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ml maximum likelihood
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     g. mL?
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     ml.
     术中平均失血 32 0ml。
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     ml~(-1).
     ml~(-1)。
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     ml~(-1).
     ml~(-1),r=0.9999;
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     ml~(-1).
     ml~(-1)的鲎试剂作细菌内毒素检查。
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The speech separation task was seen as a convolution mixture blind signal separation (BSS) problem. There are 3 kinds of main approaches to solve the BSS problem: ME(maximum entropy) algorithm, MMI(minimum mutual information) algorithm, and ML (maximum likelihood) algorithm. The relationship among the 3 kinds of algorithms was analyzed in this paper. Based on the feedback architecture and Gaussian mixture model (GMM) probability density function (pdf) estimation, a new extended ME algorithm...

The speech separation task was seen as a convolution mixture blind signal separation (BSS) problem. There are 3 kinds of main approaches to solve the BSS problem: ME(maximum entropy) algorithm, MMI(minimum mutual information) algorithm, and ML (maximum likelihood) algorithm. The relationship among the 3 kinds of algorithms was analyzed in this paper. Based on the feedback architecture and Gaussian mixture model (GMM) probability density function (pdf) estimation, a new extended ME algorithm speech separation algorithm was proposed. Based on the computer simulations of the proposed algorithm and traditional ME algorithm, it can be concluded that the proposed algorithm has better convergence performance.

基于最大熵法(Maxim um Entropy, ME)、最小互信息量法(Minim um Mutual Inform a-tion, MMI)和最大似然法(Maxim um Likelihood, ML)是解决盲信号分离问题的常用算法,分析了ME、MMI以及ML算法之间关系.基于高斯混合模式(Gaussian Mixture Model, GMM)概率密度函数估计,提出了一种采用反馈结构的扩展最大熵语音分离算法.与传统ME的计算机模拟实验结果比较得知,新算法具有更好的收敛性能和语音分离效果.

This paper is about the synchronization algorithms of OFDM systems First,the ML (maximum likelihood)algorithm is introduced,and its drawbacks are analyzed A new method is presented in this paper which uses a training frame to estimate the timing and frequency deviation jointly The result of computer simulation has showed that the new joint algorithm can overcome the drawbacks of ML algorithm and get better synchronization

本文主要讨论了OFDM系统的定时和频率偏差估计算法 ,针对现有的ML(最大似然 )算法定时不够精确、频偏估计范围过小的缺点 ,提出了一种新算法。该算法利用OFDM训练帧进行定时估计和频偏捕获 ,结合ML算法进行频率估计。仿真结果说明 ,新算法克服了ML算法的缺点 ,能精确定时并进行较大范围的频偏估计

This paper introduces first a multiuser detection model for nonlinear modulation and a psuedolinear system model.Then we describe a ML(Maximum Likelihood) detecting method.The performance of the method is good,but its calculation quantity is large.Then we introduce three nearoptimum detection schemes with lower calculation quantity,and their performance approches the level of the optimum detector.

首先介绍了非线性调制信号的多用户检测模型和伪线性系统模型。然后介绍了ML(MaximumLikelihood)检测方法,其性能最佳,但计算量大,故文中又介绍了3种次最佳检测方法,其计算量小,但性能接近最佳检测器。

 
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