time_features1

remove the lower harmonic orders from output of 11 level inverter

线列阵对相干信号进行目标方位估计，包括前向平滑，后向平滑，双向平滑，修正music算法等(Line Array for coherent signal DOA estimates, including forward smoothly, backward smoothing, double smoothing, modified music algorithm)

文章除了对基于DCT的图像压缩编码进行研究外，还对基于其他算法的图像压缩编码也作了相应的研究。在完成理论方面的研究后，应用MATLAB仿真软件，实现了基于DCT的图像压缩编码。(The article is in addition to picture that according to the DCT compress coding expatiating, returning to picture that according to other calculates way empress coded to also make the homologous introduction, make discuss more detailed with completely. At study that complete the theories empress, apply the MATLAB imitate true software, realizes according to the DCT picture compresses the coding.)

去混响的一种方法，使用两段法去混响。内附有语音文件方便比较。(a kind of dereverberation method )

该算法完整地描述了小波神经网络对交通流预测的过程(The algorithm is a complete description of wavelet neural network in forecasting of traffic flow process)

一个Newton_Raphon迭代的简单代码，还有Primal Dual Interior Point的例子(A simple iterative code of Newton_Raphon method)

移动通信系统中，卷积码的matlab仿真程序(Convolutional code matlab simulation program)

关于狭缝衍射的夫琅和费远场模拟的例子，是高等物理光学模拟的入门例子。(About slit Fraunhofer diffraction far-field simulation example is the example of the entry of higher physical optics simulation.)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)

k -medoids algorithm is a clustering algorithm related to the k -means algorithm and the medoidshift algorithm.