心电信号QRS波检测

描述可视化数字编码的matlab程序，在matlab上调试通过(Describe visualization digital encoding matlab program, matlab debugging through)

image fcm segmentation

GPS信号捕获的源程序设计，提供的信号捕获的原始算法实现(GPS signal acquisition of the source design, providing a signal acquisition of the original algorithm)

说明：  一个简化的simulink中crowbar电路的搭建(Construction of crowbar circuit in a simplified simulink)

应用背景直线通常对应着重要的边缘信息，直线提取是计算机视觉中一项非常关键重要的技术，因此把直线单独抽取出来研究有重要的意义，其提取方法与一般的边缘检测方法不同。关键技术hough变换是对二值图像进行直线检测的有效方法，其实质是对图像进行坐标变换，将图像空间的点映射到参数空间，使变换的结果便于检测和识别。

matlab实例分析一个Matlab实现的实分析程序(real analysis)

本算法在Matlab2008b 环境下实现。包括main,san 和ssd 三个函数。 这次实现的算法并不是比较两个已经知道的点是否匹配，而是已知一个图形中的特征点，在另外一个图像中找到与其最匹配的点，匹配度用SAD 或者SSD 来度量。 main.m 是程序的入口，包括生成和读入实验数据，调用sad 函数和ssd 函数求匹配点，最后绘 制出最后的结果。sad.m 是用sad 度量方法在另一图中求解匹配点的函数实现，有3 个参数 y=sad(x,image1,image2)。其中x 是代匹配的数据，image1 是图像1 数据，image2 是图像2 数据。 从理论上分析，ssd 比sad 算法要复杂一点，经过测试，在一幅640*480 的图像中寻找10 个匹配点数据SAD 用时25.062519 秒，SSD 用时25.291432 秒。(The algorithm Matlab2008b environment to achieve. Including the main, san, and ssd three functions. The implementation of the algorithm is not the point of comparing two matches already know, but the known feature points in a graphic, an image found in the other match with the most points, matching measured with the SAD or SSD. main.m is the entry, including the generation and reading test data, call the sad ssd function evaluation functions and matching points, and finally draw the final result. sad.m measure is sad figure in another match point in the function implementation to solve, there are three parameters y = sad (x, image1, image2). Where x is the generation of matching data, image1 is the data image 1, image2 is the image 2 data. From the theoretical analysis, ssd little more complicated than the sad algorithm, tested in a 640* 480 images of 10 match points in the search for data using time 25.062519 seconds SAD, SSD with time 25.291432 seconds. )

说明：  harrisi 算法，用此算法可以快速在图像中寻找到角点，快速 准确(harrisi algorithm)

jpeg图像压缩和解压缩过程，使用的哈弗曼表包含于程序中，内有详细的文件说明(jpeg image compression and decompression process, the use of Huffman tables included in the program, with detailed documentation)

该代码是根据基于流行正则化和后验概率的分类模型而改写的，能够对复杂数据进行准确的分类。(The code is based on the popular regularization and posterior probability classification model and rewritten, able to accurately classify complex data.)