AM_and_FM_modulation

3/4速率Trellis编码的matlab实现(3/4 rate TRELLIS matlab coding to achieve)

本程序由一对m序列优选对模二加生成一组平衡GOLD序列码，用于对直扩系统仿真之中，通过改变第二个m序列的初始状态可以生成不同的GOLD序列码(This procedure by a pair of m-mode sequence optimization of two-plus-one group to generate a balanced sequence GOLD code, for the DS system simulation, and by changing the second m sequence of the initial state can produce different code sequences GOLD)

自适应滤波算法（最小均方误差算法）matlab源码程序(Adaptive filtering algorithm Matlab source program (minimum mean square error algorithm))

一个很有用的程序，小波包分析提取振动信号中的特征频率，D-S证据理论数据融合。( A very useful program, Wavelet packet analysis to extract vibration signal characteristic frequency, D-S evidence theory data fusion.)

压缩感知 L1范数最小化算法正交匹配追踪法重构信号(compressive sensing L1-norm OMP signal reconstruction )

此程序是有关用四阶龙格库塔法计算微分方程的程序。(This program is the fourth-order Runge-Kutta method to calculate the differential equations of the program.)

pwm整流器的建模仿真以及可以实现双闭环控制，并已经验证可以成功(controllable PWM Rectifier pwm rectifier modeling and simulation can achieve double-loop control, and validation can)

BP神经网络进行数据分类，是一种多层前馈神经网络，该网络的主要特点是信号前向传递，误差反向传播。运用梯度下降法，(BP neural network is a multi-layer feedforward neural network for data classification. Its main characteristics are forward signal transmission and back error propagation. Using gradient descent method and BP neural network to classify data is a multi-layer feedforward neural network. The main characteristics of this network are forward signal transmission and back error propagation. Using gradient descent method,)

说明：  P1口的8个灯循环亮，按P32后全部闪烁5次后继续循环亮， 中断程序的典型应用。 (P1 mouth of the eight bright light cycles, according to P32, all the flashes five times after the continued cycle of light, interrupt procedures typical application.)

高分辨率雷达 距离向压缩算法滤波器设计 三种方法 全面设计(clc clear all close all 2013年9月28日 ---------------参数---------- Tr=42e-6 fs=9.5e6 K=0.7e11 Nfft=2048 -------------匹配滤波 时域生成-------------- t=(-Tr/2+1/fs:1/fs:Tr/2-1/fs) w = kaiser(length(t),2.5) w=w w = ones(1,length(t)) ht1=w.*exp(-1i*pi*K*t.^2) ht2=w.*exp(1i*pi*K*t.^2) hf1=fft(ht1,Nfft) hf2=fft(ht2,Nfft) figure subplot(211) plot(abs(hf1)) title( 匹配滤波器1 幅频响应 ) subplot(212) plot(phase(hf1)) title( 匹配滤波器1 相位 ) figure subplot(211) plot(abs(hf2)) title( 匹配滤波器2 幅频响应 ) )