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完成VC++控制WORD书签程序，能够自动在VC下打开WORD2000程序(complete control procedures Word bookmarks to automatically open the VC procedures WORD2000)

说明：  小波脊线提取算法研究,用于信号调制类型识别(Wavelet ridge extraction algorithms for signal modulation type recognition)

自己实现字符串类，包括字符串的符号重载功能等，适合于C++初学者学习字符串的结构及C++的封装。(Achieve their own string class, including overloaded function symbol string, etc., suitable for beginners to learn the structure and C++ C++ package string.)

说明：  计算去噪前后的图像PSNR代码，利用MATLAB实现(The code is realized to calculate PSNR of images before and after denoising, and realized by MATLAB)

a射线浏览器V3.1源码程序，结合易语言扩展界面支持库，调用API函数实现一般浏览器的功能。(a ray Browser V3.1 source program, combined with easy language support library expansion interface , call the API functions to achieve the general browser functions.)

迷你SPY源码例程，程序结合易语言扩展界面支持库，调用API函数取窗口信息。(Mini SPY source routines, programs combined with easy language support library expansion interface , call the API function takes the window information .)

windows网络编程电子书,内容包括NetBIOS和Windows重定向器方法、Winsock方法、客户端远程访问服务器方法。本书论述深入浅出、用大量实例详解了微软网络API函数的应用。配套光盘包含了所有实例代码，方便读者使用。本书适合中、高级程序设计人员以及网络设计与管理人员参考。-windows network programming e-books, including NetBIOS and Windows redirector methods, Winsock methods, client methods of remote access server. The book discusses in simple terms, using a large number of detailed examples of the Microsoft Network Application API function. Supporting CD-ROM contains all example code to allow readers to use. This book suitable for middle and senior programming staff, as well as network design and management of reference.

可以检测网络状态，包挂是否伤亡，打开生么网址很好-can detect network state, linking packet casualties, the website opened Mody good health

c++ step by step trainig ebook

说明：  使用协同过滤的推荐系统测试代码Predicting movie ratings using collaborative filtering and latent concept extractors based on matrix decomposition methods like SVD and CUR on the movielens-100k dataset The project explores techniques for predicting unknown user ratings based on a large dataset of users and movie ratings.(Recommender-SystemsWe used 6 different recommender methods to predict user ratings across the data sets. Namely we set up Collaborative Filtering along with its baseline variant and Matrix decomposition techniques like Singular Value Decomposition and CUR. Moreover, we also implemented dimensionality reduction in these matrix decomposition techniques which preserved 90% of the energy so as to increase the computational efficiency while keeping the accuracy similar for these methods.)