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说明：  详细介绍了matlab图像处理函数，并总结了函数的适用情况。实验问题详细的答案讲解。(Details of the matlab image processing function, and summarizes the functions of the application. Detailed answers to explain the experimental problem.)

总体PCC和集团统计混淆矩阵3D 显示一个3D混淆矩阵，并返回N组深入每个组的整体统计和统计(Confusion Matrix 3D with Overall PCC and Group Statistics)

镜像经验模态分解，可以实现信号的分析与分解，也可以做时频图(Mirroring the empirical mode decomposition, analysis and decomposition of the signal can be achieved, but also can do the time-frequency diagram)

基于迫零（ZF）与MMSE的两种预编码MATLAB程序(Based on zero forcing (ZF) and MMSE precoding two MATLAB programs)

潮流计算的MATLAB源程序 简单潮流计算的牛顿拉夫逊程序，相关的原始数据数据数据输入格式如下： B1是支路参数矩阵，第一列和第二列是节点编号。节点编号由小到大编写 对于含有变压器的支路，第一列为低压侧节点编号，第二列为高压侧节点 编号，将变压器的串联阻抗置于低压侧处理。 第三列为支路的串列阻抗参数。 第四列为支路的对地导纳参数。 第五烈为含变压器支路的变压器的变比 第六列为变压器是否是否含有变压器的参数，其中“1”为含有变压器， “0”为不含有变压器。 B2为节点参数矩阵，其中第一列为节点注入发电功率参数；第二列为节点 负荷功率参数；第三列为节点电压参数；第六列为节点类型参数，其中 “1”为平衡节点，“2”为PQ节点，“3”为PV节点参数。 (MATLAB source of power flow calculation)

matlab官方出品的上手教程，科研必备。(Matlab official production of the first-hand course, scientific research is necessary.)

无线合作分集网络中资源分配策略研究的论文(Cooperative diversity in wireless network resource allocation policy research papers)

BPSK仿真，Eb/N0=0:10,得出BER与Eb/N0的关系曲线，AWGN信道(BPSK simulation, Eb/N0 = 0:10, draw BER versus Eb/N0 of the curve, AWGN channel)

说明：  人工蜂群算法(Artificial Bee Colony, ABC)是由Karaboga于2005年提出的一种新颖的基于群智能的全局优化算法，其直观背景来源于蜂群的采蜜行为，蜜蜂根据各自的分工进行不同的活动，并实现蜂群信息的共享和交流，从而找到问题的最优解。对原始算法的改进。(The improvement of the original Artificial Bee Colony.)

OTSU Gray-level image segmentation using Otsu s method. Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern. 9:62-66 1979). Thresholds are computed to maximize a separability criterion of the resultant classes in gray levels. OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the corresponding Iseg is therefore a binary image. The pixel values for Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ... [Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability criterion within the range [0 1]. Zero is obtained only with images having less than n gray level, whereas one (optimal value) is obtained only with n-valued images.