hiperlan2

说明：  是通过结合遗传算法和模糊神经网络结合的检测(It is through the combination of genetic algorithm and fuzzy neural network)

说明：  一个包含多个matlab文件的工具包，利用它可以方便使用模糊c均值聚类(Matlab file contains more than one kit, using it can easily use the fuzzy c means clustering)

测试以下图像信息 1。结构内容（SC） 2。均方误差（MSE） 3。峰值信噪比（PSNR值） 4。归一化互相关（NCC） 5。平均差（AD） 6。最大的差异（MD） 7。归一化绝对误差（NAE）(Image/Picture Quality Measures In this application, different image quality measures are calculated for a distorted image with reference to an original image. To test the application, a set of 20 distorted images is included in this package. The list of Image Quality measures implemented in this package include, 1. Structural Content (SC) 2. Mean Square Error (MSE) 3. Peak Signal to Noise Ratio (PSNR in dB) 4. Normalized Cross-Correlation (NCC) 5. Average Difference (AD) 6. Maximum Difference (MD) 7. Normalized Absolute Error (NAE) Refer Reference.png for the mathematical expressions implemented in this package. Original images are kept in the folder OriginalImages . Distorted images are kept in the folder DistortedImages .)

Gauss-Elimination method! to solve multiple linear equations with ease

matlab部分实用的案例，适合初学者浏览(the case of some practical matlab for beginners visit)

通过AR模型模拟得到风荷载，其模拟是有davenport风速谱得到的(Wind load is obtained by AR model simulation, which is based on Davenport wind speed spectrum.)

这个pdf文件是关于压缩感知的最基础的入门代码，里面有详细的试用说明方法还有讲解(this pdf document presents some basic codes about compressed sensing,including the userguide and explannation)

Comminucation wireless

K最邻近密度估计技术是一种分类方法，不是聚类方法。 不是最优方法，实践中比较流行。 通俗但不一定易懂的规则是： 1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。 2.选出最小的前K数据个距离，这里用到选择排序法。 3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method. It is not the best method, but it is popular in practice. Popular but not necessarily understandable rule is: 1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov). 2. select the minimum distance from the previous K data, where the choice sorting method is used. 3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)

inertial navigation system1