MATLAB

加速度计原始数据卡尔曼滤波,对加计原始数据的卡尔曼滤波matlab程序(Kalman filtering raw accelerometer data, plus the raw data for the Kalman filter matlab program)

QPSK调制下的信道信噪比的分析与研究，里面的仿真不错，很详细。(QPSK modulation of channel signal to noise ratio analysis and research, which the simulation well, very detailed.)

Normalized Cut Image Segmentation Code (Normalized Cut Image Segmentation Code)

A Complete Modeling and Simulation of Induction Generator Wind Power Systems

有关matlab 软件学习的书籍(Matlab software to learn about books)

针对高精度的畸变图像,提出了一种基于最小二乘影像匹配的高精度畸变图像矫正算 法. 算法首先利用特征提取与边缘检测对图像进行预处理,并且将特征匹配与最小二乘算法相 结合,从而实现了图像与模板之间精确的子像素定位与匹配. 实验表明,该算法较好的解决了目前高精度畸变图像矫正算法中普遍存在的定位和匹配精度较差的缺陷,图像矫正效果良好,是一种有效的畸变图像矫正算法.(Image distortion for high-precision, a least-squares image matching based on high-precision image distortion correction algorithm. First of all, the use of feature extraction algorithm for edge detection and image pre-processing, and will feature matching with a combination of least-squares algorithm in order to achieve the image and the template between the sub-pixel accurate positioning and matching. The experimental results show that the algorithm has better accuracy of the solution to the current distortion prevalent image correction algorithm to match the accuracy of the positioning and less defects, image correction effect well, is an effective algorithm to correct image distortion.)

说明：  压缩感知 正交匹配追踪一些人关心压缩感知与雷达成像，他们把稀疏表示放在最重要的地方，以为在雷达成像中成功实现压缩感知关键是稀疏表示； 事实上并不是如此。我们知道：压缩感知需要建立AX=B,且该方法具有较低的抑制信噪比能力；另外雷达成像的基础是雷达 信号与目标的相互作用，也就是电磁波与介质的相互作用，该相互作用是一个非常复杂的非线性问题，因此研究这个问题与 压缩感知的关系才是解决雷达成像问题的关键点所在。从另外一个角度来看，雷达成像中惯用的方法是匹配滤波，它之所以 能够处理低信噪比的问题，是因为它利用了回波数据的冗余信息。也就是目前雷达成像算法之所以成功的关键是具有足够 多的冗余信息。现在，在雷达成像中使用压缩感知恰好是反其道而行之！ (compressed sensing)

《matlab实例》中有关图像处理的实例源码，希望对大家有帮助。(" Matlab examples" in the instance of the image processing source code, we want to help.)

自己编的EMODEL 的matlab程序，希望对大家有帮助!多指正!谢谢!(Own EMODEL of the matlab program, and they hope to help you! More correction! Thank you!)

说明：  高通滤波器，带通滤波器，数字FIR滤波器(The Shannon entropy is a measure of the average information content one is missing when one does not know the value of the random variable. The concept was introduced by Claude E. Shannon in his 1948 paper "A Mathematical Theory of Communication". Shannon s entropy represents an absolute limit on the best possible lossless compression of any communication, under certain constraints: treating messages to be encoded as a sequence of independent and identically-distributed random variables, Shannon s source coding theorem shows that, in the limit, the average length of the shortest possible representation to encode the messages in a given alphabet is their entropy divided by the logarithm of the number of symbols in the target alphabet. )