gmskmatlab_compress

某家电企业在全国二线城市的年销售量Y(单位：万台)与该城市人口数X1(单位:万人)以及该市人均月收入X2(单位:元)之间的关系，在某年对15个城市作了调查，得上述各量的观测值（见数据表data1.txt）(A household electrical appliance enterprises in the country in sales of second-tier cities, Y (unit: million units) and the urban population, the number of X1 (unit: million) and the city' s per capita monthly income X2 (unit: yuan) between, in a particular year made a survey of 15 cities, was the amount of the above observations (see data sheet data1.txt))

分布傅立叶法模拟薛定谔方程，用于非线性薛定谔方程的数值模拟。(Split Step Fourier Method)

convex optimizationconvex optimization algo new mit dimtri bertskas lesson 1

信道估计中两种经典算法的对比，MMSE和SVD-MMSE，上传的为验证过的函数程序，自行补充(Channel estimation in two classic algorithms in contrast, the MMSE and SVD-the MMSE, upload function for proven program to complement their own)

说明：  用Jacobi和Gauss-seidel方法解方程组。并比较迭代次数，是误差小于10E-3(Use of Jacobi and Gauss-seidel method to solve equations. And compare the number of iterations is less than 10E-3 error)

an api used for diffrent things

Compressive sampling is an emerging technique that promises to effectively recover a sparse signal from far fewer measurements than its dimension. The compressive sampling theory assures almost an exact recovery of a sparse signal if the signal is sensed randomly where the number of the measurements taken is proportional to the sparsity level and a log factor of the signal dimension. Encouraged by this emerging technique, this thesis briefly reviews the application of Compressive sampling in speech processing. It comprises the basic study of two necessary condition of compressive sensing theory: sparsity and incoherence. In this thesis, various sparsity domain and sensing matrix for speech signal and different pairs that satisfy incoherence condition has been compiled.

基于相位的模板匹配算法，此算法快速，可行性较好~(Template matching algorithm based on phase, this algorithm is fast, the feasibility of better ~)

人月神话，又是一本软件工程领域的经典书籍，必读。。。。(MythManMonth.rarMythManMonth.rar)

多被动传感器UKF与EKF算法在目标跟踪的的应用，扩展卡尔曼滤波(More passive sensor UKF and EKF algorithm in target tracking applications)