Paper Info
Title
An analytical approach to signal reconstruction using Gaussianapproximations applied to randomly generated and flow cytometric data
Abstract
This study introduces an analytical approach to signal reconstruction using Gaussian distributions. A major problem encountered in real-world data distributions is in the ability to accurately separate those data distributions that experience overlap. A first objective then is to develop a method of determining accurately the characteristics of a given distribution even when it has been affected by another distribution that lies close to it. In addition, normally, two-dimensional (2-D) Gaussian distributions are described by means of a correlation coefficient, but in this case, a normal 2-D distribution will be assumed in a direction parallel to a reference axis and then rotated by some angle θ. This outcome will not affect the results in terms of the standard use of the correlation coefficient. In this study, an attempt is made to provide a highly accurate yet computationally inexpensive approach of resolving the problem of overlap as we seek the reconstruction of signals through Gaussian curve fitting. Implementation results are shown in support of this assertion
Year
DOI
Venue
2000
10.1109/78.869034
IEEE Transactions on Signal Processing
Keywords
Field
DocType
analytical approach,Gaussian curve fitting,real-world data distribution,computationally inexpensive approach,major problem,2-D distribution,Gaussian distribution,direction parallel,data distribution,flow cytometric data,correlation coefficient
Correlation coefficient,Signal processing,Mathematical optimization,Curve fitting,Flow (psychology),Random sequence,Gaussian,Gaussian function,Mathematics,Signal reconstruction
Journal
Volume
Issue
ISSN
48
10
1053-587X
Citations 
PageRank 
References 
0
0.34
8
Authors
4
Name
Order
Citations
PageRank
Adjouadi, M.1304.01
Reyes, C.200.34
Vidal, P.300.34
A. B. Barreto400.34