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说明：  smith预估控制算法的ＭＡＴＬＡＢ程序实现(smith predictor control algorithm MATLAB program realization)

用于matlab 的学习，前五章内容为基本介绍，包括软件安装与卸载，数据类型，数组与matalab常用功能(For the matlab learning, basic introduction to the first five chapters content, including software installation and uninstallation, data types, arrays with common functions matalab)

Quantization source code

MACHINE SYNCHRONE A AIMANT PERMANANT ALIMENTE CONNECTEE A UNE EOLIENNE

Design and Implementation of protocols related to Radio Network Layer over eNB in 3GPP LTE System

地球坐标系ecef坐标与地理坐标系(bj54/wgs84)转换。(Ecef coordinate system and geographic coordinates (bj54/wgs84) conversion.)

matlab的图像处理。图像编码（HUFFMAN编码）小波变换，1、 分别用sobel、Laplacian-Gaussian方法对一幅灰度图像进行边缘提取(Matlab image processing. Image Coding (HUFFMAN coding) wavelet transform, 1, respectively segmentation, Laplacian- Gaussian approach to a gray-scale image from the edge)

test2: 一、 基本最小二乘法一次算法 二、 基本最小二乘法递推算法 三、 最小二乘遗忘因子一次完成算法 四、 最小二乘遗忘因子递推算法 五、 最小二乘限定记忆算法 六、 最小二乘偏差补偿算法 七、 增广最小二乘算法 八、 广义最小二乘算法 test3: 一、 辅助变量自适应滤波算法 二、 辅助变量纯滞后算法 三、 辅助变量Tally原理算法 四、 多级最小二乘算法 五、 各类改进最小二乘算法的特点 test4: 1、 第二类随机性辨识问题的梯度校正 2、 随机牛顿法 test5: 1、 递推的极大似然估计 2、 预报误差参数辨识 test6: 1、 根据Hankel矩阵秩估计模型阶次(弱噪声) 2、 根据Hankel矩阵秩估计模型阶次(强噪声) 3、 利用行列式比估计模型阶次（白噪声） 4、 利用行列式比估计模型阶次（有色噪声） 5、 利用残差的方差估计模型阶次（白噪声） 6、 利用残差的方差估计模型阶次（有色噪声） 7、 AIV定阶法（白噪声） 8、 AIV定阶法（有色噪声） test7: 1、 一阶惯性+纯滞后环节-----两点法 2、 面积法1 3、 levy法 (test2: First, the basic method of least squares algorithm once Second, the basic recursive least squares algorithm Third, once the forgetting factor least squares algorithm Fourth, the forgetting factor recursive least squares algorithm Fifth, the least-squares algorithm limited memory Six , least squares error compensation algorithm Seven , extended least squares algorithm Eight , generalized least squares algorithm test3: An auxiliary variable adaptive filtering algorithm Second, the auxiliary variable time delay algorithm Third, the principle of auxiliary variable algorithm Tally Fourth, multi-stage least squares algorithm Fifth, the least squares algorithm to improve the characteristics of various types of test4: 1, the second class of stochastic gradient identification problem corrected 2 , randomized Newton test5: 1, the recursive maximum likelihood estimation 2, the prediction error parameter identification test6: 1 , according to the Hankel matrix rank est)

实现基础RD成像算法，3个点目标仿真成像。(Basis for achieving RD imaging algorithm, three-point target simulation imaging.)

第一步是计算输人信号单边功率谱密度（ESD）。使用快 ％速傅里叶（FFr）算法将信号从时域转换到频域。因为FFr算法的输出是离散谱，而这 ％里我们需要的是连续谱，因此需要引人不同的比例因子来实现从离散谱到连续谱的转换。 ％在第二步中，我们利用迭代算法计算出相对于特定阂值的ESD的最高和最低频率，从而 ％估算出被检测信号所占用的带宽。这种算法同时适用于基带信号和已调制信号。最后， ％在第三步中，我们给出了输出图形的原代码。(The first step is calculating the input signal unilateral power spectral density (ESD). The use of fast Fourier speed (FFr) algorithm for the signal conversion from time domain to frequency domain. FFR algorithm because the output is a discrete spectrum, which we need is a continuous spectrum, so the need for food of different scale factor to achieve from the discrete spectrum to continuous spectrum conversion. In the second step, we use iterative algorithm to calculate a specific threshold in relation to the ESD of the highest and lowest frequency, thus estimated to be occupied by the signal detection bandwidth. This algorithm applies to both baseband signal and modulation signals. Finally, in the third step, we give the graphical output of the original code.)