What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Control theory
Xiaofeng Liao focuses on Control theory, Algorithm, Encryption, Artificial neural network and Exponential stability.
Xiaofeng Liao has included themes like Bounded function and Synchronization in his Control theory study.
The concepts of his Algorithm study are interwoven with issues in Chaotic and Theoretical computer science.
His Encryption research includes elements of Image, Distributed computing and Cryptography.
The Artificial neural network study combines topics in areas such as Equilibrium point, Time delays, Exponential growth and Interval.
His Exponential stability research is multidisciplinary, relying on both Stability theory, Lyapunov function, Applied mathematics and Constant.
His most cited work include:
- A new chaos-based fast image encryption algorithm (429 citations)
- Delay-dependent exponential stability analysis of delayed neural networks: an LMI approach (394 citations)
- Event-Triggering Sampling Based Leader-Following Consensus in Second-Order Multi-Agent Systems (337 citations)
What are the main themes of his work throughout his whole career to date?
Xiaofeng Liao mainly focuses on Control theory, Artificial neural network, Algorithm, Exponential stability and Chaotic.
In his study, Chaotic systems is strongly linked to Synchronization of chaos, which falls under the umbrella field of Control theory.
His studies in Artificial neural network integrate themes in fields like Monotonic function, Interval, Applied mathematics and Constant.
His research in Algorithm intersects with topics in Image, Artificial intelligence, Theoretical computer science and Encryption.
In his research, Bogdanov–Takens bifurcation is intimately related to Mathematical analysis, which falls under the overarching field of Exponential stability.
Xiaofeng Liao has researched Chaotic in several fields, including CHAOS, Lyapunov stability, Attractor, Synchronization and Cryptosystem.
He most often published in these fields:
- Control theory (40.98%)
- Artificial neural network (23.57%)
- Algorithm (18.05%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (18.05%)
- Control theory (40.98%)
- Encryption (11.04%)
In recent papers he was focusing on the following fields of study:
Xiaofeng Liao mostly deals with Algorithm, Control theory, Encryption, Chaotic and Mathematical optimization.
His Algorithm study deals with Pixel intersecting with Information hiding.
The study incorporates disciplines such as Artificial neural network, Multi-agent system, Consensus and Function in addition to Control theory.
His Encryption study integrates concerns from other disciplines, such as Image, Theoretical computer science and Compressed sensing.
His Chaotic research is multidisciplinary, incorporating perspectives in Digital image, Partial differential equation, Synchronization, Ordinary differential equation and Applied mathematics.
His Mathematical optimization research integrates issues from Convergence and Upper and lower bounds.
Between 2016 and 2021, his most popular works were:
- Image encryption using 2D Hénon-Sine map and DNA approach (103 citations)
- Color image encryption based on chaotic systems and elliptic curve ElGamal scheme (76 citations)
- Cryptanalysis and enhancements of image encryption based on three-dimensional bit matrix permutation (62 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Computer network
Xiaofeng Liao spends much of his time researching Algorithm, Encryption, Control theory, Multi-agent system and Image.
His Algorithm study combines topics in areas such as Coupled map lattice, Logistic map and Transmission.
His studies deal with areas such as Theoretical computer science and Sine as well as Encryption.
His Control theory study combines topics from a wide range of disciplines, such as Function and Computer simulation.
His Multi-agent system research includes themes of Event, Discrete time and continuous time, Correctness and Protocol.
His Nonlinear system research is multidisciplinary, relying on both Reaction–diffusion system, Dirichlet boundary condition, Artificial neural network, Topology and Complex network.
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