embedVideo2Video

说明：  用MATLAB实现雷达回波信号的仿真(Using MATLAB realize radar signal simulation)

content based image retrievel using color histograms in the hsv space

捷联惯导系统初始对准几种不同滤波方法对比matlab程序(SINS initial alignment of several different filtering matlab program)

噪声中正弦信号的现代法频谱分析，自己写的matlab程序一份，包括现代法谱估计中AR模型的自相关法，Burg法，协方差法，改进的协方差法四种估计算法(Modern methods of spectrum analysis)

说明：  如果对实际工程结构作简化处理，建立其动力学模型，并确定系统的质量、刚度、阻尼参数，便可以用多种方法建立系统的动力微分方程。常用的方法有直接 法、影响系数法、拉格朗日法以及有限元法等。拉格朗日法是从能量的观点建立系统的动能T、势能U和功W之间的标量关系，是研究静、动力学问题的一种方法。如果在选定运动坐标以后，能够设法求得坐标相对应的质量矩阵和刚度矩阵，就可以用影响系数法建立多自由度系统运动微分方程[4]。本文采用拉格朗日法建立了1/4车辆几何模型。(If the actual engineering structure is simplified, its dynamic model is established, and the mass, stiffness and damping parameters of the system are determined, the dynamic differential equation of the system can be established by various methods. The commonly used methods are direct method, influence coefficient method, Lagrange method and finite element method. Lagrange method is a method to study the static and dynamic problems, which is to establish the scalar relation among kinetic energy t, potential energy u and work W from the point of view of energy. If the mass matrix and stiffness matrix corresponding to the coordinates can be obtained after the motion coordinates are selected, the differential equation of motion of the multi degree of freedom system can be established by the influence coefficient method [4]. In this paper, 1 / 4 vehicle geometry model is established by Lagrange method.)

基于学习的图像超分辨率重建，可以进一步了解基于学习的方法。(Learning-based super-resolution image reconstruction, we can further understand the learning-based approach.)

使用MATLAB仿真ask信号，并画出眼图，使用窗函数对数据进行功率谱计算，并画图(Use the data window function power spectrum calculation, and drawing)

matlab的基础教学，对于从来没有学习过matlab的同学，该文档可以提供由浅入深的详细介绍与说明。(matlab basis for teaching, never studied matlab for students, the document provides a detailed description and explanation progressive approach.)

matlab code for adding and splitting frame to the video

nice matlalc code for browsing through an array of related plots