Design of filterbank based on convolved blackman window for minimization of aliasing error

2016 ◽  
Author(s):  
S. Datar ◽  
A. Jain
Keyword(s):  
Aliasing Error

scholarly journals Aliasing Error for Sampling Series Derivatives

2014 ◽  
Vol 13 (1) ◽  
pp. 1-20
Author(s):  
R. M. Asharabi
Keyword(s):  
Aliasing Error ◽  
Sampling Series

scholarly journals Vector sampling expansion in Riesz bases setting and its aliasing error

2008 ◽  
Vol 25 (3) ◽  
pp. 315-334 ◽  
Author(s):  
J.M. Kim ◽  
K.H. Kwon
Keyword(s):  
Riesz Bases ◽  
Aliasing Error

A least upper bound for aliasing error

1968 ◽  
Vol 13 (6) ◽  
pp. 754-755 ◽  
Author(s):  
J. Brown
Keyword(s):  
Upper Bound ◽  
Aliasing Error

scholarly journals The aliasing error in recovery of nonbandlimited signals by prefiltering and sampling

2003 ◽  
Vol 16 (6) ◽  
pp. 949-954 ◽  
Author(s):  
Wenchang Sun ◽  
Xingwei Zhou
Keyword(s):  
Aliasing Error

Application of gPCRK Methods to Nonlinear Random Differential Equations with Piecewise Constant Argument

2017 ◽  
Vol 7 (2) ◽  
pp. 306-324
Author(s):  
Chengjian Zhang ◽  
Wenjie Shi
Keyword(s):  
Differential Equations ◽  
Polynomial Chaos ◽  
Piecewise Constant ◽  
Aliasing Error ◽  
Piecewise Constant Argument ◽  
Finite Dimensional ◽  
Discretisation Error ◽  
Random Differential Equations ◽  
Projection Error ◽  
Constant Argument

AbstractWe propose a class of numerical methods for solving nonlinear random differential equations with piecewise constant argument, called gPCRK methods as they combine generalised polynomial chaos with Runge-Kutta methods. An error analysis is presented involving the error arising from a finite-dimensional noise assumption, the projection error, the aliasing error and the discretisation error. A numerical example is given to illustrate the effectiveness of this approach.

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