PSOabc

对于一幅输入图像，将产生一幅输出图像，输出图像的每个像素点的灰度值由输入像素点决定，将感兴趣特征的对比度扩展使之占据可显示灰度级的更大部分。使一输入图像转换为在每一灰度级上都有相同的像素点数，均衡化会减少灰度级数。大多数自然图像由于其灰度值分布集中在较窄的范围之内，引起图像细节不够清楚。采用直方图修正后可使图像的灰度间距拉大或使灰度均匀分布，从而增大了反差，使图像细节清楚，达到增强的目的。(For an input image, will produce an output image, the output image gray value of each pixel from the input pixel decided to expand interest in the contrast of features to make it account for more gray levels can be displayed most. Make an input image is converted to grayscale in each pixel have the same points, equalization will reduce the gray series. Most natural images because of their gray value distribution concentrated in a narrow range, causing the image detail is not clear enough. Make the image histogram revised by the gray space widening or so gray uniform distribution, thereby increasing the contrast between the image details clear objective to achieve enhanced.)

mimo alamouti diversity

说明：  利用matlab编写的遗传算法实现控制系统的计算机程序的优化设计(using Matlab prepared by the Genetic Algorithm computer control system design optimization process)

能够实现经典谱估计和现代谱估计的方法的比较，而且表现结果直接明了。(Can realize classic spectral estimation and modern spectral estimation method , and performance results direct and clear. )

ISAR运动补偿逆合成孔径雷达运动补偿逆合成孔径雷达(ISAR motion compensation ISAR motion compensation inverse synthetic aperture radar)

Matrix Converter Commutation Strategies With and Without Explicit Input Voltage Sign Measurement

实现基本（15，11）汉明码的编码及译码过程的MATLAB仿真(Achieve basic (15,11) Hamming code encoding and decoding process of MATLAB simulation)

这个matlab程序通过标准的hough变换，并指定圆半径的情况下，在二值图像中检测圆。(The matlab program through standard hough transform, and specify the radius, the two values in the detection of circular images.)

给lee。用来通信仿真试验。关于信道编码和信源编码(to lee. For communications simulation. On the channel coding and source coding)

希望给辛苦科研的人带来一点点帮助--学习因子可以变化的PSO算法，不再是c1=c2=2，粒子进化更加灵活，下面是学习因子同步变化的pso，调用形式为[xm,fv]=LNCPSO(fitness,N,cmax,cmin,w,M,D)(People who want to work hard to bring a little bit of research to help- learning factor can vary PSO algorithm is no longer c1 = c2 = 2, particle evolution is more flexible, learning factor is below synchronous changes pso, calling in the form of [xm, fv] = LNCPSO (fitness, N, cmax, cmin, w, M, D))