dataanalysis

BPSK的Matlab简单仿真，得到理想情况下的误码曲线(BPSK仿真之matlab)

sofware witch is very important in with omnetpp and views networks architectures

说明：  改进遗传算法，克服遗传算法的缺点，有一定的参考价值。(The disadvantage improved genetic algorithm to overcome the genetic algorithm, a certain reference value.)

用charal函数实现对加噪降晰图像的反降晰.charal函数实现约束最小二乘法的算法，其中alpha（1/lamda）由Charalambous确定.反降晰的图像相对于降晰加噪图像清晰度有所提升，但是反降晰的图像存在较为明显的寄生波纹。(Deblurring the blurred image with noise by using Charal function. Charal function implements constrained least squares algorithm, where alpha (1/lamda) is determined by Charalambous. Compared to the blurred image, the sharpness of the image deblurred has improved, but obvious parasitic ripples arise.)

边缘是图像最基本的特征，是图像分割的第一步。经典的边缘检测方法如：Roberts，Sobel，Prewitt，Kirsch，Laplace等方法，基本都是对原始图像中象素的小邻域构造边缘检测算子，进行一阶微分或二阶微分运算，求得梯度最大值或二阶导数的过零点，最后选取适当的阀值提取边界。由于这些算法涉及梯度的运算，因此均存在对噪声敏感、计算量大等缺点。在实践中，发现SUSAN算法只基于对周边象素的灰度比较，完全不涉及梯度的运算，因此其抗噪声能力很强，运算量也比较小。并将SUSAN算法用于多类图像的边缘检测中，实验证明该算法非常适合含噪图像的边缘检测。 2 SUSAN边缘检测简介 (err)

有限元方法中的使用一次线性元在矩形区域上求解laplace方程的数值代码例子(Finite element method, the use of a linear finite element numerical code examples for solving laplace equation on a rectangular area)

数据频域分解法，基于结构的环境白噪声激励响应数据，识别结构的频率和振型(The frequency domain decomposition method, based on the structure of the environmental white noise stimulus-response data, identify the structure of the frequencies and mode shapes)

this code is about Generalize Side Lobe Canceller and very important in array processing.

这是基于matlab的数字信号处理的实验。里面有详细的要求和程序解答，适合初学者。(This is based on digital signal processing matlab experiments. There are detailed requirements and procedures to answer, for beginners.)

包含有详细的混沌理论优化算法，可以用于控制器参数的寻优(Chaos Theory contains a detailed optimization algorithm, can be used for optimization of controller parameters)