LPKXYZ

一、 本程序采用16QAM调制方式，对一串2进制信源进行调制，采用（7，4）循环码对信源做信道编码，用升余弦滚降函数进行基带调制，再调到高频信道；在信道上加入高斯白噪声，运用匹配滤波器解调，画出解调星座图，运用最小欧氏距离译码判决，再对信号进行信道解码，画出采用信道编码技术和不采用信道编码技术的误比特率图。(First, this program uses 16QAM modulation mode, for a string of two binary source is modulated, using (7,4) cyclic code of the source-channel coding to do with a raised cosine roll-off function for base-band modulation, and then transferred to a high frequency channel in the channel by adding Gaussian white noise, the use of matched filter demodulation, demodulation constellation plot, using the minimum Euclidean distance decoding decision, and then right channel signals, decoding, channel coding techniques used to draw and non- channel coding techniques using bit error rate plans.)

这个程序使用了遗传算法来优化点对点之间的轨迹度量。(This code proposes genetic algorithm (GA) to optimize the point-to-point trajectory planning for a 3-link (redundant) robot arm.)

用于填冲图片中的空白域，设定一个种子点，然后从种子点开始填冲(Used to fill in the blank fields punch picture, set a seed point, and then rushed from the seeds begin to fill)

Three-Phase On-Line Tap Changing Regulating Transformer .

Member-function definitions for class Account. 源代码(Member-function definitions for class Account. Source code)

针对Otsu一维、二维、两个一维方法以及各种去噪方法的分析编程(For analysis of the programming Otsu one-, two-, two one-dimensional method and various denoising methods)

MATLAB中用m文件实现卡尔曼滤波算法(M file implementation using MATLAB Kalman filter algorithm)

matlab语言，编写一个简单的遗传算法程序(Matlab language, write a simple genetic algorithm program)

说明：  利用头脑风暴算法，求解cec——2017 benchmark，是史玉回老师的源代码进行的修改(brain storm algorithm)

Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST- LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), princi- pal component regression (RPCR), partial least squares (RSIMPLS) and classi¯ cation (RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.