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freq_mod_sign
frequency modulated example signal
- 2010-01-14 16:17:50下载
- 积分:1
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BSSGUIlunwen
盲源分离(BSS: Blind Source Separation),又称为盲信号分离,是指在信号的理论模型和源信号无法精确获知的情况下,如何从混迭信号(观测信号)中分离出各源信号的过程。盲源分离和盲辨识是盲信号处理的两大类型。盲源分离的目的是求得源信号的最佳估计,盲辨识的目的是求得传输通道混合矩阵。(Blind signal separation (BSS), also known as blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process. This problem is in general highly underdetermined, but useful solutions can be derived under a surprising variety of conditions. Much of the early literature in this field focuses on the separation of temporal signals such as audio. However, blind signal separation is now routinely performed on multidimensional data, such as images and tensors, which may involve no time dimension whatsoever.)
- 2017-09-20 11:01:09下载
- 积分:1
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EM_GM
% EM algorithm for k multidimensional Gaussian mixture estimation
%
% Inputs:
% X(n,d) - input data, n=number of observations, d=dimension of variable
% k - maximum number of Gaussian components allowed
% ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
% maxiter - maximum number of iteration allowed ([] for none)
% pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
% Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
%
% Ouputs:
% W(1,k) - estimated weights of GM
% M(d,k) - estimated mean vectors of GM
% V(d,d,k) - estimated covariance matrices of GM
% L - log likelihood of estimates
%( EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates)
- 2008-04-27 15:51:27下载
- 积分:1
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nasch_model
基于184号元胞自动机的NaSch交通流模型,matlab编程(No.184 Cellular Automata, NaShc Traffic Model.)
- 2014-12-19 15:18:06下载
- 积分:1
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faceprotected2
its aface detection matlab file
- 2011-12-12 16:55:55下载
- 积分:1
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hig
background subtraction technique
- 2012-01-03 17:41:00下载
- 积分:1
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时间差定位技术TDOA之chan算法
到达时间差定位技术TDOA之chan算法(Time Difference of Arrival chan algorithm of TDOA location technology)
- 2020-06-28 21:00:02下载
- 积分:1
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MATLAB3
精通MATLAB综合辅导与指南 精通MATLAB综合辅导与指南(Proficient in MATLAB comprehensive guide to counseling and comprehensive guidance and proficient in MATLAB Guide)
- 2007-09-23 11:47:34下载
- 积分:1
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pid_foc_funs
分数阶PID比一般PID多了2个可调参数,实现了常规PID所无法实现的控制功能,并且更精确有效(Fractional PID PID more than two adjustable parameters, and can not be achieved by the conventional PID control functions, and more accurate and efficient)
- 2021-04-22 19:58:48下载
- 积分:1
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dds
DDS实验 matlab 与quartus 的完美结合(DDS experimental combination of matlab and quartus)
- 2010-05-08 08:51:48下载
- 积分:1