Burg

有关SYSYTEMVIEW的一个小例子，自己做的，刚学，关于2DPSK(The SYSYTEMVIEW a small example, do it yourself, just learning about 2DPSK)

余弦距离进行聚类分析，余弦距离kmeans进行聚类分析，(Cluster analysis cosine distance, cosine distance from the cluster analysis kmeans,)

Main program for compression

说明：  lte资源分配方案，三种方案...........(lte resource allocation)

说明：  用MATLAB写的Gaussian Elimination算法即LU算法(Written using MATLAB or LU algorithm Gaussian Elimination Algorithm)

医学信号处理实验指导--对ECG信号的处理-目的是对ECG信号进行预处理，以便将其进一步应用到于心电的自动检测；本次实验还包括心率的求取 (medical signal processing experimental guidance-- right ECG signal processing- is designed to ECG signal preconditioning in order to further its application of ECG in the automatic detection; the experiment also includes HR strike)

Power Quality Improvement and Mitigation Case Study Using Distributed Power Flow Controller

IEM模型，积分方程模型，模拟地表HH计划下后向散射系数(IEM model, integral equation model, simulated surface HH program backscattering coefficient)

测试可以跑，根据自己情况修改下函数即可. NSGA-III 首先定义一组参考点。然后随机生成含有 N 个（原文献说最好与参考点个数相同）个体的初始种群，其中 N 是种群大小。接下来，算法进行迭代直至终止条件满足。在第 t 代，算法在当前种群 Pt的基础上，通过随机选择，模拟两点交叉（Simulated Binary Crossover，SBX）和多项式变异 产生子代种群 Qt。Pt和 Qt的大小均为 N。因此，两个种群 Pt和 Qt合并会形成种群大小为 2N 的新的种群 Rt=Pt∪Qt。 为了从种群 Rt中选择最好的 N 个解进入下一代，首先利用基于Pareto支配的非支配排序将 Rt分为若干不同的非支配层（F1，F2等等）。然后，算法构建一个新的种群St，构建方法是从 F1开始，逐次将各非支配层的解加入到 St，直至 St的大小等于 N，或首次大于 N。假设最后可以接受的非支配层是 L层，那么在 L+ 1 层以及之后的那些解就被丢弃掉了，且 St FL中的解已经确定被选择作为 Pt+1中的解。Pt+1中余下的个体需要从 FL中选取，选择的依据是要使种群在目标空间中具有理想的多样性。(The test can run and modify the function according to its own situation. NSGA-III first defines a set of reference points. Then the initial population containing N individuals (preferably the same number of reference points as the original literature) was randomly generated, where N was the size of the population. Next, the algorithm is iterated until the termination condition is satisfied. On the basis of current population Pt, the algorithm simulates two-point crossover (SBX) and polynomial mutation to produce offspring population Qt by random selection.)

说明：  小波阈值去噪的基本思想是先设置一个临界阈值λ，若小波系数小于λ，认为该系数主要由噪声引起，去除这部分系数;若小波系数大于λ，则认为此系数主要是由信号引起，保留这部分系数，然后对处理后的小波系数进行小波逆变换得到去噪后的信号。(The basic idea of wavelet threshold denoising is to set a threshold value of lambda first. If the wavelet coefficient is less than lambda, it is considered that the coefficient is mainly caused by noise and this part of the coefficient is removed. If the wavelet coefficient is larger than lambda, it is considered that the coefficient is mainly caused by signal, and this part of the coefficient is retained. Then the denoised signal is obtained by inverse wavelet transform of the processed wavelet coefficient.)