MATLAB0002

说明：  小波神经网络源程序，在网上搜了好久才找到的，对初学者很有用处，希望大家喜欢(Neural network source code, search the Internet for a long time to find, and very useful for beginners, hope you like)

说明：  matlab中add函数的C++实现，即矩阵加法的实现，包含条件判断(matlab add functions in C++ implementation, the realization of the matrix addition, including conditional)

说明：  MATLAB课程的学习课件，梳理了书中的重点难点，可供大家参考，节约时间(MATLAB course of study courseware, combing the focus of the book difficult, for reference, save time)

说明：  压缩采样，基于乃奎斯特采样，压缩编码，变换编码(compressed sampling)

fir滤波器，7、8、13、14，对信号进行alpha、betagamma滤波，调用方法 Hd = fir_alpha 滤波后信号 = filter(Hd,原信号) 得到的信号是时域信号，plot直接显示(Fir filter, 7, 8, 13, 14, alpha filter for signal method is called Hd = fir_alpha After filtering signal = filter (Hd, the original signal) Signal is time domain signals, the plot showed directly)

构造函数实现雪花分形，但是MATLAB运行当迭代次数过多会存在不足，实现较慢！(Constructor to achieve fractal snowflake, but the MATLAB run when the number of iterations too many deficiencies to implement slow!)

C++ 与 MATLAB 混合编程的pdf英文课件 实现图像的Median filter(a step by step guide on how to realize image median filtering with integrated C++ and MATLAB ,tutorial only,no sample code available)

说明：  MATLAB 前推回代法的配电网的潮流计算程序，适合于辐射性配电网和少环的配电网(MATLAB forward and backward substitution method of distribution network load flow calculation program, suitable for distribution network and less radiation distribution network ring)

基于FFT的比值算法，是用比值法实现的MATLAB程序。 (Based on the ratio of FFT algorithm, is the ratio method using the MATLAB program realize.)

基于信道的历史信息来预测不同信道的未来可用时间可以帮助CR选择一个最佳信道进行信息传递。不同的预测准则应用到周期或是随机的开关模式中。一个CR可以学习以往不同信道的模式。我们提出一个简单的分类和学习方法去检测模式的类型和收集需要的信息用于智能信道的选择。MATLAB仿真结果显示提出的方案在随机信道的随机和周期模式中都有优越的表现。随着时间的变化，新到的切换次数减少到55 ，并且延迟时间也相应减少，而吞吐量却在提高。(Prediction of future availability times of different channels based on history information helps a cognitive radio (CR) to select the best channels for control and data transmission. Different prediction rules apply to periodic and stochastic ON-OFF patterns. A CR can learn the patterns in different channels over time. We propose a simple classification and learning method to detect the pattern type and to gather the needed information for intelligent channel selection. Matlab simulations show that the proposed method outperforms opportunistic random channel selection both with stochastic and periodic channel patterns. The amount of channel switches needed over time reduces up to 55 , which reduces also the delay and increases the throughput.)