时隙ALOHA在MATLAB中的仿真

时隙ALOHA使用了各种控制算法以保证系统的稳定性,在应用控制算法前,必须对算法的性能进行前期仿真测试．在比较三种常用网络仿真工具的基础上,分析了MATLAB离散事件仿真原理及基于蒙特卡洛法的随机数生成原理．给出了MATLAB仿真平台下实现时隙ALoHA控制算法的流程图,并依据该流程编写了MATLAB代码对时隙ALOHA的伪贝叶控制算法性能进行测试．仿真结果表示,MATLAB能很好的实现对时隙ALOHA控制算法的仿真．     

BCH—LT级联码的分析与设计

通过把无码率LT码和BCH码进行级联,构造了一类性能优异的级联码,并且提出了软判决迭代译码算法,应用在内外码的译码过程中,解决了错误平层问题。通过仿真可知,在加性高斯白噪声二进制对称信道中,该级联码在很宽的信噪比下具有较好的性能,可以进行可靠的传输。     

用两种算法实现的自适应判决反馈均衡器性能比较

介绍了自适应判决反馈均衡器（ADFE）的结构和原理，分别用两种算法仿真实现ADFE：一种是判决反馈均衡算法，另一种是维特比算法的最大似然序列估计（MLSE）．文中对两种算法的差错率性能进行了比较。     

无约束优化问题的一种混合进化规划算法

传统的进化规划算法解决无约束优化问题时只依赖于单纯的高斯变异,使得这种算法具有一定的局限性．本文在分析高斯变异、柯西变异的优缺点的基础上提出了混合进化规划算法,给出了算法步骤,用两个经典的数值例子比较说明了所给算法的有效性．     

EM算法的研究

聚类分析是信息产业界非常热门的研究方向,也是数据挖掘的最主要的功能之一。期望最大值(EM)是聚类分析中的重要技术。本文对期望最大值(EM)算法进行分析,并对高斯混合模型进行了研究,利用最大似然估计和EM算法在一组人工生成高斯数据上进行试验,达到了较好分类的效果。     

基于低复杂度次优译码算法的准正交空时分组码（英文）

针对成对译码算法复杂度高及迫零/最小均方误差算法性能较差等问题,提出了一种pairwise-quasi-ZF/pairw ise-quasi-MMSE的低复杂度次优译码算法.利用quasi-ZF或quasi-MMSE算法计算出部分发射信号的统计判决值,将该判决值作为译码结果代入成对译码算法表达式中,从而计算出剩余部分发射信号的统计判决值并作为译码结果.比较了所提算法与几种传统译码算法的系统误比特率性能,并比较了ZF,MMSE,quasi-ZF和quasi-MMSE四种算法的计算量.仿真结果表明所提算法的BER性能与quasi-ZF算法和quasi-MMSE算法相比有明显改善,与pairwise-ZF和pairwise-MMSE算法相比BER性能相近,计算量却大大降低.     

一种新的正方型QAM解映射算法及FPGA实现

针对传统QAM解映射算法的运算量和复杂度过高的问题,结合方型QAM星座图特点,提出一种基于二叉树结构的判决输出解映射算法,并完成了基于FPGA的电路设计和仿真验证.理论分析和MATLAB仿真结果表明,该算法与ML算法具有相同的抗噪声性能,但却极大地降低了正方型QAM解映射算法的复杂度和运行时间.     

改进的基于谱直方图人脸图像特征提取方法

提出了一种基于谱直方图的人脸图像特征提取的方法．使用梯度滤波器、高斯-拉普拉斯滤波器和局域二值模式（LBP）构成的滤波器组来计算待测图像的谱直方图．仿真实验结果和分析表明,该算法具有较高的效率和准确性．     

基于MATLAB的BFSK在高斯白噪声信道中的传输性能研究

本文在MATLAB的环境下构建了BFSK在高斯白噪声信道中传输的系统模型,通过运行程序仿真,研究系统的误码率与信道质量的关系,找到在高斯白噪声信道上传输的最大信噪比及所需发射功率和调制频率,从而得出该系统在高斯白噪声信道中的最佳传输性能。     

广义非线性互补问题的高斯牛顿算法的收敛性

首先将定义在闭凸多面锥上的广义互补问题转化为一个等价的非线性方程组,然后利用阻尼高斯牛顿算法来求解该非线性方程组．并在适当条件下证明了算法的全局收敛性．     

Adaptive foreground and shadow segmentation using hidden conditional random fields

Video object segmentation is important for video surveillance, object tracking, video object recognition and video editing. An adaptive video segmentation algorithm based on hidden conditional random fields (HCRFs) is proposed, which models spatio-temporal constraints of video sequence. In order to improve the segmentation quality, the weights of spatio-temporal con- straints are adaptively updated by on-line learning for HCRFs. Shadows are the factors affecting segmentation quality. To separate foreground objects from the shadows they cast, linear transform for Gaussian distribution of the background is adopted to model the shadow. The experimental results demonstrated that the error ratio of our algorithm is reduced by 23% and 19% respectively, compared with the Gaussian mixture model (GMM) and spatio-temporal Markov random fields (MRFs).     

Integrated phase unwrapping algorithm for the measurement of 3D shapes by Fourier transform profilometry

An integrated and reliable phase unwrapping algorithm is proposed based on residues and blocking-lines detection, closed contour extraction and quality map ordering for the measurement of 3D shapes by Fourier-transform profilometry （FTP）. The proposed algorithm first detects the residues on the wrapped phase image, applies wavelet analysis to generate the blocking- lines that can just connect the residues of opposite polarity, then carries out the morphology operation to extract the closed contour of the shape, and finally uses the modulation intensity information and the Laplacian of Gaussian operation of the wrapped phase image as the quality map. The unwrapping process is completed from a region of high reliability to that of low reliability and the blocking-lines can prevent the phase error propagation effectively. Furthermore, by using the extracted closed contour to exclude the invalid areas from the phase unwrapping process, the algorithm becomes more efficient. The experiment shows the effectiveness of the new algorithm.     

并行动态随机数在DES加密算法中的应用

针对DES加密算法中使用的随机数离散度不高、线性相关度大的问题,提出采用并行动态随机数算法。经实验及分析证明该算法能显著提升DES加密算法的抗攻击强度,是一种新的提高DES安全性的有效途径。     

Non-interior continuation algorithm for solving system of inequalities over symmetric cones

As a basic mathematical structure,the system of inequalities over symmetric cones and its solution can provide an effective method for solving the startup problem of interior point method which is used to solve many optimization problems.In this paper,a non-interior continuation algorithm is proposed for solving the system of inequalities under the order induced by a symmetric cone.It is shown that the proposed algorithm is globally convergent and well-defined.Moreover,it can start from any point and only needs to solve one system of linear equations at most at each iteration.Under suitable assumptions,global linear and local quadratic convergence is established with Euclidean Jordan algebras.Numerical results indicate that the algorithm is efficient.The systems of random linear inequalities were tested over the second-order cones with sizes of 10,100,,1 000 respectively and the problems of each size were generated randomly for 10 times.The average iterative numbers show that the proposed algorithm can generate a solution at one step for solving the given linear class of problems with random initializations.It seems possible that the continuation algorithm can solve larger scale systems of linear inequalities over the secondorder cones quickly.Moreover,a system of nonlinear inequalities was also tested over Cartesian product of two simple second-order cones,and numerical results indicate that the proposed algorithm can deal with the nonlinear cases.     

Random walk models for top-N recommendation task

Recently there has been an increasing interest in applying random walk based methods to recommender systems.We employ a Gaussian random field to model the top-N recommendation task as a semi-supervised learning problem.taking into account the degree of each node on the user-item bipartite graph,and induce an effective absorbing random walk (ARW) algorithm for the top-N recommendation task.Our random walk approach directly generates the top-N recommendations for individuals,rather than predicting the ratings of the recommendations.Experimental results on the two real data sets show that our random walk algorithm significantly outperforms the state-of-the-art random walk based personalized ranking algorithm as well as the popular item-based collaborative filtering method.     

基于服务质量的多属性群决策服务选择算法

为解决基于服务质量的服务选择,提出了一个多属性群决策算法.该算法通过使用Borda社会选择函数集成了多种具有不同原理用于计算不断变化的服务质量方法的结果,从而增强了服务选择的可靠性.实验结果表明该方法具有较好的可扩展性,从而能适用于大规模的分布式服务计算环境;同时相比于随机和轮转服务选择策略,所提出的基于群决策的服务选择策略具有更优的性能,它能够有效地优化服务选择.该算法不仅仅是增加了一种用于获取不断变化的web服务质量的方法,而是建立一个具有多层知识的综合信息体,从而保障了服务选择的正确性.     

多项分布参数的Bayes区间估计的比较分析

通过对多项分布的Dirichlet先验和无信息先验（non—inforrmtive）的研究,用Matlab生成服从多项分布的一组随机数,并对每一组随机数在不同先验下的Bayesian等尾区间和HPD区间进行分析,总结出未知参数区间估计在不同先验下的特性。     

基于随机化的排序过程分析

随机化算法的流程和结果受所使用的随机函数的影响，按影响的性质和程度分为“随机不影响算法的执行结果”、“随机影响执行结果的正确性”、“随机影响执行结果的优劣”三种情况，主要表现在算法的时间效率上．用随机函数全部或部分地抵消最坏输入，可使算法的时间效率不完全依赖于输入的好坏，通过对输入的适当控制，使得执行结果相对稳定．     

基于混沌的密钥安全分配

利用混沌效应的特性,提出基于混沌的密钥加密算法.混沌加密算法以非线性动力学中的混沌为理论基础,利用其初值敏感性和迭代不会重复性以产生序列随机数,从而应用于密钥数据加密,再把加密的密钥数据进行RSA加密.通过程序仿真表明,该方案能有效加密密钥数据,加密速度快,效果好,安全性强.     

Random walk models for top-N recommendation task

Recently there has been an increasing interest in applying random walk based methods to recommender systems. We employ a Gaussian random field to model the top-N recommendation task as a semi-supervised learning problem, taking into account the degree of each node on the user-item bipartite graph, and induce an effective absorbing random walk （ARW） algorithm for the top-N recommendation task. Our random walk approach directly generates the top-N recommendations for individuals, rather than predicting the ratings of the recommendations. Experimental results on the two real data sets show that our random walk algorithm significantly outperforms the state-of-the-art random walk based personalized ranking algorithm as well as the popular item-based collaborative filtering method.