What is he best known for?
The fields of study he is best known for:
- Organic chemistry
- Artificial intelligence
- Statistics
Wei Su spends much of his time researching Artificial intelligence, Algorithm, Information hiding, Digital watermarking and Pattern recognition.
His work deals with themes such as Detection theory, Machine learning and Computer vision, which intersect with Artificial intelligence.
As a member of one scientific family, Wei Su mostly works in the field of Machine learning, focusing on Speech recognition and, on occasion, Modulation, Demodulation, Identification and Data mining.
His Algorithm study combines topics from a wide range of disciplines, such as Classifier, Higher-order statistics, Electronic engineering and Quadrature amplitude modulation.
His Information hiding research incorporates elements of Pixel, Distortion, Markov process and Support vector machine.
He has researched Distortion in several fields, including Image resolution, Image processing, Image, Grayscale and Range.
His most cited work include:
- Reversible data hiding (1888 citations)
- Survey of automatic modulation classification techniques: classical approaches and new trends (865 citations)
- Distortionless data hiding based on integer wavelet transform (244 citations)
What are the main themes of his work throughout his whole career to date?
Wei Su mainly investigates Algorithm, Artificial intelligence, Modulation, Adsorption and Pattern recognition.
Wei Su frequently studies issues relating to Computer vision and Artificial intelligence.
His biological study spans a wide range of topics, including Signal-to-noise ratio, Detection theory, Electronic engineering and Communication channel.
His Adsorption study integrates concerns from other disciplines, such as Inorganic chemistry, Chromatography, Specific surface area, Carbon and Chemical engineering.
The concepts of his Pattern recognition study are interwoven with issues in Histogram, Analog transmission and Quadrature amplitude modulation.
His Information hiding research includes themes of Distortion and Digital watermarking.
He most often published in these fields:
- Algorithm (28.38%)
- Artificial intelligence (23.65%)
- Modulation (22.97%)
What were the highlights of his more recent work (between 2014-2021)?
- Algorithm (28.38%)
- Catalysis (6.76%)
- Chemical engineering (14.86%)
In recent papers he was focusing on the following fields of study:
Wei Su focuses on Algorithm, Catalysis, Chemical engineering, Selective catalytic reduction and Wireless sensor network.
His Algorithm study combines topics in areas such as Gibbs sampling, MIMO, Bayesian network and Bayesian inference.
In the subject of general Catalysis, his work in Mesoporous material and Hydrogen production is often linked to Electrolysis and Polyol, thereby combining diverse domains of study.
His Chemical engineering research includes elements of Activated carbon and Hydrogen.
His studies deal with areas such as Modulation, Key distribution in wireless sensor networks, Brooks–Iyengar algorithm, Artificial intelligence and Pattern recognition as well as Wireless sensor network.
His Gaussian noise study in the realm of Artificial intelligence interacts with subjects such as Modulation.
Between 2014 and 2021, his most popular works were:
- Experimental and Theoretical Investigation of Mesoporous MnO2 Nanosheets with Oxygen Vacancies for High-Efficiency Catalytic DeNOx (27 citations)
- Asynchronous Linear Modulation Classification With Multiple Sensors via Generalized EM Algorithm (25 citations)
- Adsorption Equilibrium of N2, CH4, and CO2 on MIL-101 (22 citations)
In his most recent research, the most cited papers focused on:
- Organic chemistry
- Artificial intelligence
- Statistics
His main research concerns Chemical engineering, Asynchronous communication, Expectation–maximization algorithm, Electrocatalyst and Microbial fuel cell.
Wei Su interconnects Maximum likelihood, Algorithm, Signal-to-noise ratio and Modulation in the investigation of issues within Asynchronous communication.
His research in Modulation intersects with topics in Single input multiple output and Electronic engineering.
The Expectation–maximization algorithm study combines topics in areas such as Maximum likelihood sequence estimation, Artificial intelligence and Pattern recognition.
The study incorporates disciplines such as Distributed algorithm, Estimation theory and Overhead in addition to Artificial intelligence.
Wei Su integrates Pattern recognition and Modulation in his research.
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