SHUIYINTUXIANGCHULICHENGXU1219

The model used for creating the reference voltage is shown in Fig. 4. First, photovoltaic output current (Ipv) and output voltage (Vpv) are passed through a first order low pass filter with a magnitude of G = 1 and a time constant of T = 0.01 seconds in order to filter out the high frequency components or harmonics from these signals as shown in Fig. 5 and Fig. 6. The filtered current and voltage signals (Ipv_F and Vpv_F) are then fed into the MPPT control block that uses the Incremental Conductance Tracking Algorithm. An algorithm that is based on the fact the slope of the PV array power curve shown in Fig. 7 is zero at the Maximum Power Point (MPP), positive on the left of the MPP, and negative on the right. The MPP can thus be tracked by comparing the instantaneous conductance (I/V) to the incremental conductance (∆ I/∆ V) [11] as in (1):

用Matlab语言编写的实现fisher准则，可以很方便的验证fisher准则(Fisher guidelines for implementation of the Matlab language, you can easily verify that the fisher criteria)

Animation of a Simple Pendulum using the Euler-Cromer numerical method

通过重心法确定光斑图像的中心。根据个像素的灰度值和所在的位置乘积和总面值作比，进而求得重心。(Through the center of gravity method to determine the center of the image spot. According to the gray-scale pixel value and the location of the product and the total for more than face value, and then get the focus.)

说明：  判别分析的matlab源代码，文件中有三种方法，每种方法都有测试数据(Discriminant analysis matlab source code, documentation there are three methods, each method has the test data)

光流运动估计算法，对各种情况下图片都有效果(optical flow)

Matlab时频分析技术及其应用书源代码(Matlab time-frequency analysis technology and its applications book source code)

PMSM model with state feedback

探讨MATLAB在 数字水印中的应用，利用MATLAB语言编程实现了一类数字水印算法的嵌入、攻击及提取过程,并给出了程序运行 的结果。 (MATLAB to explore in the digital watermarking application, the use of MATLAB programming language to achieve a class of digital watermarking algorithm for embedding, attacks and extraction process, and gives the results of the program is running.)

温柔的形变模型分类器和两个不同的weak-learners决定树桩和感知。 问题是进行多层次的one-vs-all策略(Gentle AdaBoost Classifier with two different weak-learners : Decision Stump and Perceptron. Multi-class problem is performed with the one-vs-all strategy)