DeMat

1．在选择装入背包的物品时，对每种物品I只有两种选择，即装入背包或不装入背包。不能将物品I装入背包多次，也不能只装入部分的物品I。 2．物品的种数，重量、价值，背包的容量要求输入； 3．算法中加入剪枝函数（对重量已经超过限制重量的子树不搜索）； -1. In the selection of the items into the backpack, for each of the items I have only two choices, that is loaded or not loaded backpack backpack. Items should not be many times I load backpack can not only part of the goods loaded I. 2. Number of species items, weight, value, capacity backpack for the importation 3. Algorithm to add pruning function (of weight over the weight limit does not search sub-tree)

异质链表查询.存储不同的结构-heterogeneity Chain inquiries. Storage different structure

ST7735小液晶屏在STC51单片机上使用的控制代码，这个代码晚上不太好找，所以上传到这里共享下，同时也给自己备份下。(ST7735 small LCD screen in the use of STC51 microcontroller control code, this code is not easy to find in the evening, so upload to share here, but also to their own backup.)

说明：  一款Bootstrap垂直手风琴折叠菜单。该Bootstrap垂直手风琴折叠菜单使用boostrap网格进行布局(A Bootstrap vertical accordion folding menu. The Bootstrap vertical accordion folding menu is laid out using boostrap grid)

EduSoho网络课堂 8.1.5()

用于电力系统潮流计算，潮流方程的求解基本方法是迭代，包括牛顿-拉夫逊法，以及P-Q分解法(Power Flow Calculation for Power System)

vc并行接口通讯程序,-vc parallel interface communication procedures,

说明：  这些文件将在两用户非正交多址（NOMA）系统中实现用于信号检测的深度学习方法[1]。三个主要脚本分别是生成训练数据、训练神经网络和生成测试结果。该神经网络用于相位衰落的静态标量信道，用于NOMA系统中同时检测2个用户的单个子载波上的传输符号。考虑并测试了两种情况：一种是导频符号数目较少，另一种是循环前缀长度较短。在这两种情况下，深度学习方法都比传统的信道估计方法具有更强的鲁棒性。(These files are to implement the deep learning method for signal detection in a two-user non-orthogonal multiple access (NOMA) system [1]. The 3 main scripts are to generate training data, to train the neural network and to produce testing results, respectively. The neural network is trained for a static scalar channel with phase fading and is used to detect transmitted symbols on a single subcarrier for 2 users simultaneously in a NOMA system. Two scenarios are considered and tested: one is with fewer number of pilot symbols and the other is with shorter length of cyclic prefix. The deep learning method is shown to be more robust than conventional channel estimation methods in both cases.)

说明：  声纹识别，应用三层LSTM进行说话人识别，准确率很高(Speaker verification)

可以查看每天的美食推送，具有播放视频功能，留言功能，后台采用Java书写。(You can view the daily food push, with video playback function, message function, background using Java writing.)