scholarly journals An Improved Maximum-Likelihood Estimation Algorithm for Blind Image Deconvolution Based on Noise Variance Estimation

2012 ◽  
Vol 16 (6) ◽  
pp. 629-635
Author(s):  
Chong Yi ◽  
Tetsuya Shimamura
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Variance Estimation ◽  
Likelihood Estimation ◽  
Estimation Algorithm ◽  
Image Deconvolution ◽  
Noise Variance ◽  
Blind Image Deconvolution ◽  
Blind Image

Software Demodulation Method of PCM/FM Signal Based on the Maximum Likelihood Estimation

2014 ◽  
Vol 556-562 ◽  
pp. 5093-5097
Author(s):  
Yan Jun Wu ◽  
Gang Fu ◽  
Xue Lu Yao
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Estimation Algorithm ◽  
Estimation Model ◽  
Block Transmission ◽  
Path Search ◽  
Performance Results ◽  
Frequency Demodulation ◽  
Demodulation Method

Describes the establishment of maximum likelihood estimation model and victor than the realization of the search path. At the same time according to the data block transmission mode in the software and the characteristics of victor than path search, research a kind of software demodulation is implemented based on data redundancy, make full use of the parallel processing computer system performance, improve the operation efficiency of the algorithm. And the algorithm error rate as an index, compares the software likelihood estimation algorithm with the traditional frequency demodulation performance, results show that the BER 10-4 when algorithm can get the demodulation gain of about 3 dB.

scholarly journals Equilibrium Selection in Evolutionary Games with Imperfect Monitoring

2008 ◽  
Vol 45 (2) ◽  
pp. 388-402 ◽  
Author(s):  
Hsiao-Chi Chen ◽  
Yunshyong Chow
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Learning Algorithm ◽  
Large Population ◽  
Likelihood Estimation ◽  
Estimation Algorithm ◽  
Coordination Games ◽  
Imperfect Monitoring ◽  
Long Run ◽  
Two Equilibria

In this paper we analyze players' long-run behavior in evolutionary coordination games with imperfect monitoring in a large population. Players can observe signals corresponding to other players' unseen actions and use the proposed simple or maximum likelihood estimation algorithm to extract information from the signals. In the simple learning process we find conditions for the risk-dominant and the non-risk-dominant equilibria to emerge alone in the long run. Furthermore, we find that the two equilibria can coexist in the long run. In contrast, the coexistence of the two equilibria is the only limit distribution under the maximum likelihood estimation learning algorithm. We also analyze the long-run equilibria of other 2x2 symmetric games under imperfect monitoring.

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