anyCorrNoise

Fdesign matlab algorithm

很好的压缩感知的国外的书籍材料,对于我们的学习,尤其初学者很有帮助(ON划词翻译ON实时翻译 Very good material of compressed sensing of foreign books, for our learning, especially for beginners very helpful)

本程序是运用SVM来实现人脸识别算法，能直接运行且能进行二次开发(This procedure is to use SVM to implement face recognition algorithm that can run directly and can perform a secondary development)

200 THz electric field modulated by Gaussian envelope

根据输入的电路矩阵表示形式，形成节点导纳矩阵，形成因子表的过程可以看做是规格化运算和消去运算的重复过程，重复的次数和非基准点的节点个数相同。 每个过程中进行的操作都相同，首先，存储规格化因子及消去因子；其次，对本行进行规格化运算，并将其值存储在因子表中；然后，对其后的每行进行消去运算；如此循环n次得到因子表。(Representation of the input of the circuit matrix, forming a node admittance matrix formation process can be seen as a factor table normalization process operations and eliminate duplicate operations, the same number of nodes and the number of repetitions of non-reference point. Each operation carried out during the same, first of all, storage and elimination factor normalization factor secondly, the Bank normalized operations, and its value is stored in the factor table then, each row will be erased after their operation so the cycle n times to get factor table.)

double side band modulation code

说明：  利用冲击响应不变法和双线性不变法设计的巴特沃斯滤波器(Impulse response using the same method and design of the same bilinear Butterworth filter)

《MATLAB智能算法30个案例分析》（第二版）源代码，在第一版的基础上修改，适合高版本的matlab平台("MATLAB intelligent algorithm 30 case analysis" (the second version) source code, on the basis of the first version of the modification, suitable for high version of MATLAB platformv)

Monte Garlo 仿真程序，关于误码率与信噪比的对应关系，希望对大家学习有所帮助(Monte Garlo simulation program, on the bit error rate and signal to noise ratio of the corresponding relations and hopes to help them to learn)

全面分析机械类个企业的经济效益，以一家公司的盈利额为研究对象，选择了8个不同的利润指标，14企业关于这8个指标的统计数据如下表所示，试进行主成分分析。(Comprehensive analysis of the economic efficiency of enterprises machinery months to a company' s earnings amounted research object, select 8 different profit targets, 14 companies on these eight indicators of statistical data in the following table and try to principal component analysis.)