scholarly journals Digital Image Separation Algorithm Based on Joint PDF of Mixed Images

2015 ◽  
Vol 20 (1) ◽  
pp. 5-12 ◽  
Author(s):  
Mohd Wajid ◽  
Mayank Sharma
Keyword(s):  
Signal To Noise Ratio ◽  
Joint Probability ◽  
Joint Probability Density Function ◽  
Scatter Plot ◽  
Separation Algorithm ◽  
Image Separation ◽  
Joint Probability Density ◽  
Separation Results ◽  
Probability Density Function Pdf ◽  
Better Than

Abstract In this article, we have presented an algorithm for separating the mixed or fused images. We have considered that the two independent histogram equalized digital images are linearly mixed, and the joint probability density function (PDF) or the scatter plot of the two observed or mixed images is used for separation. The objective and subjective separation results are presented, and observed to be better than the other existing techniques in terms of Peak signal-to-noise ratio (PSNR) and Signal-to-interference ratio (SIR).

A Partial Safety Factor Method for System Reliability Prediction With Outsourced Components

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Zhengwei Hu ◽  
Xiaoping Du
Keyword(s):  
System Reliability ◽  
Joint Probability ◽  
System Level ◽  
Safety Factors ◽  
Component Reliability ◽  
Reliability Method ◽  
Component Supplier ◽  
Partial Safety Factors ◽  
Joint Probability Density ◽  
Probability Density Function Pdf

System reliability is usually predicted with the assumption that all component states are independent. This assumption may not accurate for systems with outsourced components since their states are strongly dependent and component details may be unknown. The purpose of this study is to develop an accurate system reliability method that can produce complete joint probability density function (PDF) of all the component states, thereby leading to accurate system reliability predictions. The proposed method works for systems whose failures are caused by excessive loading. In addition to the component reliability, system designers also ask for partial safety factors for shared loadings from component suppliers. The information is then sufficient for building a system-level joint PDF. Algorithms are designed for a component supplier to generate partial safety factors. The method enables accurate system reliability predictions without requiring proprietary information from component suppliers.

scholarly journals Relationship Between Kendall's tau Correlation and Mutual Information

2020 ◽  
Vol 43 (1) ◽  
pp. 3-20
Author(s):  
Mohammad Bolbolian Ghalibaf
Keyword(s):  
Probability Density Function ◽  
Mutual Information ◽  
Probability Density ◽  
Density Function ◽  
Joint Probability ◽  
Joint Probability Density Function ◽  
Kendall’S Tau ◽  
Kendall's Tau ◽  
Joint Histogram ◽  
Joint Probability Density

Mutual information (MI) can be viewed as a measure of multivariate association in a random vector. However, the estimation of MI is difficult since the estimation of the joint probability density function (PDF) of non Gaussian distributed data is a hard problem. Copula function is an appropriate tool for estimating MI since the joint probability density function ofrandom variables can be expressed as the product of the associated copula density function and marginal PDF’s. With a little search, we find that the proposed copulas-based mutual information is much more accurate than conventional methods such as the joint histogram and Parzen window-based MI. In this paper, by using the copulas-based method, we compute MI forsome family of bivariate distribution functions and study the relationship between Kendall’s tau correlation and MI of bivariate distributions. Finally, using a real dataset, we illustrate the efficiency of this approach.

The Joint Probability Density Function of the SSC Combiner Output Signal at two Time Instants in the Presence of Log-Normal Fading

1970 ◽  
Vol 109 (3) ◽  
pp. 11-16 ◽  
Author(s):  
D. S. Krstic ◽  
P. B. Nikolic ◽  
M. C. Stefanovic ◽  
F. Destovic
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Output Signal ◽  
Joint Probability ◽  
Time Instant ◽  
Joint Probability Density Function ◽  
Decision Threshold ◽  
Log Normal ◽  
Joint Probability Density

In this paper the probability density function of the Switch and Stay Combiner (SSC) output signal at one time instant and the joint probability density function of the SSC combiner output signal at two time instants, in the presence of log-normal fading, are determined in the closed form expressions. The results are shown graphically for different variance values and decision threshold values. If the digital telecommunication systems work on the manner described in this paper, the error probability will be significantly reduced. Ill. 6, bibl. 24 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.109.3.161

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