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配合刚刚上传的TOOLBOX_calib,是进行标定的标定图(With just uploaded TOOLBOX_calib, calibration is the calibration graph)

This is code to calculate Kullback-Leibler (KL) distance between histograms. It can apply for 2D image

说明：  ex9_1 脉冲信号调制 ex9_2 载波10MHz，带宽2MHz的线性调频信号及其频谱图 ex9_3 产生7位巴克码编码的二相码 ex9_4 产生7位巴克码和线性调频的混合调制信号 ex9_5 瑞利分布实现程序 ex9_6 瑞利分布＋杂波 ex9_7 相关对数正态分布杂波 ex9_8 相关weibull分布杂波 ex9_9 相干相关K分布杂波 ex9_10 线性调频信号的数字化正交解调 ex9_11 雷达脉冲压缩处理 ex9_12 二相编码信号的脉压处理 ex9_13 用FFT和FIR方法实现MTD处理(程序有错误) ex9_14 雷达的恒虚警处理（CFAR） ex9_15 比较相参积累和非相参积累 ex9_14 计算加性高斯白噪声准则下雷达探测的经典CFAR 二进制检测阈值和指定的虚警概率。 (瑞利噪声下快慢门限CFAR处理)(Ex9_1 pulse signal modulates ex9_2 carrier 10MHz, Linear Frequency Modulation signal of bandwidth 2MHz and its Spectrum Diagram ex9_14 calculation of classical CFAR under additive Gaussian white noise criterion Binary detection threshold and specified false alarm probability. (fast and slow threshold CFAR processing under Riley noise))

利用matlab計算t2管制圖phase1的管制上限及管制下限(Calculated using matlab control t2 control chart phase1 upper and lower control)

Matlab codes for the book: Higham Higham 2005 Matlab Guide 2ed

通过matlab程序实现的，基于块匹配的运动估计，预测图像(By matlab program, the block-matching based motion estimation, prediction image)

求解线性不定方程组的一个基解（未知数个数大于方程个数），代码简单有效，可以利用该代码求出所有基解，从而得到不定方程组解得一般形式(A radical solution for solving linear equations uncertain (unknown number greater than the number of equations), the code is simple and effective, you can use the code to all base solution obtained, resulting in variable equations have the general form of the solution)

含有光伏发电机和风机的微网仿真模型，用PSCAD搭建，适用于小型仿真(Micro grid simulation model with photovoltaic generator and fan, set up with PSCAD, applicable to small simulation)

无线传感器网络协议的生命周期和稳定周期对比(Wireless sensor network protocol and stability of periodic comparative life cycle)

说明：  本文从理论上推导出了 如何使用迭代方法解联立方程组, 在计算精度范围内严格地给出了泊松分布下的线性多 重回归的求解, 并且也解决了“ 面积亏损” 的问题(This paper theoretically out how to use the iterative method for solving simultaneous equations, in the context of strict accuracy of the Poisson distribution is given under the solution of linear multiple regression, and also solve the &quot loss area&quot problem)