Image decomposition and staircase effect reduction based on total generalized variation

Total variation （TV） is widely applied in image process-ing. The assumption of TV is that an image consists of piecewise constants, however, it suffers from the so-cal ed staircase effect. In order to reduce the staircase effect and preserve the edges when textures of image are extracted, a new image decomposition model is proposed in this paper. The proposed model is based on the to-tal generalized variation method which involves and balances the higher order of the structure. We also derive a numerical algorithm based on a primal-dual formulation that can be effectively imple-mented. Numerical experiments show that the proposed method can achieve a better trade-off between noise removal and texture extraction, while avoiding the staircase effect efficiently.     

Simulated annealing spectral clustering algorithm for image segmentation

The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance measure cannot ful y reveal the complex distribution data, and the result of spectral clustering is very sensitive to the scaling parameter. To solve these problems, a new manifold distance measure and a novel simulated anneal-ing spectral clustering （SASC） algorithm based on the manifold distance measure are proposed. The simulated annealing based on genetic algorithm （SAGA）, characterized by its rapid convergence to the global optimum, is used to cluster the sample points in the spectral mapping space. The proposed algorithm can not only reflect local and global consistency better, but also reduce the sensitivity of spectral clustering to the kernel parameter, which improves the algorithm’s clustering performance. To efficiently apply the algorithm to image segmentation, the Nystrom method is used to reduce the computation complexity. Experimental results show that compared with traditional clustering algorithms and those popular spectral clustering algorithms, the proposed algorithm can achieve better clustering performances on several synthetic datasets, texture images and real images.     

Global minimization of adaptive local image fitting energy for image segmentation

The active contour model based on local image fitting （LIF） energy is an effective method to deal with intensity inhomo- geneities, but it always conflicts with the local minimum problem because LIF has a nonconvex energy function form. At the same time, the parameters of LIF are hard to be chosen for better per- formance. A global minimization of the adaptive LIF energy model is proposed. The regularized length term which constrains the zero level set is introduced to improve the accuracy of the bound- aries, and a global minimization of the active contour model is presented, in addition, based on the statistical information of the intensity histogram, the standard deviation σ with respect to the truncated Gaussian window is automatically computed according to images. Consequently, the proposed method improves the performance and adaptivity to deal with the intensity inhomo- geneities. Experimental results for synthetic and real images show desirable performance and efficiency of the proposed method.     

基于重构暗通道的二次正弦衰减的去雾算法

针对暗通道算法在天空区域失效以及最小值滤波器产生伪影现象等缺陷, 提出一种基于重构暗通道的二次正弦衰减的去雾算法。首先, 利用图像分割对原始图像进行暗通道重构得到重构暗通道。然后，设计雾密度权重函数对场景中的雾浓度做近似估计得到权重系数, 并结合权重系数对重构暗通道进行二次正弦衰减得到初始透射率。为了得到更加精细的透射率, 结合直方图均衡化和快速导向滤波来优化透射率。最后, 结合大气散射模型复原无雾图像。实验结果表明, 所提算法能有效处理天空区域, 抑制光晕伪影, 且复原图像细节明显, 亮度饱和度适宜。     

Image segmentation algorithm based on high-dimension fuzzy character and restrained clustering network

An image segmentation algorithm of the restrained fuzzy Kohonen clustering network （RFKCN） based on high- dimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification of pixels in which two redundant images are built by fuzzy mean value and fuzzy median value. The second step is to construct a three-dimensional （3-D） feature vector of redundant images and their original images and cluster the feature vector through RFKCN, to realize image seg- mentation. The proposed algorithm fully takes into account not only gray distribution information of pixels, but also relevant information and fuzzy information among neighboring pixels in constructing 3- D character space. Based on the combination of competitiveness, redundancy and complementary of the information, the proposed algorithm improves the accuracy of clustering. Theoretical anal- yses and experimental results demonstrate that the proposed algorithm has a good segmentation performance.     

Best-retransmission count selection for environment optimization over wireless heterogeneous networks

This paper proposes an environment-aware best- retransmission count selected optimization control scheme over IEEE 802.11 multi-hop wireless networks. The proposed scheme predicts the wireless resources by using statistical channel state and provides maximum retransmission count optimization based on wireless channel environment state to improve the packet delivery success ratio. The media access control （MAC） layer selects the best-retransmission count by perceiving the types of packet loss in wireless link and using the wireless channel charac- teristics and environment information, and adjusts the packet for- warding adaptively aiming at improving the packet retransmission probability. Simulation results show that the best-retransmission count selected scheme achieves a higher packet successful delivery percentage and a lower packet collision probability than the corresponding traditional MAC transmission control protocols.     

Self-adjusting decision feedback equalizer for variational underwater acoustic channel environments

Aimed at the abominable influences to blind equaliza-tion algorithms caused by complex time-space variability existing in underwater acoustic channels, a new self-adjusting decision feedback equalization （DFE） algorithm adapting to different under-water acoustic channel environments is proposed by changing its central tap position. Besides, this new algorithm behaves faster convergence speed based on the analysis of equalizers’ working rules, which is more suitable to implement communications in dif-ferent unknown channels. Corresponding results and conclusions are validated by simulations and spot experiments.     

Blind channel estimation for multiple antenna OFDM system subject to unknown carrier frequency offset

The problem of channel estimation for multiple an- tenna orthogonal frequency division multiplexing （OFDM） systems subject to unknown carrier frequency offset （CFO） is addressed. Multiple signal classification （MUSIC）-Iike algorithm, which generally has been used for direction estimation or frequency estimation, is used for channel estimation in multiple antenna OFDM systems. A reduced dimensional （RD）-MUSIC based algorithm for channel estimation is proposed in multiple antenna OFDM systems with unknown CFO. The Cramer-Rao bound （CRB） of channel estimation in multiple antenna OFDM systems with unknown CFO is derived. The proposed algorithm has a superior performance of channel estimation compared with the Capon method and the least squares method.     

FAMILIES OF p-ARY SEQUENCES WITH LOW CORRELATION AND LARGE LINEAR COMPLEXITY

This paper generalizes a method of generating shift sequences in the interleaved construc- tion proposed by Gong. With the new shift sequences, some new families of p-ary sequences with desired properties can be obtained. A lower bound on the number of new families of binary sequences is also established.     

Cloud removal of remote sensing image based on multi-output support vector regression

Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.     

Fast image retargeting based on strip dividing and resizing

Based on image strip dividing, an effective and fast image retargeting algorithm is proposed for resizing images. First,we construct the image energy map using gradient magnitude of the pixels and calculate the accumulated energy of each column,dividing the image into several strips by integrating similar energy columns. The reduced amount of dimension is decided in inverse proportion to the average energy for each strip. Then we retarget the image combining scaling with cropping in terms of each strip＇s reduced ratio. Experiment results show that the proposed algorithm is capable of implementing fast image retargeting and preserving both the local structures and the global visual effect of the image.     

高移动性Jakes信道的学习与估计

在高移动场景下, 信道具有快速时变性和非平稳特性, 对信道的准确估计提出了新的挑战。针对高移动性Jakes信道, 提出一种基于图像重建和恢复原理的信道学习估计网络。首先，根据Jakes信道矩阵中局部相关特性, 构建快速超分辨卷积神经网络提取信道特征, 并对信道插值完成信道图像建模。然后, 利用去噪神经网络降低信道噪音的影响, 进一步提高估计精度。最后，通过时域和频域的仿真测试, 所提方案估计性能优于传统算法。在与现有基于深度学习最新方法比较中, 所提方案也有性能优势, 并且收敛速度更快。     

基于C均值分类的极化SAR图像白化斑点滤波方法

极化合成孔径雷达SAR图像相干斑抑制基本原理是依据各通道间的极化信息差异。提出在各极化通道上分别进行非监督C均值分类,在标号图像中对各区域的水平通道平均散射强度、交叉极化通道与同通道强度比以及同极化通道间的相关系数等参数进行估计,将估计值应用到极化白化滤波器(PWF)中,从而得到改进的PWF滤波输出。经真实极化SAR图像检验,表明该方法能够提高传统PWF滤波器在均匀区域对相干斑的抑制能力。     

基于局部一致性的马尔可夫随机场去雾

为克服暗通道先验的适用局限性,同时增强一阶马尔可夫随机场对图像全局信息的约束能力,在颜色衰减先验的基础上,提出了一种局部一致马尔可夫随机场(Markov random fields, MRF)单幅图像去雾算法。首先,结合颜色衰减和暗通道两先验假设的特征，获取普适性更强的介质传输图粗估计,然后利用基于颜色特征的图像局部一致块代替MRF的二阶及其高阶能量项来构造代价函数,达到优化介质传输图和获取最终去雾图像的目的。实验结果表明,所提算法可以获取细节保持更好且鲁棒性更强的去雾效果。     

WHO WON THE 2008 OLYMPICS？ A MULTICRITERIA DECISION OF MEASURING INTANGIBLES

It often happens that at the end of Olympics games the medals won by more than one country are many and close in total number as in the 2008 games where China won 100 medals with many gold ones and the United States won 110 medals but with a lesser number of gold medals. The question is： Although it is often done arbitrarily, is there a way to quantify the values of gold, silver and bronze medals legitimately to resolve this concern？ This short exposition shows that there is by using the author＇s theory for the measurement of intangibles, the Analytic Hierarchy Process.     

Asymptotic and stable proper ties of general stochastic functional differential equations

The asymptotic and stable properties of general stochastic functional differential equations are investigated by the multiple Lyapunov function method, which admits non-negative up-per bounds for the stochastic derivatives of the Lyapunov functions, a theorem for asymptotic properties of the LaSal e-type described by limit sets of the solutions of the equations is obtained. Based on the asymptotic properties to the limit set, a theorem of asymptotic stability of the stochastic functional differential equations is also established, which enables us to construct the Lyapunov functions more easily in application. Particularly, the wel-known classical theorem on stochastic stability is a special case of our result, the operator LV is not required to be negative which is more general to fulfil and the stochastic perturbation plays an important role in it. These show clearly the improvement of the traditional method to find the Lyapunov functions. A numerical simulation example is given to il ustrate the usage of the method.     

Spectral analysis and stabilization of a coupled wave-ODE system

In this paper, an interconnected wave-ODE system with K-V damping in the wave equation and unknown parameters in the ODE is considered. It is found that the spectrum of the system operator is composed of two parts： Point spectrum and continuous spectrum. The continuous spectrum consists of an isolated point 1 1/d, and there are two branches of the asymptotic eigenvalues： The first branch is accumulating towards 1 -2, and the other branch tends to -∞. It is shown that there is a sequence of generalized eigenfunctions, which forms a Riesz basis for the Hilbert state space. As a consequence, the spectrum-determined growth condition and exponential stability of the system are concluded.     

Robust bounded control for uncertain flight dynamics using disturbance observer

The robust bounded flight control scheme is developed for the uncertain longitudinal flight dynamics of the fighter with control input saturation invoking the backstepping technique. To enhance the disturbance rejection ability of the robust flight control for fighters, the sliding mode disturbance observer is designed to estimate the compounded disturbance including the unknown external disturbance and the effect of the control input saturation. Based on the backstepping technique and the compounded disturbance estimated output, the robust bounded flight control scheme is proposed for the fighter with the unknown external disturbance and the control input saturation. The closed-loop system stability under the developed robust bounded flight control scheme is rigorously proved using the Lyapunov method and the uniformly asymptotical convergences of all closed-loop signals are guaranteed. Finally, simulation results are presented to show the effectiveness of the proposed robust bounded flight control scheme for the uncertain longitudinal flight dynamics of the fighter.     

Multi-agent consensus with time-varying delays and switching topologies

The consensus problems of multi-agents with time-varying delays and switching topologies are studied. First, assume that an agent receives state information from its neighbors with fixed communication delays and processes its own state information with time-varying self-delay respectively. The state time-delay feedback is introduced into the existing consensus protocol to begenerate an improved protocol. Then a sufficient condition is derived which can make the system with time-varying self-delays achieve the consensus. On this basis, a specific form of consensus equilibrium influenced by the initial states of agents, time-delays and state feedback intensity is figured out. In addition, the multi-agent consensus is considered with time-varying topologies. Finally, simulations are presented to il ustrate the validity of theoretical results.