A5029336540- Mathematical and Theoretical Epidemiology and Ecology Models
- Complex Network Analysis Techniques
- Face and Expression Recognition
- Blind Source Separation Techniques
- Network Security and Intrusion Detection
- Advanced Neuroimaging Techniques and Applications
- Opinion Dynamics and Social Influence
- Sparse and Compressive Sensing Techniques
- Tensor decomposition and applications
- Assembly Line Balancing Optimization
- Error Correcting Code Techniques
- Medical Image Segmentation Techniques
- Ion channel regulation and function
- Digital Radiography and Breast Imaging
- Biometric Identification and Security
- Image Retrieval and Classification Techniques
- Advanced Wireless Communication Techniques
- Radiomics and Machine Learning in Medical Imaging
- Evolutionary Game Theory and Cooperation
- Advanced Memory and Neural Computing
- Neural dynamics and brain function
- Evolution and Genetic Dynamics
- EEG and Brain-Computer Interfaces
- Maritime Ports and Logistics
- Spam and Phishing Detection
Guangdong Medical College
1994-2023
Hong Kong Baptist University
2021
Guizhou University
2015-2017
A stochastic computer virus spread model is proposed and its dynamic behavior fully investigated. Specifically, we prove the existence uniqueness of positive solutions, stability virus‐free equilibrium viral by constructing Lyapunov functions applying Ito′s formula. Some numerical simulations are finally given to illustrate our main results.
Computer virus spread model concerning impulsive control strategy is proposed and analyzed. We prove that there exists a globally attractive infection‐free periodic solution when the vaccination rate larger than θ 0 . Moreover, we show system uniformly persistent if less 1 Some numerical simulations are finally given to illustrate main results.
This paper exactly formulates the k th‐order fixation probabilities on complete star digraphs (CSDs), which extend results from Broom and Rychtář (2008). By applying these probability formulae, some asymptotic properties CBDs are analyzed, certain CSDs determined to be amplifiers of selection for arbitrary relative fitness larger than 1, while all proved fixed slightly 1. A numerical method population structure (by solving a linear system) is developed calculate bipartite (CBDs), conjectures...
. The OSort algorithm, a pivotal unsupervised spike sorting method, has been implemented in dedicated hardware devices for real-time sorting. However, due to the inherent complexity of neural recording environments, still grapples with numerous transient cluster occurrences during practical process. This leads substantial memory usage, heavy computational load, and complex architectures, especially noisy recordings multi-channel systems.
Digital breast tomosynthesis (DBT) with improved lesion conspicuity and characterization has been adopted in screening practice. DBT-based diagnosis strongly depends on physicians' experience, so an automatic malignancy classification model using DBT could improve the consistency of among different physicians. Tensor-based approaches that use original imaging data as input have shown promising results for many tasks. However, are pseudo-3D volumetric slice spacing is much coarser than...
A worm spread model concerning impulsive control strategy is proposed and analyzed. We prove that there exists a globally attractive virus-free periodic solution when the vaccination rate larger than<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math>. Moreover, we show system uniformly persistent if less...
Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in support vector machine (SVM) optimization scheme for brain image analysis.However, classical SVM convert 3D discrete images naturally represented by higher-order tensors one-dimensional vectors order meet the input requirements.This traditional method destroys natural structure correlation original data, generates vectors.In this manuscript, is improved modified tensor (STM)...
Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in classical single kernel support vector machine optimization scheme, wherein the sequential minimal (SMO) training algorithm is adopted, for brain image analysis. However, satisfy conditions required case, it unreasonably assumed that regularization parameter equal one. In this letter, approach improved by combining SMO with multiple learning avoid assumption optimally...
Recently, a high dimensional classification framework has been proposed to introduce spatial structure information in classical single kernel support vector machine optimization scheme for brain image analysis. However, during the construction of this framework, huge adjacency matrix is adopted determine relation between each pair voxels and thus it leads very computational complexity calculation. The method improved manuscript by new tensorial wherein 3-order tensor preserve so that...
The classical linear discriminant analysis (LDA) was previously modified by orthogonal projection into null space LDA (N_LDA) and direct (D_LDA) for solving small sample size (SSS) problem. In this paper, the author proposes an extension of oblique projection, wherein N_LDA D_LDA are included as special cases, to reduce discriminative information loss resulted from single or D_LDA. effectiveness proposed algorithm is tested image forensics face recognition.
Tensorial two-dimensional principal component analysis (T2DPCA), a novel approach considering the third-order tensors as linear operators on space of oriented matrices, has been proposed recently and showed better performance than traditional (PCA) in image recognition. Kernel T2DPCA (KT2DPCA), nonlinear extension T2DPCA, is powerful technique for extracting structure from data. However, original form KT2DPCA requires storing manipulating kernel matrix tensor size which square number...