CSMA_CA

用matlab产生仿真BPSK信号，并对BPSK信号采用FFT进行频谱分析。(Simulation of BPSK signals generated by matlab, and BPSK signals using FFT spectral analysis.)

采用k空间方法仿真超声声场及光声、剪切波传播等的专用matlab工具箱程序包，能模拟非均匀介质情况下的波动传播情况，功能强大，(k-wave toolbox for ultrasound simulation)

[ convex_optimization.rar ] - 凸优化 程序包，包含各种凸优化算法,可供方便调用. [ signal_decomposition_by_bp.rar ] - 基于基追踪（basis pursuit）对信号进行稀疏表示的算法 [ cvx .zip ] - 凸规划建模系统，有助于学习压缩感知([Convex_optimization.rar]- convex optimization package that contains a variety of convex optimization algorithms for easy call [signal_decomposition_by_bp.rar]-. Based on the base track (basis pursuit) algorithm for signal sparse representation [cvx .zip]- Convex planning modeling system, including user manuals, help learning compressed sensing)

采用神经网络控制的方法。利用人工神经网络的自学习这一特性，并结合传统的PID控制理论，构造神经网络PID控制器，实现控制器参数的自动调整(Control method using neural network. Artificial neural network to learn the characteristics of the self, combined with the traditional PID control theory, structural neural network PID controller, automatically adjust the controller parameters )

说明：  使用matlab实现希尔伯特黄变换中的经验模态分解，可以抑制信息处理中的端点问题(pelease keep it ,it is useful!)

Fin the FFT, this program find the fast fourier transform, this is of many applications for the processing signal

stm32 matlab 联合开发，利用simulink模块对stm32进行快速开发！

Exercise on Kalman Filter

MUSIC算法MATLAB程序，可用于阵列信号处理。(MUSIC algorithm MATLAB procedures, can be used in array signal processing.)

说明：  在过去的几年中, 背包问题吸引了众多理论研究人员和应用工作者的注意力, 因而得到了广泛而深入的研究。理论方面, 研究兴趣主要来自于该问题简单的结构, 而这种特点既可以深入探索许多组合特性, 又可以通过解决一系列背包子问题来最终求解更为复杂的优化问题。实践方面, 这些问题可以表述许多工业场合的应用, 最典型的应用包括货 物装载、存储分配和资本预算等等。在寻找背包问题可行解的过程中, 出现了众多的算法, 本文是利用MATLAB 程序求背包问题可行解的一种算法。(In the past few years, knapsack problem has attracted many researchers and application of theoretical attention of workers, which has been extensive and in-depth study. Theory, research interest mainly from the simple structure of the problem, and this characteristic combination of both in-depth exploration of many features, but also can solve a series of knapsack sub-problems to solve more complex final optimization problem. Practice, these problems can be expressed in many industrial applications where the most typical applications include cargo loading, storage, distribution and capital budgets, and so on. Knapsack problem in finding feasible solutions in the process, there have been many algorithms, this is the knapsack problem using MATLAB program seeking a feasible solution algorithm.)