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在运动的位置叠加噪声。进行JPDA概率数据关联及kalman滤波。 两运动目标在x-y平面做匀速直线运动。初始位置是（4000,1200）（300,1500）速度分别是（200,200）（400,200）传感器对量目标进行位置状态量测。 采样间隔T=1,点数n=80.检测概率为1，正确量测落入跟踪内的概率为0.99，杂波均匀分布的密度为2个/km2由RAND函数产生在[0，1]上均匀分布的随机变量，跟踪门限为9.21。 (Superimposed noise in the position of the movement. JPDA probabilistic data association and kalman filtering. Two moving targets uniform linear motion in the xy plane. The initial position (4000,1200) (300,1500) speed (200,200) (400,200) position sensor on the amount of target state measurements. Sampling interval T = 1, points n = 80. Detection probability of correctly measured fall into the tracking probability 0.99, 2/km2 clutter uniform distribution of density generated by the RAND function [0,1] uniformly distributed random variables tracking threshold of 9.21.)

Program that checks the inventory level excels sheets

IIR滤波器设计，包含切比雪夫，巴特沃兹，椭圆等滤波器matlab代码。可直接运行。(IIR filter design, including Chebyshev, Butterworth, oval filter matlab code. Can run.)

Proper Orthogonal Decomposition of Gaussian blobs in an image sequence

实现维纳滤波用于语音增强。第一步采用频域谱函数计算最佳的维纳滤波器，再对输入带噪信号进行滤波；第二步是在此基础上的改进，用了自适应的思想，及时根据当前帧的状态更新修正滤波器的系统函数，得到更精确的无噪语音信号。第三步是采用自适应LMS方法，在时域迭代计算滤波器系数，迭代次数足够多时可得到较准确的有用信号。(Implement wiener filtering used in speech enhancement. First step optimum wiener filter is obtained by using the frequency spectrum function, again to input the signal with noise filtering The second step is the improvement on the basis of this, with the idea of adaptive, and in a timely manner according to the current frame updates correction system function of the filter, get a more accurate without noise speech signal. The third step is to use the adaptive LMS method, iterative calculation in time domain filter coefficient, the number of iterations enough to get the useful signal accurately.)

maven seetingmaven seetingmaven seetingmaven seeting(maven seetingmaven seetingmaven seetingmaven seetingmaven seetingmaven seeting)

Testing ECMA conformance of Function.prototype.apply.

ewtons.m to solve a set of nonlinear eqs f1(x)=0, f2(x)=0

系统辨识的经典之作，作者为matlab系统辨识工具箱的编写者。(System Identification classic, author matlab System Identification Toolbox writers.)

cjiajia调用MATLAB实例，源代码(c++ to call MATLAB examples, source code)