spread-spectrum-correlation-properties-simulation

The new meshless method based on the cover least square approximation, which utilize cover instead of points that are used in the moving least square approximation, is proposed. For the cover distribution, multi scale voxel data is used for the ease of analysis in 3D solid. Several 3D examples are shown for demonstration. The method is applied to adaptive analysis of seepage flow problem of rock including complicated cracks. The rock model with 3 cracks and 1000 cracks are analyzed

Creates a Gaussian mixture model with specified architecture.MIX = GMM(DIM, NCENTRES, COVARTYPE) takes the dimension of the space DIM, the number of centres in the mixture model and the type of the mixture model, and returns a data structure MIX.

本算法是一种变步长的LMS算法，可以适用于智能算法等相关技术中(this algorithm is about a variable step-size LMS adaptive filter,and can be implied in samrt antenna)

利用matlab来进行画门函数的频谱图和能量谱密度图(Use matlab to draw gate function spectrum and energy spectrum density map)

Implementation of Leach Protocol using MATLAB

说明：  很多分割算法的例子 图像处理的同志大家了一下再看啊看你(Many examples of image processing segmentation comrades we had to see what you look at ah)

使用MATLAB程序编写的压缩感知的几种重构算法，是调试完整的几个比较好的程序，包括OMP算法，IAjSvZ程序，CoSaMP，NpgCHMa构造等，包含了现在各种构造函数。 (Using MATLAB programming several reconstruction algorithm compression perceived, is commissioning complete a few good programs, including OMP algorithm, IAjSvZ program, CoSaMP,NpgCHMa structure, etc., and now includes a variety of constructors. )

This function takes a quantized analog signal and calculating the signal to quantization noise ratio (SNR)

thanx u gav me a nice option to upload and take models

说明：  径向基函数神经网络的优点：逼近能力，分类能力和学习速度等方面都优于BP神经网络，结构简单、训练简洁、学习收敛速度快、能够逼近任意非线性函数，克服局部极小值问题。原因在于其参数初始化具有一定的方法，并非随机初始化。代码为RBF的matlab程序，可以使用但是需要自己配置数据。(Radial Basis Function Neural Network (RBF-NN) has the advantages of better approximation ability, classification ability and learning speed than BP Neural Network (BP Neural Network). It has simple structure, simple training, fast learning convergence, and can approximate any non-linear function to overcome the local minimum problem. The reason is that its parameter initialization has a certain method, not random initialization.)