Matlab2010a

射频功率放大器预失真，通过matlab建模来实现预失真前后对比(RF PA predistortion)

利用matlab求已知数据点的拉格朗日插值多项式，包含多种方法(Known data points using matlab find the Lagrange interpolation polynomial, contains a variety of methods)

Codes used to beamform with a horizontal line array

Calculation the impedance of Passive Power Filter

matlab实现粒子群算法，matlab实现粒子群算法。matlab实现粒子群算法。(PSO algorithm matlab implementation)

embed and extract watermark

说明：  用MATLAB编写的meanshift分割图像的方法，供大家参考!(meanshift)

极大似然函数参数估计，mle参数估计实现方法(Maximum Likelihood function parameter estimation, mle parameter estimation method to achieve)

ACE自适配通信环境（ADAPTIVE Communication Environment）是可自由使用、开放源码的面向对象（OO）框架（framework），它实现了许多用于并发通信软件的核心模式。ACE提供了一组丰富的可重用C++包装外观（wrapper facade）和框架组件，可跨多种平台完成通用的通信软件任务，其中包括：事件多路分离和事件处理器分派、信号处理、服务初始化、进程间通信、共享内存管理、消息路由、分布式服务动态（重）配置、并发执行和同步，等等。(ACE Adaptive Communication Environment (ADAPTIVE Communication Environment) is free to use, open source object-oriented (OO) framework (framework), it implements a number of concurrent communication software for the core model. ACE provides a rich set of reusable C++ Packaging appearance (wrapper facade) and the framework components, can be a variety of cross-platform communications software to complete common tasks, including: multi-way separation of incident and event processor assignment, signal handling, service initialization, interprocess communications, shared memory management, message routing, distributed service dynamically (re) configuration, concurrent execution and synchronization, and so on.)

最小均方（LMS）自适应算法就是一中已期望响应和滤波输出信号之间误差的均方值最小为准的，依据输入信号在迭代过程中估计梯度矢量，并更新权系数以达到最优的自适应迭代算法。LMS算法是一种梯度最速下降方法，其显著的特点是它的简单性。这算法不需要计算相应的相关函数，也不需要进行矩阵运算。(Minimum mean-square (LMS) adaptive algorithm is that one has to respond to the expectations and filtering the output signal of the mean square error between the value of the minimum-based, based on the input signal is estimated in the iterative process of gradient vector, and to update the weights in order to achieve the most Adaptive iterative algorithm gifted. LMS algorithm is a steepest descent gradient method, and its significant features is its simplicity. This algorithm does not require calculation of the corresponding correlation function, nor the need for matrix calculation.)