Zhitong Cao

Zhejiang University, Hangzhou, Zhejiang Sheng, China

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Publications (11)9.95 Total impact

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    ABSTRACT: The upper and lower bounds of the linear variance decay (LVD) dimension density are analytically deduced using multivariate series with uncorrelated and perfectly correlated component series. Then, the normalized LVD dimension density (δnormLVD) is introduced. In order to measure the complexity of a scalar series with δnormLVD, a pseudo-multivariate series was constructed from the scalar time series using time-delay embedding. Thus, δnormLVD is used to characterize the complexity of the pseudo-multivariate series. The results from the model systems and fMRI data of anxiety subjects reveal that this method can be used to analyze short and noisy time series.Highlights► Deducing the upper and lower bounds of δLVD dimension density analytically. ► Proposing the normalized LVD dimension density (δnormLVD). ► Measuring the complexity of a scalar time series by δnormLVD. ► Voxel-base analysis of fMRI data set of anxiety disease by δnormLVD.
    Physics Letters A 01/2011; 375(17):1789-1795. · 1.77 Impact Factor
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    ABSTRACT: Estimating the true dimensionality of the data to determine what is essential in the data is an important but a difficult problem in fMRI dataset. In this paper, cubic spline interpolation is introduced to detect the number of essential components in fMRI dataset. By constructing proper interpolation variable, more reasonable estimation of the coefficient of an autoregressive noise model of order 1 can be made. Simulation data and real fMRI dataset of resting-state in human brains are used to compare the performance of the new method incorporating an autoregressive noise model of order 1 with cubic spline interpolation (AR1CSI) with that of the method based only on an autoregressive noise model of order 1 (AR1). The results show the AR1CSI method leads to more accurate estimate of the model order at many circumstances, as illustrated in simulated datasets and real fMRI datasets of resting-state human brain.
    Neurocomputing. 01/2009;
  • Xiaoping Xie, Zhitong Cao, Xuchu Weng
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    ABSTRACT: In this work, the spatiotemporal nonlinearity in resting-state fMRI data of the human brain was detected by nonlinear dynamics methods. Nine human subjects during resting state were imaged using single-shot gradient echo planar imaging on a 1.5T scanner. Eigenvalue spectra for the covariance matrix, correlation dimensions and Spatiotemporal Lyapunov Exponents were calculated to detect the spatiotemporal nonlinearity in resting-state fMRI data. By simulating, adjusting, and comparing the eigenvalue spectra of pure correlated noise with the corresponding real fMRI data, the intrinsic dimensionality was estimated. The intrinsic dimensionality was used to extract the first few principal components from the real fMRI data using Principal Component Analysis, which will preserve the correct phase dynamics, while reducing both computational load and noise level of the data. Then the phase-space was reconstructed using the time-delay embedding method for their principal components and the correlation dimension was estimated by the Grassberger-Procaccia algorithm of multiple variable series. The Spatiotemporal Lyapunov Exponents were calculated by using the method based on coupled map lattices. Through nonlinearity testing, there are significant differences of correlation dimensions and Spatiotemporal Lyapunov Exponents between fMRI data and their surrogate data. The fractal dimension and the positive Spatiotemporal Lyapunov Exponents characterize the spatiotemporal nonlinear dynamics property of resting-state fMRI data. Therefore, the results suggest that fluctuations presented in resting state may be an inherent model of basal neural activation of human brain, cannot be fully attributed to noise.
    NeuroImage 06/2008; 40(4):1672-85. · 6.25 Impact Factor
  • Xiaoping Xie, Zhitong Cao, Xuchu Weng
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    ABSTRACT: In this work, a nonlinear dynamics method, coupled map lattices, was applied to functional magnetic resonance imaging (fMRI) datasets to examine the spatiotemporal properties of resting state blood oxygen level-dependent (BOLD) fluctuations. Spatiotemporal Lyapunov Exponent (SPLE) was calculated to study the deterministic nonlinearity in resting state human brain of nine subjects based on fMRI datasets. The results show that there is nonlinearity and determinism in resting state human brain. Furthermore, the results demonstrate that there is a spatiotemporal chaos phenomenon in resting state brain, and suggest that fluctuations of fMRI data in resting state brain cannot be fully attributed to nuclear magnetic resonance noise. At the same time, the spatiotemporal chaos phenomenon suggests that the correlation between voxels varies with time and there is a dynamic functional connection or network in resting state human brain.
    Applied Mathematics and Computation. 01/2008;
  • Bo Shao, Zhitong Cao, Yuetong Xu
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    ABSTRACT: The cogging force has great impact to the efficiency of permanent magnetic liner synchronous motor (PMLSM) especially at high precision and low speed. This paper presents a cogging force estimator based on radical basis functional network (RBFN) by accelerated fuzzy c-means algorithm. Comparing to the estimator based on back propagation neural network (BPNN) with momentum method, the novel estimator increases the clustering of NN by boosting learning rates. Simulation results show the fractional slot with q<1 structure effectively depresses cogging force in PMLSM. Experiments prove that the estimator has high accuracy and efficiency. The novel estimator achieves demand of agility design and gives reference for structural parameters selection in PMLSM.
    Neural Information Processing, 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part I; 01/2006
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    ABSTRACT: In this paper, we proposed an improved delay feedback control method (IDFC) for a chaotic neural network. In the method, a delay feedback control signal is added into the term of the refractoriness of the chaotic neuron to resist the chaos in the chaotic neural network. The computer experiments show that the output sequence of the controlled chaotic neural network become periodic. The controlling chaos in a chaotic neural network is therefore implemented. When control parameters K and τ are taken as 1.3 and 1 respectively, the outputs of the network is periodic-16.
    Neural Networks and Brain, 2005. ICNN&B '05. International Conference on; 11/2005
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    ABSTRACT: In the paper, the modified fuzzy c-means (MFc) is firstly used to treat the ill-balanced fMRI dataset to improve the efficiency, remove the redundance and reduce the population of analyzed voxels. Then the iteration self-organization data analysis techniques algorithm (ISODATA) method, as the development of data-driving methods, is utilized to find out the activated region in the brain. Therefore a multi-step strategy, including MFc and ISODATA, has been proposed to analyze a hybrid dataset and a real experimental fMRI dataset. On the whole, clustering analysis is calculated by multi-step strategy for local activity of fMRI dataset under auditory stimulation. Results show the multi-step strategy has its special characteristics in flexibility and efficiency compared with other data-driving dynamic method and SPM
    Neural Networks and Brain, 2005. ICNN&B '05. International Conference on; 11/2005
  • Jiebin Gu, Zhitong Cao, Xi Zheng, Cai Aihua
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    ABSTRACT: In fMRI dataset, the population of actived voxels is always much less than the total population of the voxels, and that produced an ill-balanced dataset. Some methods, such as limiting the analysis to the gray matter voxels where the BOLD signal is expected and removing the voxels that is absolutely non-actived based on statistical criteria, have been used to treat the ill-balanced dataset. In this article, a new method, Modified Fuzzy c-means(MFc), has been proposed to treat the ill-balanced dataset of fMRI. The main difference from other statistical methods is that it is datadriven. iven. The MFc method is used to classify the voxels into two clusters with nearly the same population and all actived voxels are contained in one cluster. Thus we got nearly half voxels to analysis and the ill-balanced dataset can be treated. The efficiency of clustering analysis is also boosted.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2005; 2:1411-4.
  • Zhitong Cao, Jiongjiong Cai
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    ABSTRACT: Recently, rare earth giant magnetostrictive materials (GMM) have drawn a lot of attention. Their applications are developing quickly owing to their unique features, especially at room temperature, such as giant strain coefficient, efficient electric(magnetic)-mechanical transformation ability, and so on. In this paper, a design model for magnetic and mechanical energy coupling and transformation, so-called coupled field iteration, is firstly described through the finite element method (FEM), including the calculation of magnetostrictive force, which is analyzed through the local application of the virtual work principle. Then a prototype of single GMM actuator is designed and comparison between the calculated deformations and experiment measurements is exhibited. Based on these results, a new motor is designed and fabricated by combining two single actuators with a metallic annulus. The metallic annulus is vibrated in elliptical motion mode, which is driven by the two actuators with specific input current pattern. Finally the elliptical motion is validated by the experiments.
    Sensors and Actuators A-physical - SENSOR ACTUATOR A-PHYS. 01/2005; 118(2):332-337.
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    ABSTRACT: A chaotic neural network consisting of chaotic neurons exhibits such rich dynamical behaviors as nonperiodic associative memory. But it is difficult to distinguish the stored patterns from others, since the chaotic neural network shows chaotic wandering around the stored patterns. In order to apply the nonperiodic associative memory to information search or pattern identification, it is necessary to control chaotic dynamics. In this paper, we propose a delay feedback control method for the chaotic neural network. Computer simulation shows that, by means of the control method, the chaotic dynamics in the chaotic neural network are changed. The output sequence of the controlled network wanders around one stored pattern and its reverse pattern.
    01/2004;
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    ABSTRACT: The chaotic neural network constructed with chaotic neuron shows the associative memory function, but its memory searching process cannot be stabilized in a stored state because of the chaotic motion of the network. In this paper, a pinning control method focused on the chaotic neural network is proposed. The computer simulation proves that the chaos in the chaotic neural network can be controlled with this method and the states of the network can converge in one of its stored patterns if the control strength and the pinning density are chosen suitable. It is found that in general the threshold of the control strength of a controlled network is smaller at higher pinned density and the chaos of the chaotic neural network can be controlled more easily if the pinning control is added to the variant neurons between the initial pattern and the target pattern.
    Neural Networks 11/2003; 16(8):1195-200. · 1.93 Impact Factor