夫琅禾费衍射光强的反常分布和 Matlab模拟

讨论了夫琅禾费衍射实际实验中的反常现象,在一定条件下给出了计算机模拟的结果。实验表明,衍射图样的实验数据点分布结果与数值计算的模拟结果一致。由此可知,夫琅禾费衍射光强的反常分布是远场条件失效所致。     

基于区域最大相似度的快速图像分割算法

针对基于区域最大相似度图像分割(MSRM)算法中利 用颜色直方图描述符计算相邻区域之间相似度存 在着计算量大和描述能力的不足,提出改用基于局部二值模式(LBP)纹理描述符计 算相邻区域相似度的改进MSRM(IMSRM)算法。LBP描述符通过对像素点之间局 部微结构进行编码实 现了对其空间关系的描述,有效提高了对区域特征的描述能力,并且所获得的特征矢量维数 远小于颜色直 方图,区域之间的相似度计算效率大幅度得到提高。与MSRM算法对比实验表明,IMSRM算法 可以从各种复杂背景中有效提取待分割对象的轮廓,所提取的轮廓边缘细节上更优,算法执 行效率能够提高50%左右。     

视频感兴趣区域快速提取与编码算法

如何在复杂度和时延受限的条件下,减少时域、空域和统计冗余,获得率失真性能的最优化是视频编码设计的核心问题。作为最新的视频编码标准,H.264在取得更高压缩率的同时,编码复杂度也随之剧增,限制了其在实时环境中的应用。本文在传统视频编码框架基础上,结合人类视觉系统感知特征,提出一种基于编码信息的视频感兴趣区域快速提取算法和优化编码方案。仿真实验结果表明,本文提出的编码策略在重建视频质量基本无损失和维持原有码率的提前下,将H.264编码时间平均节省52.55%,并能够与其它快速编码技术相结合,进一步提高H.264编码速度,以更好的适应异构网络及多样性终端环境,满足日益丰富的多媒体与通信业务需求。     

Sliding mode control and stability analysis of buck DC-DC converter

This paper is proposed to deal with the voltage regulation of buck DC-DC converter based on sliding mode control (SMC) technology. A buck DC-DC converter with parasitic resistance is inherently a bilinear system possessing inevitable uncertainties, such as variable resistive load and input disturbance. First, the buck DC-DC converter is modified into an uncertain linear model. Then, SMC technology is adopted to suppress the input disturbance and reduce the effects from the load variation. In addition, the continuous conduction mode (CCM) for normal operation can be guaranteed by the design of sliding function. Finally, experimental results are included for demonstration.     

基于结构张量特征值的纹理图像分割模型

通过对结构张量的研究和对纹理图像的分析,提出了一种基于结构张量特征值的标量型纹理特征描述,将其和原图像分别嵌入到两相模糊区域竞争模型和CV模型中,给出了一种纹理和灰度相结合的无监督纹理图像分割模型.为获得新模型的全局最优解,采用了Chambolle对偶法加以实现.针对自然和合成纹理图像进行了相关实验,结果表明该模型特征数据维数少,具有较快的收敛速度和更准确的分割效果.     

基于子块区域分割和自嵌入技术的全盲多功能图像水印算法

利用子块区域分割思想和自嵌入技术提出了一种只利用攻击图像就能进行版权鉴别和内容认证的全盲多功能图像水印算法。将原始图像分割成不重叠的子块,对每个子块进行离散余弦变换,将每个子块离散余弦变换矩阵分成为区域1和区域2两部分,然后对区域1进行奇异值分解,通过判断奇异值均值的最高位数字奇偶性产生特征水印,然后通过调整区域2的2个离散余弦变换交流系数大小自嵌入特征水印,最后对每个子块进行逆离散余弦变换得到含水印图像。算法通过结合将区域1产生的特征水印自嵌入区域2和在区域2盲提取认证水印实现全盲检测和多功能。实验结果表明,算法既能实现版权保护又能实现内容认证,而且还具有区分恶意篡改和无意篡改的能力。     

基于感兴趣区域的图像检索方法

提出了一种新的基于感兴趣区域的图像检索算法。首先基于多曲率多项式提取图像显著点,并依据显著点提取图像感兴趣区域,然后基于感兴趣区域的颜色和纹理特征进行图像检索。实验结果表明该方法可有效地提取图像感兴趣区域,并取得了较好的检索效果。     

Features of the turbulent flow around symmetric elongated bluff bodies

The flow fields around three elongated bluff bodies with the same chord-to-thickness ratios but distinct leading and trailing edge details were measured at a Reynolds number of 3×10⁴. These models each represent a case where: leading edge shedding dominates, trailing edge shedding dominates and a case where there is a balance between the two. The results show that the vortex street parameters vary between the models, and in particular, the shedding frequencies are significantly altered by the geometry. However, contrary to the current understanding for shorter bluff bodies, the scale of the recirculation region is found to be similar for each model, even though the shedding frequency changes within the range from 0.15 to 0.24. Also, the base pressure does not follow trends with shedding frequency expected from shorter bluff bodies. A force balance of the recirculation region shows that the near wake of each body is significantly affected by the Reynolds shear stress distribution and the resultant force due to the pressure field in the mean recirculation region. These differences infer that the distinct vortex formation characteristics depend on the state of the trailing edge shear layers. The boundary layers at the trailing edge have been quantified, as have the leading edge separation bubbles, and the marked differences in the wake details are shown to depend on the leading edge separation.     

Empirical likelihood inference for the accelerated failure time model

Accelerated failure time (AFT) models are useful regression tools for studying the association between a survival time and covariates. Semiparametric inference procedures have been proposed in an extensive literature. Among these, use of an estimating equation which is monotone in the regression parameter and has some excellent properties was proposed by Fygenson and Ritov (1994). However, there is a serious under-coverage problem for small sample sizes. In this paper, we derive the limiting distribution of the empirical log-likelihood ratio for the regression parameter on the basis of the monotone estimating equations. Furthermore, the empirical likelihood (EL) confidence intervals/regions for the regression parameter are obtained. We conduct a simulation study in order to compare the proposed EL method with the normal approximation method. The simulation results suggest that the empirical likelihood based method outperforms the normal approximation based method in terms of coverage probability. Thus, the proposed EL method overcomes the under-coverage problem of the normal approximation method.     

Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation

Attribute reduction is one of the key issues in rough set theory. Many heuristic attribute reduction algorithms such as positive-region reduction, information entropy reduction and discernibility matrix reduction have been proposed. However, these methods are usually computationally time-consuming for large data. Moreover, a single attribute significance measure is not good for more attributes with the same greatest value. To overcome these shortcomings, we first introduce a counting sort algorithm with time complexity O(∣C∣ ∣U∣) for dealing with redundant and inconsistent data in a decision table and computing positive regions and core attributes (∣C∣ and ∣U∣ denote the cardinalities of condition attributes and objects set, respectively). Then, hybrid attribute measures are constructed which reflect the significance of an attribute in positive regions and boundary regions. Finally, hybrid approaches to attribute reduction based on indiscernibility and discernibility relation are proposed with time complexity no more than max(O(∣C∣²∣U/C∣), O(∣C∣∣U∣)), in which ∣U/C∣ denotes the cardinality of the equivalence classes set U/C. The experimental results show that these proposed hybrid algorithms are effective and feasible for large data.