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Mean_mean

于 2011-05-17 发布 文件大小:1KB
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  remove noise frome multiple noisy images by mean operation

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  • levinson
    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.)
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    仿真图是速度、距离、幅度三维图像,用于图像处理的独立分量分析,关于神经网络控制,对于初学者具有参考意义,相关分析过程的matlab方法,多目标跟踪的粒子滤波器,研究生时的现代信号处理的作业,包括最小二乘法、SVM、神经网络、1_k近邻法。( FIG simulation speed, distance, amplitude three-dimensional image, Independent component analysis for image processing, On neural network control, For beginners with a reference value, Correlation analysis process matlab method, Multi-target tracking particle filter, Modern signal processing jobs when the graduate, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.)
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