MatlabSourceCode

三维逆合成孔径雷达成像算法研究，国外牛人写的，国内下不来，我们教研室托关系在国外下载的(An efficient 3-D near-field ISAR algorithm)

考虑雨衰 阴影 和多径影响，可直接计算得到多重分形谱,非归零型差分相位调制信号建模与仿真分析 。( Consider shadow rain attenuation and multipath effects It can be directly calculated multi-fractal spectrum, NRZ type differential phase modulation signal modeling and simulation analysis.)

灰度双线性插值，缩放图像。可以对图像进行几乎无损的小倍数放大，倍数大时有损。(Gray bilinear interpolation, zooming the image. Can be almost non-destructive image to enlarge small multiples, multiples of large detrimental.)

这MFC图像处理源代码总集包括了均值滤波，直方图均衡，高斯平滑，，锐化，拉普拉斯锐化（边缘检测），阈值变(With the MFC implementation of an image processing example code)

亲测，能实现模糊C均值聚类的MATLAB程序(MATLAB program that can realize fuzzy C mean clustering)

图像处理中压缩感知理论，对一维信号运用omp重构(Compressed sensing image processing theory, the use of one-dimensional signal omp reconstructed)

完成车道线检测，且运行效率高，附带图片测试(it has a high performance over lane detection, and leave pictures for testing)

Assembly code for interfacing a 7segment that display a random Letters.

说明：  同态滤波利用去除乘性噪声(multiplicative noise)，可以同时增加对比度以及标准化亮度，借此达到图像增强的目的。(Homomorphic filtering can increase contrast and standardize brightness simultaneously by removing multiplicative noise, so as to achieve the purpose of image enhancement.)

卡尔曼滤波源代码 Some brief notes ---------------- kfdemo.m is a Matlab script file to run a demonstration of the Bayesian Kalman filter. It loads file kfdemo.mat (saved as version 4 so that it will read in either v4 or v5 Matlab). The other files are called by kf.m (the Kalman filter) or included as they may be useful. Normalis.m normalises data to zero-mean, unit variance along components. I always do this before any further analysis. Steve Roberts 7-2-98 (Kalman filter source code Some brief notes kfdemo.m is a Matlab script file to run a demonstration of theBayesian Kalman filter. It loads file kfdemo.mat (saved as version 4so that it will read in either v4 or v5 Matlab). The other files are called by kf.m (the Kalman filter) or includedas they may be useful. Normalis.m normalises data to zero-mean, unit variance along components. I always do this before any furtheranalysis.Steve Roberts 7-2-98)