English-VB6_Demo

C语言精彩百例第45－70例 第二篇 深入提高篇 实例45 结构体变量 实例46 结构体数组 实例47 结构体指针变量 实例48 结构体指针数组 实例49 共用体变量 实例50 枚举类型 实例51 读写字符 实例52 读写字符串 实例53 格式化输出函数 实例54 格式化输入函数 实例55 打开和关闭文件 实例56 fputc()和fgetc() 实例57 函数rewind() 实例58 fread()和fwrite() 实例59 fprintf()和fscanf() 实例60 随机存取 实例61 错误处理 实例62 综合实例 实例63 动态分配函数 实例64 常用时间函数 实例65 转换函数 实例66 查找函数 实例67 跳转函数 实例68 排序函数 实例69 伪随机数生成 实例70 可变数目变元(err)

Visual Assist X 10.5.1715

此程序可以让你轻松的获得对方的数据，也可以发送给对方，这个可是通过winsock实现的，可以作为理解winsock的一个很好的例子-This procedure allows you to easily access each other"s data can also be sent to the other side, but through the realization of the related protocols, the protocols of understanding as a good example!

filter audio signalThen use the Butter function, for instance to obtain your signal(type Butter in your Matlab command window and you will find many other type of filters). Once you have obtained the B and A vector coefficients you can filter your signal using the function filter. y=filter(x,A,B) x(is your input signal) and y your filtered signal.(an example filtering signalThen use the Butter function, for instance to obtain your signal(type Butter in your Matlab command window and you will find many other type of filters). Once you have obtained the B and A vector coefficients you can filter your signal using the function filter. y=filter(x,A,B) x(is your input signal) and y your filtered signal.)

由ActionList1动态生成树的操作 可方便对权限的管理 可方便对数据字典的管理(Dynamic spanning tree operation by the ActionList1 can facilitate rights management can facilitate the management of the data dictionary)

二进制输出文本，很好用很好用很好用很好用(VERY USEFUL)

多线程实时网络传输，用于上位机与下位机实时通信-Multi-threaded real-time network transmission, for PC and next-bit machine real-time communication

当把U盘放插入 然后程序自动将U盘的内容复制到本地硬盘(When put to insert U disk, then the program will automatically copy the contents to your local hard disk U)

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.)

重力地形改正，细节处理很完美，近场很精细( Terrain correction)