一类3-正则3-连通平面Hamilton图(英文)

设Γk是由带如下结构的3-正则3-连通平面图G所组成的图类:G中含一个圈C,使得G-E(C)产生k个不相交的树,并且每个树具有至少三条边.本文证明了Γ1中所有的图都是Hamilton图.

A NEW APPROACH FOR UNSUPERVISED RESTORING IMAGES BASED ON WAVELET-DOMAIN PROJECTION PURSUIT LEARNING NETWORK

The Wavelet-Domain Projection Pursuit Learning Network (WDPPLN) is proposedfor restoring degraded image. The new network combines the advantages of both projectionpursuit and wavelet shrinkage. Restoring image is very difficult when little is known about apriori knowledge for multisource degraded factors. WDPPLN successfully resolves this problemby separately processing wavelet coefficients and scale coefficients. Parameters in WDPPLN,which are used to simulate degraded factors, are estimated via WDPPLN training, using scalecoefficients. Also, WDPPLN uses soft-threshold of wavelet shrinkage technique to suppress noisein three high frequency subbands. The new method is compared with the traditional methodsand the Projection Pursuit Learning Network (PPLN) method. Experimental results demonstratethat it is an effective method for unsupervised restoring degraded image.

Window shrink contourlet coefficients for image denoising

An adaptive image denosing technique was proposed to achieve the tradeoff between details retain and noises removal. In order to achieve this objective, the contourlet transform was introduced and a new threshold method, namely CWinShrink, is presented. It shrinks the contourlet coefficients with adaptive shrinkage factors. The shrinkage factors were calculated with reference to the sum of squares of the contourlet coefficients within the neighborhood window. This approach achieves enhanced results for images those are corrupted with additive Gaussian noise. In numerical comparisons with various methods, for a set of noisy images （the PSNR range fi＇om 10.86dB to 26.91dB） , the presented method outperforms VisuShrink and Wiener filter in terms of the PSNR. Experiments also show that this method not only keeps the details of image but also yields denoised images with better visual quality.

Classification Method of Graphlike Manifold

In this paper,we give a kind of method which can easily be operated to classify the graphlike manifold with contraction.

Unretractivity and End-Regularity of a Graph

In this paper, a relationship among unretractivity, E-H-unretractivity andend-regularity of a graph is described.

E-H-Unretractivity of Bipartite Graphs

By End（G） and hEnd（G） we denote the set of endomorphisms and half-strong endomorphisms of a graph G respectively. A graph G is said to be E-H-unretractive if End（G） = hEnd（G）. A general characterization of an E-H-unretractive graph seems to be difficult. In this paper, bipartite graphs with E-H-unretractivity are characterized explicitly.

Effects of acupuncture stimulation of the sacral region on EEG and urinary bladder activity in rats

Objective To observe the effect of acupuncture stimulation of the sacral segment on the excitability of the cerebral cortex and the activity of the urinary bladder and the involvement of the cholinergic neurons in the laterodorsal tegmental （LDT） nucleus of the brainstem in acupuncture-induced electroencephalogram （EEG） changes. Methods A total of 109 SD rats were used in the present study. Under anesthesia （urethane）, a pair of stainless steel electrodes was separately implanted into the frontal and parietal bony sutures to record EEG. Glass microelectrodes were used to record extracellular discharges of single neuron of the LDT nucleus in the brainstem. Urinary bladder pressure was recorded through a catheter inserted in the bladder and the contraction was induced by infusion of normal saline. A filiform acupuncture needle was inserted into the sacral segment Ecorresponding to Zhongliao （中髎BL 33）] and rotated manually for 1 min. Results In 27 rats whose bladder was full of normal saline, acupuncture stimulation of the sacral region suppressed the contraction activity of the bladder, the fast EEG with lower amplitude and higher frequency tuned into slow EEG with higher amplitude and lower frequency in 6 cases （22.2%）. The inhibitory effect occurred from 45 s to 12 min after acupuncture manipulation. In 82 rats whose bladder was empty, acupuncture stimulation caused the fast EEG to turn into slow EEG in 71 cases （86.6%）. Simultaneously, LDT cholinergic neurons reduced their firing rates from （2.9±1.5） Hz to （1.2±0.6） Hz （n = 12, P〈0.05）, and the reduction of LDT neuronal discharge was earlier in time than the change of EEG. Conclusion Acupuncture stimulation of the sacral region can lower the excitability of the cerebral cortex and suppress bladder activity, which is closely associated with its resultant inhibitory effect on the electrical activity of LDT cholinergic neurons.

Research on Split Augmented Largrangian Shrinkage Algorithm in Magnetic Resonance Imaging Based on Compressed Sensing

This paper aims to meet the requirements of reducing the scanning time of magnetic resonance imaging （MRI）, accelerating MRI and reconstructing a high quality image from less acquisition data as much as possible. MRI method based on compressed sensing （CS） with multiple regularizations （two regularizations including total variation （TV） norm and L1 norm or three regularizations consisting of total variation, L1 norm and wavelet tree structure） is proposed in this paper, which is implemented by applying split augmented lagrangian shrinkage algorithm （SALSA）. To solve magnetic resonance image reconstruction problems with linear combinations of total variation and L1 norm, we utilized composite spht denoising （CSD） to split the original complex problem into TV norm and L1 norm regularization subproblems which were simple and easy to be solved respectively in this paper. The reconstructed image was obtained from the weighted average of solutions from two subprohlems in an iterative framework. Because each of the splitted subproblems can be regarded as MRI model based on CS with single regularization, and for solving the kind of model, split augmented lagrange algorithm has advantage over existing fast algorithm such as fast iterative shrinkage thresholding（FIST） and two step iterative shrinkage thresholding （TWIST） in convergence speed. Therefore, we proposed to adopt SALSA to solve the subproblems. Moreover, in order to solve magnetic resonance image reconstruction problems with linear combinations of total variation, L1 norm and wavelet tree structure, we can split the original problem into three subproblems in the same manner, which can be processed by existing iteration scheme. A great deal of experimental results show that the proposed methods can effectively reconstruct the original image. Compared with existing algorithms such as TVCMRI, RecPF, CSA, FCSA and WaTMRI, the proposed methods have greatly improved the quality of the reconstructed images and have better visual effect.