weifengxianxing

this is can apply to run simulink

在无数点中，寻找一条直线，在高书中用的是两点确定一个直线，而在此用的是对比两个点，然后求平均值，以此类推(In many points, to find a straight line, is used in the two high book to determine a straight line, and in this contrast with the two points, and then averaged, and so on )

一种基于蚁群算法的图像边缘检测算法，有中文注释。(Image edge detection algorithm based on ant colony algorithm)

an article about a model of dvr

基于matlab的有限元法求解泊松方程，WORD的形式，里面附有源程序(Matlab-based finite element method for solving Poisson s equation)

MATLAB通信仿真及应用实例详解好的一本书，里面有实例和M文件代码。(MATLAB simulation and communication application example explanation good book, there are examples and M file code.)

使用matlab对心电信号进行处理，使用离散余弦变换(ECG signal using matlab processing, the use of discrete cosine transform)

21种特殊函数的数值计算的matlab代码，包括bessel函数，gamma函数，beta函数等(21 kinds of special functions matlab numerical code, including bessel function, gamma function, beta function, etc.)

基于蒙特卡洛方法的主动声纳信号检测性能分析.主动声纳信号检测性能的分析上,目前在计算机仿真中一般假定混响包络的统计特性符合瑞利分布模型。基于此模型,已经有了较完善的理论。然而,在现代高分辨声纳系统中,混响包络的统计特性并不符合瑞利分布模型。此时在接收机工作特性分析时存在大量繁琐的公式推导。因此该文采用蒙特卡洛(M onte Carlo)统计试验方法,实现对瑞利分布混响背景下的主动声纳信号检测性能分析。结合对接收机工作特性曲线的仿真,得出了检测概率的理论值和仿真结果的误差曲线。误差曲线表明,蒙特卡洛方法在主动声纳信号检测的性能评估中是可行的。 更多还原 (Active sonar signal detection performance analysis, computer simulation is generally assumed that the statistical properties of the reverberation envelope distribution model of Rayleigh active sonar signal detection performance analysis based on the Monte Carlo method. Based on this model, has been a better theory. However, in the modern high-resolution sonar system, the statistical characteristics of the reverberation envelope does not comply with the Rayleigh distribution model. At this point in the receiver operating characteristic analysis, there is a lot of tedious formula derivation. In this paper, using the Monte Carlo (M onte Carlo) statistical test methods, active sonar signal detection performance analysis reverberation background Rayleigh distribution. The receiver operating characteristic curve simulation obtained the error curve of the theoretical value and the simulation results of the detection probability. The error curve shows that the Monte Carlo method in the assessm)

image classification using SVM