Traffic generation model.
Xiaobo Qu has included themes like Regression analysis and Risk assessment in his Transport engineering study.
His most cited work include:
- Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator (225 citations)
- Bus stop-skipping scheme with random travel time (182 citations)
- Undersampled MRI reconstruction with patch-based directional wavelets (166 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Transport engineering, Algorithm, Computer vision and Compressed sensing.
The study of Artificial intelligence is intertwined with the study of Pattern recognition in a number of ways.
His studies deal with areas such as Risk assessment and Operations research as well as Transport engineering.
He interconnects Hankel matrix and Sampling in the investigation of issues within Algorithm.
His is doing research in Image quality, Image processing and Image fusion, both of which are found in Computer vision.
His work often combines Iterative reconstruction and Data acquisition studies.
He most often published in these fields:
- Artificial intelligence (21.29%)
- Transport engineering (21.29%)
- Algorithm (13.31%)
What were the highlights of his more recent work (between 2019-2021)?
- Nuclear magnetic resonance spectroscopy (6.84%)
- Artificial intelligence (21.29%)
- Algorithm (13.31%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Nuclear magnetic resonance spectroscopy, Artificial intelligence, Algorithm, Deep learning and Energy consumption.
In most of his Artificial intelligence studies, his work intersects topics such as Computer vision.
His study in Image quality and Iterative reconstruction falls under the purview of Computer vision.
His work carried out in the field of Algorithm brings together such families of science as Hankel matrix, Sampling, Platoon and Exponential function.
His research in Sampling intersects with topics in Fourier transform and Signal processing.
His Deep learning research is multidisciplinary, incorporating perspectives in Artificial neural network and Data science.
Between 2019 and 2021, his most popular works were:
- Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach (60 citations)
- Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach (48 citations)
- Dynamic headway control for high‐frequency bus line based on speed guidance and intersection signal adjustment (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Algorithm
His primary scientific interests are in Artificial intelligence, Nuclear magnetic resonance spectroscopy, Algorithm, Deep learning and Sorting.
His work on Iterative reconstruction as part of general Artificial intelligence research is frequently linked to Estimation, bridging the gap between disciplines.
Xiaobo Qu performs integrative Iterative reconstruction and Acceleration research in his work.
Xiaobo Qu combines subjects such as Data science, Experimental data and Process with his study of Nuclear magnetic resonance spectroscopy.
The Algorithm study which covers Platoon that intersects with Traffic capacity, Domain, Integer programming and Enhanced Data Rates for GSM Evolution.
Deep learning and Artificial neural network are commonly linked in his work.
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