compare_segmentations

DPSK信号调制解调干扰性能分析，自己写的代码仅供参考！(DPSK signal modulation and demodulation performance analysis of interference, to write the code for reference purposes only!)

有限元法(finite element method)求波导中的TE模(FEM (finite element method) waveguide for the TE mode)

This letter presents a novel algorithm to compute the instantaneous frequency (IF) of a multicomponent nonstationary signal using a combination of fractional spectrograms (FS). A high resolution time frequency distribution (TFD) is defined by com-bining FS computed using windows of varying lengths and chirp rates. The IF of individual signal components is then computed by applying a peak detection and component extraction procedure

自己搭建的dc/dc变换器 ，里面采用PID法进行控制(To build their own dc/dc converter, which uses the PID control)

聚类算法,绝对值距离且比雪夫距离\平均值距离等.matlab程序,GUI界面,有详细说明(Clustering algorithm, the absolute value of the distance and Chebyshev distance the average distance. Matlab program, GUI interface, there is a detailed description)

说明：  一个“车辆用无极变速器”的典型工况仿真的算法，个人项目，仅供参考。(A )

应用蒙特卡洛的方法为欧式看涨期权定价。同时，该程序是应用对偶方法进行模拟的。(pricing european call option with antithetic method in monte carlo)

提取数据波形时域特征（绝对幅值、平均值、峭度、过零点）并且用LDA分类(Time-domain waveform data extraction features (absolute amplitude, mean, kurtosis, zero crossing) and classification with LDA)

Vincent Jaquet & Marek Pechal的社交网络扩散模型，matlab(Social network diffusion model of Vincent Jaquet & Marek Pechal, matlab)

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.)