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说明：  用MATLAB实现Harris图像的边缘检测(Harris，MATLAB)

MATLAB在物理中的应用，在学习物理的同时也能掌握一门计算机机技术，并能熟练应用计算机技术来学习物理与分析物理。 已通过测试。 (MATLAB in physics, to study physics at the same time able to grasp the technology of a computer machine and skilled application of computer technology to study physics and analysis of physics. Has been tested.)

matlab 学习技巧，现在把我用的，也就是对我帮助很大的留个文件传上去，希望对大家有用，第一入门与提高(matlab learning skills, and now I use, which is a great help to me to leave a document passed up, we want to be useful, the first entry and improve)

802.16a的三径莱斯信道模型，可直接使用。(802.16a diameter of three Rice channel model can be used directly.)

采用UPML边界条件的三维TM波FDTD算例，自由空间matlab程序(UPML boundary conditions using three-dimensional TM wave FDTD example, free-space program matlab)

基于图像处理的颗粒分析系统设计，对颗粒图像进行处理，滤波，二值化，分割，最后统计颗粒粒径与个数(Particle analysis based on image processing system design, particle image processing, filtering, binarization, segmentation, the final particle size and the number of statistical)

Pseudo-Random Generator(Autokorelation function of PN secvence)

本文主要做了1.对三维重建系统中的预处理算法和分割算法进行细致研究，并通过大量实验进行对比分析，指出其优缺点及适用围。 2.传统的分割算法处理每一幅图片都需要用户选定对象和背景，耗时较长。针 对医学CT连续断层图像间相关性强特点，提出一种把Graph Cut方法和区域生长方法相结合的图像分割算法。3.最后将分割目标与后续重建算法结合起来，建立一个完整的三维重建系统。 (This paper has 1 of 3D reconstruction system of preprocessing and segmentation algorithm is carefully studied, and through a lot of experiments are compared and analyzed, and points out their advantages and disadvantages and application range. 2 traditional segmentation algorithm to deal with each picture requires the user to select the object and background, time-consuming. For medical CT images correlation strong characteristic, put forward a segmentation algorithm combining Graph Cut method and region growing method. 3 finally the target segmentation and subsequent reconstruction algorithms are combined, the establishment of a complete 3D )

高分辨率雷达 距离向压缩算法滤波器设计 三种方法 全面设计(clc clear all close all 2013年9月28日 ---------------参数---------- Tr=42e-6 fs=9.5e6 K=0.7e11 Nfft=2048 -------------匹配滤波 时域生成-------------- t=(-Tr/2+1/fs:1/fs:Tr/2-1/fs) w = kaiser(length(t),2.5) w=w w = ones(1,length(t)) ht1=w.*exp(-1i*pi*K*t.^2) ht2=w.*exp(1i*pi*K*t.^2) hf1=fft(ht1,Nfft) hf2=fft(ht2,Nfft) figure subplot(211) plot(abs(hf1)) title( 匹配滤波器1 幅频响应 ) subplot(212) plot(phase(hf1)) title( 匹配滤波器1 相位 ) figure subplot(211) plot(abs(hf2)) title( 匹配滤波器2 幅频响应 ) )

用MCE算法训练分类器，用Train.txt里的数据进行训练，用Test.txt的数据进行性能检测(Use MCE training algorithm of MCE to train the classifier, use the training data in Train.txt to train the classifier and use the data in the Test.txt to test the performance of the classifier )