yangbenshang

digital signal processing and matlab

将实验采集的人生理信号进行处理，提取心音信号，对心音信号进行时域和频域分析(The experiments were collected physiological signal processing, extraction of heart sound signals, heart sound signals in time domain and frequency domain analysis)

MATLAB解微分方程；遗传算法工具箱；laplace偏微分方程求解；(MATLAB for solving differential equations genetic algorithm toolbox laplace partial differential equations )

一篇关于TOPS的有用宽带信号测向方法，一篇关于TOPS的有用宽带信号测向方法(TOPS useful article on the method of measurement of broadband signals, a broadband signal on the TOPS useful method of finding)

ZVS的开环和闭环的matlab仿真，很准确的(simulink of ZVS open and close loop)

在非正交（NOMA）环境下，固定功率分配和树形算法分配的比较，说明树形的优势(In the non-orthogonal (NOMA) environment, relatively fixed power allocation and distribution tree algorithm, explain the benefits of tree)

根据有无固定基础设施，无线局域网又可分为BSS (Basic Service Set)和IBSS (Independent Basic Service Set)。我们要研究的ad hoc网络属于后者。假设在一个ad hoc网络中，移动节点的发射功率PTx总是恒定的。要发送数据的移动节点总是先监听信道，测量接收到的信号功率X，其中X= I + N, I为接收到的干扰，N是噪声。移动节点只有在X＜INThre时，才可以发射。式中，INThre为背景噪声门限。 在仿真中，我们规定每个移动节点的发射功率是常数，PTx = 1W；接收节点接收机的灵敏度Smin = -80 dBm；信号质量 min = 2 dB；系统的背景噪声门限INThre = 1.2e-10。 (According to the availability of fixed infrastructure, wireless local area network can be divided into BSS (Basic Service Set) and IBSS (Independent Basic Service Set). We have to study the ad hoc network belong to the latter. Assuming in an ad hoc network, mobile node s transmission power is always constant PTX. To send data to the mobile node always monitor channel, measuring the received signal power X, which X = I+ N, I was received interference, N is the noise. Mobile node only when X <INThre when it is fired. Where, INThre threshold for background noise. In the simulation, we require that each mobile node s transmission power is constant, PTx = 1W receiver node receiver sensitivity Smin =-80 dBm signal quality )

PLS计算程序，非常经典。可自由组合装PLS计算程序，非常经典。可自由组合装(The PLS calculation procedures, very classic. Freely Combo Pack)

各种LMS算法程序各种LMS算法程序各种LMS算法程序(lms suanfa and so on)

基于ASM的人脸识别程序，包括训练部分。由Ghassan Hamarneh编写(ASM-based face recognition procedures, including the training component. Prepared by Ghassan Hamarneh)