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Biomedical subjects

J B Gao

Publications and source records attributed to J B Gao.

9 recordsLinked to original sources
PubMed2002

Noise-induced Hopf-bifurcation-type sequence and transition to chaos in the lorenz equations.

We study the effects of noise on the Lorenz equations in the parameter regime admitting two stable fixed point solutions and a strange attractor. We show that noise annihilates the two stable fixed point attractors and evicts a Hopf-bifurcation-like sequence and transition to chaos. The noise-induced oscillatory motions have very well defined period and amplitude, and this phenomenon is similar to stochastic resonance, but without a weak periodic forcing. When the noise level exceeds certain threshold value but is not too strong, the noise-induced signals enable an objective computation of the largest positive Lyapunov exponent, which characterize the signals to be truly chaotic.

Journal Article↗
PubMed2002

Pathological tremors as diffusional processes.

Two types of pathological tremors, essential and Parkinsonian, are studied using dynamical systems theory. It is shown that pathological tremors can be characterized as diffusional processes. The time-scale range for the diffusional scaling law to be valid starts from about one to several tens of the mean oscillation period. This time-scale range contrasts sharply with the predictable time scale for deterministic chaos, which is usually only a small fraction of the mean oscillation period. The diffusions in pathological tremors are usually anomalous. A number of quantities are designed to characterize the diffusions in the tremor. Their relevance to potential clinical applications is discussed. It is argued that in order to discriminate between Parkinsonian and essential tremors, quantities not of purely dynamical origin may be more useful, since purely dynamical quantities emphasize more the dynamical similarities between the two types of tremors.

Essential Tremor↗
PubMed2001

Detecting nonstationarity and state transitions in a time series.

One cause of complexity in a time series may be due to nonstationarity and transience. In this paper, we analyze the nonstationarity and transience in a number of dynamical systems. We find that the nonstationarity in the metastable chaotic Lorenz system is due to nonrecurrence. The latter determines a lack of fractal structure in the signal. In 1/f(alpha) noise, we find that the associated correlation dimension are local graph dimensions calculated from sojourn points. We also design a transient Lorenz system with a slowly oscillating controlling parameter, and a transient Rossler system with a slowly linearly increasing parameter, with parameter ranges covering a sequence of chaotic dynamics with increased phase incoherence. State transitions, from periodic to chaotic, and vice versa, are identified, together with different facets of nonstationarity in each phase.

Journal Article↗
PubMed2001

On a class of support vector kernels based on frames in function Hilbert spaces.

There has been an increasing interest in kernel-based techniques, such as support vector techniques, regularization networks, and gaussian processes. There are inner relationships among those techniques, with the kernel function playing a central role. This article discusses a new class of kernel functions derived from the so-called frames in a function Hilbert space.

Algorithms↗
PubMed2001

[A statistical wavelength selection method of infrared spectra used in exhaust gas detection].

In order to extract the quantitative features of the rare pollution components from noisy atmospheric infrared spectra and thus create calibration models, a wavelength selection method based on statistic theory is proposed in this paper. In this method, an objective function is defined based on the estimation of spectral noise level at every wavelength position. Because the size of the wavelength subset is also included in the function, the model size will not become too big during the minimization of the error of the calibration model. To test the performance of this method, the wavelength subsets of measured spectra with background noises were selected and the calibration models were then created using neural network technique for three pollution gases, respectively. The test showed that the sizes of the selected wavelength subsets accorded with the calculated results. The subset sizes were less than 2% of the total wavelength points. Meantime, the spectral noises were also restrained markedly in the calibration model because of the wavelength selection. The experimental results proved the validity of the wavelength selection method.

Air Pollutants↗
PubMed2001

[Maximal entropy principle wavelet denoising].

In the filed of wavelet denoising, an essential problem is how to determine the cutting threshold of wavelet coefficients that divides the coefficients corresponding to signal and noise respectively. The wavelet denoising method discussed here determines this threshold by using the maximal entropy principle (MEP) of information theory. From the basic principle of probability theory, it can be deduced that the detailed wavelet coefficients sequence of an arbitrary distributed random noise sequence satisfies a normal distribution. Based on this conclusion, an optimal threshold is determined using MEP. Such that the coefficients whose absolute values are less than the threshold satisfies a normal probabilistic distribution. This threshold is an optimal value that distinguishes the wavelet coefficients of signal and noise in view of statistics. The simulation analysis using spectral data and the comparison with other methods showed that this method provides a best improvement of signal-to-noise ratio, and its performance is least sensitive to the change of signal-to-noise ratio.

Algorithms↗
PubMed2001

[Input layer self-construction neural network and its use in multivariant calibration of infrared spectra].

In order to solve the problems of feature extraction and calibration modelling in the area of quantitatively infrared spectra analysis, an input layer self-constructive neural network (ILSC-NN) is proposed. Before the NN training process, the training data is firstly analyzed and some prior knowledge about the problem is obtained. During the training process, the number of the input neurons is determined adaptively based on the prior knowledge. Meantime, the network parameters are also determined. This algorithm of the NN model helps to increase the efficiency of calibration modelling. The test experiment of quantitative analysis using simulated spectral data showed that this modelling method could not only achieve efficient wavelength selection, but also remarkably reduce the random and non-linear noises.

Algorithms↗
PubMed1985

Ultrastructure and chemical composition of calcite urinary calculi in Chinese swamp buffalo.

Four urinary calculi, derived from Chinese swamp buffalo, were studied by using qualitative chemical analysis, X-ray diffraction, scanning electron microscopy and qualitative energy dispersive (electron probe) microanalysis. Qualitative chemical analysis showed that the predominant ions were calcium and carbonate with small amounts of magnesium and ammonium. X-ray diffraction confirmed that the calculi were primarily composed of calcium carbonate (calcite). On ultrastructural examination, three apparently distinct structural regions were identified in the calculi: outer large laminations; cavities containing variable numbers of small spheres and rods; and large spheres. There did not appear to be material that acted as a nidus and all regions, on qualitative electron probe analysis, contained primarily calcium with trace amounts of magnesium, phosphorus, potassium and chloride. It was concluded that calcite calculi in Chinese swamp buffalo are probably formed through a process of asynchronous layering and that nidus formation may not be necessary. Moreover, the ultrastructure of the calcite calculi is similar to that reported for siliceous calculi in ruminants and this suggests that similar factors may be involved in their formation.

Animals↗