xinzaobitexing

这也是我在网上下载的,不知是否好用,主要用于半自动提取线状图形.(It is used for semi-autometic extracting line-shaped image)

介绍回归问题中高斯过程的应用，C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning,(Gaussian regression problem introduced in the application process, CE Rasmussen & CKI Williams, Gaussian Processes for Machine Learning,)

通过频域合成和复图像合成，实现一维信号的带宽合成，从而提高回波信号距离向的分辨率。(Through the frequency domain synthesis and complex image synthesis, realize a d signal bandwidth synthesis, so as to improve the echo signal to the resolution of the distance. )

含分布式发电的配电网有功-无功综合优化，分布式电源包含风机光伏和无功补偿装置(Active/reactive power integrated optimization in distribution networks with distributed generation)

信号功率谱分析，方法三、四，已在此详细编写(Signal power spectral analysis, methods, three, four, has been prepared in detail here)

This program counts the occurrence of specified patterns in the input strands and produces a report for each pattern. The first input to this program will be an integer indicating how many patterns will be provided (call this N). The next N words to this program will be patterns of nucleobases to search for. Following the patterns will be a sequence of strands terminated by the word "end" as before.

VC++调用matlab画图，效果很好！值得看(VC++ Call matlab drawing, the effect is very good!)

说明：  为作业最终的仿真结果与分析 PL_free.m：free space path loss model PL_logdist.m: log-distance path loss model PL_lognorm.m: log-normal shadowing path loss model plot_PL_general.m: plot the figures itum.m: ITU M.1225 vehicular-B(120km/hr) channel model AWGN.m: create AWGN channel object(The final for the job simulation results and analysis PL_free.m: free space path loss model PL_logdist.m: log-distance path loss model PL_lognorm.m: log-normal shadowing path loss model plot_PL_general.m: plot the figures itum.m: ITU M.1225 vehicular-B (120km/hr) channel model AWGN.m: create AWGN channel object)

说明：  通信原理系统仿真系列--ASK的调制。供刚接触到matlab编程的朋友使用。(communications system simulation series-- ASK modulation. For Matlab programming begin to use a friend.)

Real time optical flow, and video processing, with support for camera input in Matlab.(Start by running "runMe.m" Supports input through: a) synthetically generated test images b) reading avi-file c) camera input (requires image aquisition toolbox) Calculates optical flow by 4 different local methods(all moment based), 2 implementations of Lucas and Kanade, and 2 implementations of point-line flow. Can process easily hundreds of frames per second on a laptop for resolution 200x200 images.)