section4_mutual_information

图像配准常常是作为其他图像处理应用的前处理步骤使用的，往往用于图像的对准、目标识别与定位。介 绍了在MATLAB系统中，如何应用MATLAB的IPT函数对图像进行配准的方法：首先用人机交互的方法在待配 准图像与基准图像之间进行图像配准所必需的匹配控制点的选取，然后用这些匹配控制点来计算两图像的某种空 间几何变换关系，最后利用这个空间几何变换关系对待配准图像进行几何变换，获得配准结果。实验表明，该方 法实现容易，且非常有效。 (Image registration is often as other image processing applications, the use of pre-treatment steps are often used for image alignment, target identification and location. Introduced the system in MATLAB, how to use MATLAB function of the IPT image registration method: First of all, employing the method of cross-machine in question and the baseline image registration between image image registration by matching control points necessary for the selection, and then use these control points to match the two images to calculate the geometric transform the relationship between a certain space, and finally make use of the space relationship between the treatment of geometric transformations with quasi-geometric image transformation, the results obtained registration. Experiments show that the method easy and very effective.)

利用MATLAB GUI设计实现图像处理的图形用户界面，利用MATLAB图像处理工具箱实现以下的图像处理功能： 1. 采用matlab Guide设计实现图像界面与按钮界面。 2. 连接摄像头，编写MATLAB初始化程序 3. 用摄像头采集一副图像，并能实现保存（使用控件按钮保存）。 4. 设计按钮实现对图像进行任意的亮度和对比度变化调整，显示和对比度变化前后的图像。 5. 设计按钮实现用户能够用鼠标选取图像感兴趣区域，显示和保存该选择区域。 (Designed and implemented using MATLAB GUI graphical user interface, image processing, using MATLAB image processing toolbox of image processing functions to achieve the following: 1. Designed to achieve an image using matlab Guide button interface with the interface. 2 Connect the camera to write MATLAB initialization procedure 3, with the camera capture an image and save to achieve (using the control button to save). 4 Design button on the image to achieve any change in the brightness and contrast adjustment, display, and contrast changes before and after images. 5 button design to achieve the user to select an image region of interest with the mouse, display and save the selected area.)

这个主要是3D图像显示的一个matlab程序(matlab code on 3D plot )

基于Hilbert包络解调及倒谱分析法，在LabVIEW平台开发的齿轮箱故障诊断程序(Based on Hilbert envelope demodulation and cepstrum analysis, the gearbox fault diagnosis program developed on the LabVIEW platform)

说明：  基音检测,检测出语音的基音频率，采用了小波和经典方法(base tone)

matlab实现，验证奈奎斯特采样定理，对信号求傅里叶变换的数值解，数值求解，画出时域图，频域图(matlab implementation, verification Nyquist sampling theorem, Fourier transform of the signal find the numerical solution, numerical solution, draw the graph in time domain, frequency domain graph)

MIT--航空航天工程中的计算方法_有限元方法实例——涡轮叶片传热 MIT公开课程--航空航天工程中的计算方法 这门课程适用于在航空和航天工程中出现的计算方法的入门学习。其应用是从航空宇宙结构，空气动力学，动力学和控制，和航空宇宙系统选出的。其技术包括：常微分方程系统的数值积分；偏微分方程式的有限差分,有限体积, 和有限元的离散化；数值线性代数；本征值问题和带有约束的最优化。 (MIT- Aerospace Engineering in the finite element method calculation _ instance- turbine blade heat transfer MIT Open Course- Aerospace engineering calculation methods for this course in aviation and aerospace engineering in the calculation of the entry appears to learn. The application is from aerospace structures, aerodynamics, dynamics and control, and aerospace systems selected. The technologies include: numerical integration of ordinary differential equations systems partial differential equations of finite difference, finite volume, and finite element discretization numerical linear algebra eigenvalue problems with constraints and optimization.)

进行频域处理的必备程序，学信号的人一定要顶下(Essential for frequency domain processing procedures, the person must learn the signal under the top)

基于1范数的稀疏信号反卷积算法的程序代码，语音信号、迭代算法(The sparse signal deconvolution algorithm based on 1 norm of program code, speech signal, an iterative algorithm)

说明：  相关向量机（Relevance Vector Machine，简称RVM）是Micnacl E.Tipping于2000年提出的一种与SVM（Support Vector Machine）类似的稀疏概率模型，是一种新的监督学习方法。 它的训练是在贝叶斯框架下进行的，在先验参数的结构下基于主动相关决策理论（automatic relevance determination，简称ARD）来移除不相关的点，从而获得稀疏化的模型。在样本数据的迭代学习过程中，大部分参数的后验分布趋于零，与预测值无关，那些非零参数对应的点被称作相关向量（Relevance Vectors），体现了数据中最核心的特征。同支持向量机相比，相关向量机最大的优点就是极大地减少了核函数的计算量，并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method. Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)