Evaluate

矩阵转换四元数代码 欧拉角转换四元数代码 四元数转换到矩阵代码(The quaternion matrix transformation code Euler angles conversion quaternion code converts quaternion to a matrix code)

《Matlab应用大全》书本上的程序，有初级到高级，包含了信息处理，数字图像处理，模糊算法，小波变换等(Program on the books " Matlab application Daquan" There beginner to advanced, including information processing, digital image processing, fuzzy algorithms, wavelet transform)

说明：  在matlab中产生心电信号的程序。很实用(ECG signals generated in the matlab program. Very practical)

该文档是《粒子滤波原理及应用MATLAB仿真》一书的配套例程，对于学习粒子滤波算法的MATLAB实现很有帮助(This document is a companion routine to the book Particle Filtering Theory and Application of MATLAB Simulation. It is helpful for learning MATLAB implementation of particle filter algorithm.)

说明：  任务 1 编写以两幅灰度图像为输入的 MATLAB 函数，计算下列图像差值度量：均方误 差（MSE）信噪比（SNR）峰值信噪比（PSNR）均值绝对误差（MAE）处理给定的测试图像“Lena”及其原始和后添加噪声形式。 任务 2 编写 MATLAB 函数，通过两种不同的方法将一幅灰度图像从原始大小下采样到 给定输出大小：a)直接消除像素，b)在消除像素之前进行初步图像平滑处理。用缩放系数 2:1、4:1、8:1 和 16:1 对给定的测试图像“Living.”进行处理。 任务 3 编写 MATLAB 函数，通过两种不同的方法将灰度图像从原始大小缩放到给定的 输出大小：a)像素重复使用上述方法将任务 2 中处理的图像缩放回原始大小。使用为任务 1 编写的 MATLAB 方法计算原始图像和缩放图像的 MSE 和 PSNR。将 MSE和 PSNR 分别作为大小缩减率的函数。定义对于相同的尺寸缩小比，收缩/缩放方法的组合提供最小 MSE 和最大 PSNR。 任务 5 编写 MATLAB 函数分别对测试图像“Fig0316(1)(top_.)”和“Fig0316(4)(._.)”执行直方图均衡。显示直方图均衡前后直方图的直方图。(task1 task2 task3 task5)

FIchier ode4.m et intégration d équations de robotiques

用matlab实现在midi音频中的隐写过程(Matlab achieve midi audio steganography)

电力系统典型经济调度问题，采用遗传算法进行求解，具有很好的研究价值。(The typical economic dispatch problem of power system is solved by genetic algorithm, which has good research value.)

ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option). It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier

This is the Purdue University EE 606 Homework. I uploaded it and hope friends could enjoy it together. Thanks very much