bucknew

说明：  根据脉冲序列，使用Hankel矩阵法求系统脉冲响应传递函数(According to pulse sequence, the use of Hankel matrix method for system impulse response transfer function)

OS-CFAR的Matalb实现，并且可以根据虚警率的不同绘制ROC曲线，是学习恒虚警检测器的好材料(The Matalb implementation of OS-CFAR, and can draw the ROC curve according to the false alarm rate, is a good material for learning CFAR detector)

matlab实现画出阵列天线的波束图，通过仿真可以观察主瓣指向、阵元数、波长对波束图的影响，帮您更好地理解方向图的概念及其影响因素。附报告、源程序、仿真结果截图，内容详尽(matlab implementation array antenna beam pattern drawn by the main lobe pointing simulation can be observed, the number of array elements, wavelength beam pattern effects, to help you better understand the concept of pattern and its influencing factors. Attached to the report, the source, the simulation results screenshots, detailed)

matlab 入门教程 PPT matlab 入门教程 PPT matlab 入门教程 PPT(PPT matlab matlab Tutorial Tutorial Tutorial PPT PPT matlab)

高斯平滑滤波器(含matlab代码),可供图像初步处理持用，已验证效果。(Gaussian smoothing filter (with matlab code), available image processing holders of preliminary, verified results.)

说明：  磁共振成像仪上的梯度回波序列，我用matlab把时序图画出来了，供大家参考，谢谢(Magnetic resonance imaging gradient echo sequence on, I use matlab to timing out the picture for your reference, thank you)

说明：  卡尔曼滤波和自适应滤波 y(Kalman filtering and adaptive filtering y)

一个关于线程池的小demo，windows环境下，可以直接运行。( A little on the thread pool demo, windows environment, can be directly run.)

经典的Gabor滤波器的源码，Matlab实现(Gabor Filter code in Matlab)

％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％( EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates)