LDPC-encdecPAWGN

An English document about Automatic Plate recognition system.

matlab源程序 很有用的 你们可以试用一下(matlab source code useful you can try)

these codes are for solving OED with matlab

Savitzky-Golay Filtering of a Speech Signal Smooth the mtlb signal by applying a cubic Savitzky-Golay filter to data frames of length 41.

所传代码主要功能是通过MCKD算法对故障信号进行故障特征频率的提取(The main function of the code is to extract the fault characteristic frequency of the fault signal by MCKD algorithm)

在matlab编程环境下的标准粒子群（PSO）优化算法程序(err)

说明：  利用MATLAB实现的产生M序列的程序及自动产生本原多项式的程序(MATLAB achieved using M sequences generated automatically generated procedures and procedures for primitive polynomial)

无限冲击响应滤波器 (IIR) 实现，涉及一个Butterworth低通滤波器，设计指标为：在通带内频率低于0.2π时，最大衰减小于1dB；在阻带内[0.3π, π]频率区间上，最小衰减要大于15dB (An infinite impulse response filter (IIR) implementation, involves a Butterworth low-pass filter design specifications as follows: in the passband frequencies below 0.2π, the maximum attenuation is less than 1dB in the stopband [0.3π, π] frequency interval , the minimum attenuation greater than 15dB )

matlab图像融合工具箱,实现多源传感器图像融合功能(Matlab toolbox image fusion, multi-sensor image fusion function)

图1为均衡带限信号所引起失真的横向或格型自适应均衡器(其中横向FIR系统长M=11), 系统输入是取值为±1的随机序列，其均值为零；参考信号；信道具有脉冲响应： 式中用来控制信道的幅度失真(W = 2～4, 如取W = 2.9,3.1,3.3,3.5等)，且信道受到均值为零、方差(相当于信噪比为30dB)的高斯白噪声的干扰。试比较基于下列几种算法的自适应均衡器在不同信道失真、不同噪声干扰下的收敛情况(对应于每一种情况,在同一坐标下画出其学习曲线): 1）横向/格-梯型结构LMS算法 2）横向/格-梯型结构RLS算法 并分析其结果。(Figure 1 is a band-limited signal caused balanced horizontal or lattice distortion adaptive equalizer (FIR system in which the lateral length M = 11), the system input is the value of a random sequence of ± 1, mean zero reference signal channel has impulse response: Wherein the amplitude of the distortion for the control channel (W = 2 ~ 4, and so as to take W = 2.9,3.1,3.3,3.5), and subjected to a channel with zero mean and variance (equivalent SNR is 30dB) Gaussian white noise. Compare the following types of algorithm-based adaptive equalizer in a different channel distortion, the convergence of different noise interference (corresponding to each situation, draw their learning curve in the same coordinate): 1) landscape/grid- LMS algorithm ladder structure 2) landscape/grid- RLS algorithm ladder structure And analyze the results.)