全国电子设计竞赛基础篇

说明：  深度图像恢复程序，限于matlab程序仿真使用(depth-recover is for the develp of image or video)

一个很实用的DNS域名解析DNS，用于网络视频服务器中。-A very useful DNS DNS DNS, for network video server.

各个语音研究中，几个比较有代表性的噪声，从而便与研究语音降噪性能。(n each speech research, several representative noises can be used to study the performance of speech noise reduction.)

说明：  汽车bai的7自由度模型du一般是在设计汽车悬架时用zhi到，1/4悬架时2自由度模型，包括簧上部分的dao垂直运动和簧下部分的垂直运动，半车悬架是4自由度模型，整车用7自由度模型。考虑汽车悬架作用，这车车身有三个自由度，分别是车体的Z轴上下运动，沿X轴和Y轴的转动。对于空间刚体6个自由度中的其他3个自由度，2个平动和转动与悬架系统无关，所以没有考虑。上述3个车身自由度加上4个悬架的簧下部分(主要是轮胎的上下跳动)，一共是7个自由度。(The 7-DOF model Du of automobile Bai is generally a 2-DOF model when designing automobile suspension, including the vertical movement of Dao on the spring part and the vertical movement of the under spring part. The half car suspension is a 4-DOF model, and the whole car is a 7-DOF model. Considering the function of automobile suspension, the car body has three degrees of freedom, which are the up and down movement of Z axis of the car body, and the rotation along X axis and Y axis. For the other three of the six degrees of freedom of the space rigid body, two translation and rotation are independent of the suspension system, so they are not considered. The above three body degrees of freedom plus the four suspension spring parts (mainly the up and down runout of tires), a total of 7 degrees of freedom.)

for beginners of adaboost

说明：  根据傅里叶原理，在GUI界面中用正余弦波合成方波(According to Fu Liye's principle, Yu Xianbo is synthesized by positive Fang Bo in the GUI interface)

asp time mod class module

一个用ACEESE做的学生选课系统，希望大家用好！・-ACEESE to do with a student elective system, I hope you make good use of!*

说明：  组态软件带注册码，有谁要，免费拿去。。。。。。。。。。。。。。(Configuration software with registration code, who wants to take it for free...)

说明：  2DPCA是一个图像特征提取的算法。本代码中使用的分类器是最近邻分类器，最后得到的是一个图片识别率与特征矩阵维数的散点图，符合论文《Two-Dimensional PCA: A New Approach to Appearance-BasedFace Representation and Recognition》的预期结果。(2DPCA is an algorithm for image feature extraction. The classifier used in this code is nearest neighbor classifier, and the final result is a scatter diagram of image recognition rate and feature matrix dimensions, which conforms to the expected results of "Two-dimensional PCA: A New Approach to Appearance-BasedFace Representation and Recognition".)