基于等效子午面与互信息量的医学图像配准

提出了一种新的基于等效子午面和互信息量的三维医学图像快速配准算法--EMP-MI算法.传统的互信息量的方法需要考虑整个三维数据的信息,计算复杂度大,无法满足临床需要.而本算法将三维数据的配准转化为二维数据的配准,在保证精度的前提下,减少了配准所需时间.文中创新点在于利用主成分分析计算出图像的等效子午面并将图像转化到标准坐标系下,从而将质心和等效子午面粗配准,精细配准时只需要对浮动图像进行微小的调整计算等效子午面的互信息量,这就大大提高了配准速度,减少了陷入局部极值的可能.实验结果表明这种先整体后局部的方法能准确、快速地处理图像刚性配准问题,特别适用于三维医学图像的配准.     

基于评价相似度对WSN声誉系统合谋攻击的检测机制

针对无线传感器网络(WSN)声誉系统中可能存在的多个恶意节点合谋攻击网络节点,并影响其准确定位等安全问题,提出了恶意推荐(BS)合谋攻击团及其检测机制(BSCD),并给出了该机制的实现方法。该机制通过对推荐节点进行异常检测,分析推荐节点间的评价行为相似度,有效检测出存在的合谋攻击团,从而降低其对声誉系统的破坏和影响。仿真实验表明,BSCD在检测和抵制BS合谋攻击团方面效果显著,有效提高了声誉系统中恶意节点检测率和整个系统抵抗恶意节点破坏的能力。     

GwMKnn：针对类属性数据加权的MKnn算法

互k近邻MKnn算法是k-近邻算法的一种有效改进算法,但其对类属性数据通常采用属性值相同为0,不同为1的方法处理,从而在类属性数据较多的数据集上分类效率受到一定程度的抑制。针对MKnn对类属性数据处理方法的不足,对类属性数据的处理引进类别基尼系数的概念,对同类样本,用基尼系数统计某一类属性中不同值分布对这个类的贡献度作为此类属性的权重,并以此作为估算不同样本之间的相似性对MKnn进行优化,扩宽MKnn的使用面。实验结果验证了该方法的有效性。     

基于小波变换和互信息的医学图像配准

为提高医学图像配准效果,提出了一种基于小波变换和互信息的配准方法。该方法首先通过小波变换将图像分层,并用小波分解的近似分量从最顶层开始搜索,同时以添加边界约束条件的下降单纯形法为搜索策略,而以搜索结果作为下一层搜索的粗略位置;然后逐层细化,以实现由粗到细的搜索过程;同时,针对不同的分解层采用不同的配准方法,即下层引入结合空间信息的区域互信息(RMI)为相似性测度,而上层采用PV插值法,以避免陷入局部极值。最后将此法应用于加噪MR图像单模配准、PET图像单模配准和MR-PET图像多模配准的。实验结果表明,该方法可以得到精确、有效的配准结果。与传统方法相比,该方法不仅配准精度高、抗噪性能好,而且计算效率高。     

使用特征点与灰度值的医学图像局部配准方法

针对医学图像配准中,存在某些图像间大部分区域没有差异或者存在差异但不被关心的情况,提出了一种局部图像配准方法。该方法使用局部可控的紧支撑径向基函数作为配准变换函数,通过在感兴趣区域设置特征点,将变换函数作用范围限制在图像中某一特定区域,保持其他区域不发生变形。利用图像间的互信息量作为测度函数,更加精确地求解变换函数。在优化策略的选择中,将图像配准看作为寻优过程,采用基于小生境的遗传算法优化变换函数参数,能够克服经典遗传算法早熟、搜索能力差等缺点。通过对已知变换函数的仿真图像与真实医学图像进行实验,结果表明该算法能够准确地找到较优的变换函数,并且将作用区域限制在较小范围内。该方法结合了基于特征点和基于像素配准方法的优点,有效的搜索策略保证了变换函数准确性,是一种可行的、鲁棒的局部医学图像配准方法。     

Non-linear variable selection for artificial neural networks using partial mutual information

Artificial neural networks (ANNs) have been widely used to model environmental processes. The ability of ANN models to accurately represent the complex, non-linear behaviour of relatively poorly understood processes makes them highly suited to this task. However, the selection of an appropriate set of input variables during ANN development is important for obtaining high-quality models. This can be a difficult task when considering that many input variable selection (IVS) techniques fail to perform adequately due to an underlying assumption of linearity, or due to redundancy within the available data.This paper focuses on a recently proposed IVS algorithm, based on estimation of partial mutual information (PMI), which can overcome both of these issues and is considered highly suited to the development of ANN models. In particular, this paper addresses the computational efficiency and accuracy of the algorithm via the formulation and evaluation of alternative techniques for determining the significance of PMI values estimated during selection. Furthermore, this paper presents a rigorous assessment of the PMI-based algorithm and clearly demonstrates the superior performance of this non-linear IVS technique in comparison to linear correlation-based techniques.     

The Application of the Commparable Corpora in Chinese—English Cross—lingual Information Retrieval

This paper proposes a novel Chinese-English Cross-Lingual Information Retrieval(CECLIR)model PME,in which bilingual dictionary and comparable corpora are used to translate the query terms.The proximity and mutual information of the term-paris in the CHinese and English comparable corpora are employed not only to resolve the translation ambiguities but also to perform the query expansion so as to deal with the out-of -vocabulary issues in the CECLIR.The evaluation results show that the query precision of PME algorithm is about 84.4% of the monolingual information retrieval.     

《操作系统》课程的教学体会

操作系统中,进程同步和互斥问题以及与之相关的信号量机制是教学过程中的重点和难点问题。本文介绍了教学实践中总结的有关如何用信号量机制解决进程同步与互斥问题的求解规律及教学经验,旨在提高教学效果,促进学生对操作系统基本原理的理解和掌握。最后针对本课程的特点,提出了操作系统今后的教学研究方向。     

视频眼震图中的瞳孔定位与转角测定方法

针对在大噪声环境下视频眼震图中瞳孔的精确定位及准确测定转角的问题,提出了一种新方法.该方法首先运用模板匹配方法实现了在大噪声环境下瞳孔的精确定位,接着运用最大互信息量方法解决了转角测定的问题.在模拟图上测试,取得了满意的效果.     

Object Detection and Localization by Dynamic Template Warping

A simple method is presented for detecting, localizing and recognizing instances of classes of objects, while accommodating a wide variation in an object's pose. The method utilizes a small two-dimensional template that is warped into an image, and converts localization to a one-dimensional sub-problem, with the search for a match between image and template executed by dynamic programming. For roughly cylindrical objects (like heads), the method recovers three of the six degrees of freedom of motion (2 translation, 1 rotation), and accommodates two more degrees of freedom in the search process (1 rotation, 1 translation). Experiments demonstrate that the method provides an efficient search strategy that outperforms normalized correlation. This is demonstrated in the example domain of face detection and localization, and can extended to more general detection tasks. An additional technique recovers rough object pose from the match results, and is used in a two stage recognition experiment in conjunction with maximization of mutual information.