program

标量积算法反演雷达海浪图形的反演算法，可得到各种海流参数(Scalar wave radar graph algorithm inversion inversion algorithm parameters obtained various currents)

在心电信号中混入50HZ工频噪声，并设计自适应滤波器消除消除工频噪声。程序附有详细文字解释，可运行。(In the ECG signal mixed with 50HZ frequency noise, and design of adaptive filter to eliminate the elimination of power frequency noise. The program is accompanied by a detailed text explanation, can run.)

green project in matlab

Theory of PDH communication for programming in any language

在matlab环境下，实现加密解密算法中的DES算法的源代码，以及GUI界面的生成(In the matlab environment, the realization of encryption decryption algorithm of DES algorithm source code, and the generation of GUI interface)

基于SVD海杂波抑制算法 文件中包含源程序！(a clutter suppression scheme based on SVD including the original code!)

matlab code solutions for autopilot mav book assignmetns

若不希望用与估计输入信号矢量有关的相关矩阵来加快LMS算法的收敛速度，那么可用变步长方法来缩短其自适应收敛过程，其中一个主要的方法是归一化LMS算法（NLMS算法），变步长 的更新公式可写成 W（n+1）=w（n）+ e（n）x（n） =w（n）+ （3.1） 式中， = e（n）x（n）表示滤波权矢量迭代更新的调整量。为了达到快速收敛的目的，必须合适的选择变步长 的值，一个可能策略是尽可能多地减少瞬时平方误差，即用瞬时平方误差作为均方误差的MSE简单估计，这也是LMS算法的基本思想。 (Want to estimate if the input signal vector and the relevant matrix to speed up the convergence rate of LMS algorithm, then the variable step size method can be used to shorten its adaptive convergence process, one of the main method is normalized LMS algorithm (NLMS algorithm) , variable step-size update formula can be written W (n+ 1) = w (n)+ e (n) x (n) = w (n)+ (3.1) where, = e (n) x (n) the right to express filter update vector iterative adjust the volume. In order to achieve the purpose of fast convergence, we must choose the appropriate value of variable step size, a possible strategy is as much as possible to reduce the instantaneous squared error, which uses the instantaneous squared error as the mean square error MSE of the simple estimate, which is the basic LMS algorithm思想.)

多径多普勒效应讲义(含matlab程序)(Multi-path Doppler effect notes (with matlab program))

用于对全景镜头进行标定，能求解出图像上对应点的世界坐标的方向向量(The OcamCalib Toolbox for Matlab allows the user (also inexpert users) to calibrate any central omnidirectional camera, that is, any panoramic camera having a single effective viewpoint (see section 17). The Toolbox implements the procedure initially described in the paper(Scaramuzza, D., Martinelli, A. and Siegwart, R., (2006). "A Flexible Technique for Accurate Omnidirectional Camera Calibration and Structure from Motion", Proceedings of IEEE International Conference of Vision Systems (ICVS 06), New York, January 5-7, 2006.) and later extended in (Scaramuzza, D., Martinelli, A. and Siegwart, R., (2006). "A Toolbox for Easy Calibrating Omnidirectional Cameras", Proceedings to IEEE International Conference on Intelligent Robots and Systems (IROS 2006), Beijing China, October 7-15, 2006. ). A detailed introduction to this model is in section 19 of this Tutorial. Furthermore, you can also see a demo of how the toolbox works here. The Toolbox permits the user to easily and quickly cal)