Gauss_lengend_pi

一个详细介绍Simulink仿真过程的课件，对于刚开始接触Simulink仿真的朋友们很值得一看！(Simulink simulation of a detailed process of courseware, for the beginning of contact with friends Simulink simulation is worth a visit!)

自适应预测，可用于预测已知信号的未来时间序列(Adaptive prediction)

windowed linear-phase FIR digital

雷达信号的分选程序包括sdif，pri变换，cdif算法(Radar signal sorting procedures include sdif, pri transform, cdif algorithm)

Kernel_ELM传说中的核-极限学习机，基于官网给的版本，拆分成了训练和预测两个模块，方便大家得到训练模型和预测标签(Kernel_ELM, Extreme Learning Machine using Kernel，including two modules as train & predict)

希尔伯特变换理论及其在工程领域的应用，介绍了原理及其实现方法(hibert transform)

第一步是计算输人信号单边功率谱密度（ESD）。使用快 ％速傅里叶（FFr）算法将信号从时域转换到频域。因为FFr算法的输出是离散谱，而这 ％里我们需要的是连续谱，因此需要引人不同的比例因子来实现从离散谱到连续谱的转换。 ％在第二步中，我们利用迭代算法计算出相对于特定阂值的ESD的最高和最低频率，从而 ％估算出被检测信号所占用的带宽。这种算法同时适用于基带信号和已调制信号。最后， ％在第三步中，我们给出了输出图形的原代码。(The first step is calculating the input signal unilateral power spectral density (ESD). The use of fast Fourier speed (FFr) algorithm for the signal conversion from time domain to frequency domain. FFR algorithm because the output is a discrete spectrum, which we need is a continuous spectrum, so the need for food of different scale factor to achieve from the discrete spectrum to continuous spectrum conversion. In the second step, we use iterative algorithm to calculate a specific threshold in relation to the ESD of the highest and lowest frequency, thus estimated to be occupied by the signal detection bandwidth. This algorithm applies to both baseband signal and modulation signals. Finally, in the third step, we give the graphical output of the original code.)

基于mantlab编程实现学生成绩管理，包括学生信息的增加、删除、查询等，增强了容错处理，能很好地运行。(Based mantlab programming of student achievement, including student information to add, delete, query, etc., enhances the fault-tolerant and can run well.)

此文件夹包含了无线传感器网络中的几个经典模型leach,HEED, 分簇算法(This folder contains several classic models of wireless sensor network leach, HEED, clustering algorithm)

使用SVM中的1V1以及1VO算法实现对数据的分类以及matlab实现(Classification of data and implementation of matlab using 1V1 and 1VO algorithms in SVM)