u=0.2 Duffing方程的分叉图

说明：  网jQuery焦点图是一款来自网站首页的带有tab选项卡的jQuer(Web jQuery Focus Map is a jQuer with tab tab from the home page of the website.)

小游戏能弓大家交流希望各位提点意见我建议给我让我改进-Game to bow to facilitate the exchange of提点意见hope that I would suggest to me, let me improve

说明：  用于网页大麦网抢票脚本，用于演唱会门票，剧院门票等(damaiwang Concert ticket)

这是个关于“日期、菜单和字符串”C函数代码-This is on the "date menu, and the string" C function code

fortran77转换成matlab语言代码(Fortran77 conversion to MATLAB language code)

说明：  有关MIMO系统的建模以及其注水算法的实现(The modeling of MIMO system and the implementation of its water injection algorithm)

说明：  K3工业插件二次开发billform常用公开的方法和属性(Common public methods and attributes of billform for secondary development of K3 industrial plug-ins)

RTD2260芯片手册，该芯片支持VGA，HDMI，DVI等多种格式输入，LVDS输出连接液晶屏，内部集成单片机，可以做OSD菜单(RTD2260 chip manual, the chip supports VGA, HDMI, DVI and other forms of input, LVDS output connected to the LCD screen, internal integration of microcontroller, can do OSD menu)

说明：  设计一个数字FIR低通滤波器，其技术指标如下： Wp=0.2pi Rp=0.25Db Ws=0.3pi As=50db 试写出MATLAB设计的源程序 (Design a digital FIR low-pass filter, the technical indicators are as follows: Wp = 0.2pi Rp = 0.25Db Ws = 0.3pi As = 50db Write down the design source MATLAB)

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.)