OFDMtb

一个关于最速法的，matlab程序。经试验，效果很好。(A law on the most speed, matlab program. After testing, the effect is very good.)

The KLT Tracker algorithm implementation. The starting location of the object must be manually initialized. Object tracking is accomplished using two methods: simple track and advanced track.

polish book about cybership 2

auss Numerical Integration for univariate, finite integrals

矩形波的产生，分三个步骤，第一步先产生矩形波，然后画出产生矩形波的源代码 (Rectangular wave generated in three steps, the first step to produce the rectangular wave, and then draw rectangular wave generated source code)

optiFDTD计算MIM波导缺陷结构，这是源文件

matlab要求编写函数，并画出图的matlab习题，供大家参考(prepared Matlab functions, and draws a plan of Matlab Exercise, for your reference! !)

calssification algorithm5

关于合成孔径雷达成像R-D算法的一个很好的资料，跟大家共享一下，希望对大家有用。(R-D algorithm)

为了精准、稳定地提取滚动轴承故障特征，提出了基于变分模态分解和奇异值分解的特征提取方法，采用标准模糊C均值聚类(fuzzy C means clustering, FCM)进行故障识 别。对同一负荷下的已知故障信号进行变分模态分解，利用 奇异值分解技术进一步提取各模态特征，通过FCM形成标准聚类中心，采用海明贴近度对测试样本进行分类，并通过计算分类系数和“卜均模糊嫡对分类性能进行评价，将该方法 应用于滚动轴承变负荷故障诊断。通过与基于经验模态分解的特征提取方法对比，该方法对标准FCM初始化条件小敏感，在同负荷故障诊断中表现出更好的分类性能 变负荷故障诊断时，除外圈故障特征线发生明显迁移，其他测试样本故障特征线仍在原聚类中心附近，整体故障识别率保持在100 ，因此，该方法能精确、稳定提取故障特征，为实际滚动轴承智能故障诊断提供参考。( In order to extract fault features of rolling bearing precisely and steadily, a method which is based on variational mode decomposition(VMD) and singular value decomposition was proposed for fault diagnosis using standard fuzzy C means clustering(FCM). First of all, the known fault signals measured in the same load but with different faults were decomposed by VMD, and the modes characteristics were further extracted using singular value decomposition technique,forming the standard clustering centers by FCM, and then the test samples were clustered by a Hamming nearness approach, and the classification performance was uated by calculating classification coefficient and average fuzzy entropy. At last, the method was applied in rolling bearing fault diagnosis under variable loads. By comparing with a method based on EMD, this approach is not sensitive to the initialization of standard FCM, and exhibits better classification performance in the same load fault diagnosis Fo)