MATLAB-GUI-DESIGN

说明：  一个遗传算法的函数优化，求函数的最小值，matlab编写的程序(A genetic algorithm for function optimization, seeking the minimization, matlab program written)

一种利用迭代求精法来改进的DV-hop算法，MATLAB程序源码(DV-hop localization algorithm)

very interessting PSO thesis

intel的ipp性能库sample code，提供了加速的静态图象处理示例，充分显示了ipp在多核平台下的性能优势(intel performance of ipp library sample code, provided the acceleration of static image processing example, it shows that in multi-core platform ipp performance advantages)

matlab实现16QAM调制解调 matlab实现16QAM调制解调(ALLIANCES is a recently proposed cooperative random access protocol for wireless networks. In this paper we modify the original model to include user location information. We also derive pair-wise error probability (PEP) under Rayleigh flat fading channel and a power-law attenuation environment. Based on the PEP analysis we propose an optimal relay selection scheme, which achieves significant throughput gains as compared to the random relay selection scheme in the original ALLIANCES.)

nonlinear predictive control

说明：  In the field of communication systems, whenever the need for wireless communication arises, there occurs the necessity of an antenna. Antenna has the capability of sending or receiving the electromagnetic waves for the sake of communication, where you cannot expect to lay down a wiring system. The following scenario explains this.

基于lms算法的自适应滤波器的matlab实现（包括详细的编程代码）(Adaptive filter based on the lms algorithm matlab implementation (including details of the programming code)

包含了802.11a的matlab仿真程序，同时附有原版中英文标准，对了解802.11a无限局域网技术有很好的指导作用。(Includes 802.11a, matlab simulation program, accompanied by original Chinese and English standards, 802.11a wireless LAN technology to understand a very good guide.)

1,、设置变量和参量： 　　X(n)为输入向量，或称为训练样本 　　W(n)为权值向量 　　e(n)为偏差 　　d(n)为期望输出 　　y(n)为实际输出 　　η为学习速率 　　n为迭代次数 　　2、初始化，赋给w(0)各一个较小的随机非零值，令n=0 　　3、对于一组输入样本x(n)和对应的期望输出d，计算 　　e(n)=d(n)-X^T(n)W(n) 　　W(n+1)=W(n)+ηX(n)e(n) 　　4、判断是否满足条件，若满足算法结束，若否n增加1，转入第3步继续执行(, set the variables and parameters: X (n) is the input vector, otherwise known as the training sample W (n) for the weight vector e (n) for the deviation d (n) is the desired output y (n) is the actual output η is the learning rate n is the number of iterations)