Chunzheng Cao
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View article: Generalized Metaplectic Convolution-Based Cohen's Class Time-Frequency Distribution: Theory and Application
Generalized Metaplectic Convolution-Based Cohen's Class Time-Frequency Distribution: Theory and Application Open
The convolution type of the Cohen's class time-frequency distribution (CCTFD) is a useful and effective time-frequency analysis tool for additive noises jamming signals. However, it can't meet the requirement of high-performance denoising …
View article: Adaptive Cohen's Class Time-Frequency Distribution
Adaptive Cohen's Class Time-Frequency Distribution Open
Inspired by the use of adaptive kernel-based Cohen's class time-frequency distributions (CCTFDs) for cross-term suppression, this paper aims to explore novel adaptive kernel functions for denoising. We integrate Wiener filter principle and…
View article: Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology
Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology Open
The conventional Cohen's distribution can't meet the requirement of additive noises jamming signals high-performance denoising under the condition of low signal-to-noise ratio, it is necessary to integrate the metaplectic transform for non…
View article: Multi-dimensional Graph Linear Canonical Transform
Multi-dimensional Graph Linear Canonical Transform Open
Many multi-dimensional (M-D) graph signals appear in the real world, such as digital images, sensor network measurements and temperature records from weather observation stations. It is a key challenge to design a transform method for proc…
View article: Graph Linear Canonical Transform Based on CM-CC-CM Decomposition
Graph Linear Canonical Transform Based on CM-CC-CM Decomposition Open
The graph linear canonical transform (GLCT) is presented as an extension of the graph Fourier transform (GFT) and the graph fractional Fourier transform (GFrFT), offering more flexibility as an effective tool for graph signal processing. I…
View article: Curve Classification Based on Mean-Variance Feature Weighting and Its Application
Curve Classification Based on Mean-Variance Feature Weighting and Its Application Open
The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classifi…
View article: Adaptive Cohen's Class Time-Frequency Distribution
Adaptive Cohen's Class Time-Frequency Distribution Open
View article: A Skew Normal Mixed Models with Noise Estimation and Anisotropic Spatial Information Constraints
A Skew Normal Mixed Models with Noise Estimation and Anisotropic Spatial Information Constraints Open
View article: Dynamic Analysis of a Stochastic Rumor Propagation Model with Regime Switching
Dynamic Analysis of a Stochastic Rumor Propagation Model with Regime Switching Open
We study the rumor propagation model with regime switching considering both colored and white noises. Firstly, by constructing suitable Lyapunov functions, the sufficient conditions for ergodic stationary distribution and extinction are ob…
View article: Robust Task Learning Based on Nonlinear Regression With Mixtures of Student-<i>t</i> Distributions
Robust Task Learning Based on Nonlinear Regression With Mixtures of Student-<i>t</i> Distributions Open
We propose a robust task learning method based on nonlinear regression model with mixtures of t-distributions. The model can adaptively reduce the effects of complex noises and accurately learn the nonlinear structure of targets. By introd…
View article: A Robust Spatial Information-Theoretic GMM Algorithm for Bias Field Estimation and Brain MRI Segmentation
A Robust Spatial Information-Theoretic GMM Algorithm for Bias Field Estimation and Brain MRI Segmentation Open
Due to their simplicity and flexibility, the unsupervised statistical models such as Gaussian mixture model (GMM) are powerful tools to address the brain magnetic resonance (MR) images segmentation problems. However, the GMM is based only …
View article: Regression Analysis for Multivariate Dependent Count Data Using Convolved Gaussian Processes
Regression Analysis for Multivariate Dependent Count Data Using Convolved Gaussian Processes Open
Research on Poisson regression analysis for dependent data has been developed rapidly in the last decade. One of difficult problems in a multivariate case is how to construct a cross-correlation structure and at the meantime make sure that…
View article: Robust functional regression model for marginal mean and subject-specific inferences
Robust functional regression model for marginal mean and subject-specific inferences Open
We introduce flexible robust functional regression models, using various heavy-tailed processes, including a Student $t$-process. We propose efficient algorithms in estimating parameters for the marginal mean inferences and in predicting c…