Comments on "Linearization techniques for nth-order sensor models in MOS VLSI technology

1992 ◽  
Vol 39 (9) ◽  
pp. 677 ◽  
Author(s):  
Z. Czarnul
Keyword(s):  
Vlsi Technology ◽  
Sensor Models ◽  
Linearization Techniques

Deep Radar Sensor Models for Accurate and Robust Object Tracking

2020 ◽  
Author(s):  
Jasmin Ebert ◽  
Thomas Gumpp ◽  
Sebastian Munzner ◽  
Alex Matskevych ◽  
Alexandru P. Condurache ◽  
...  
Keyword(s):  
Object Tracking ◽  
Radar Sensor ◽  
Sensor Models

A multivariate spectral quasi-linearization method for the solution of (2+1) dimensional Burgers’ equations

2020 ◽  
Vol 21 (7-8) ◽  
pp. 683-691
Author(s):  
Phumlani G. Dlamini ◽  
Vusi M. Magagula
Keyword(s):  
Collocation Method ◽  
Three Dimensional ◽  
Linearization Method ◽  
High Accuracy ◽  
Time Variable ◽  
Spectral Collocation Method ◽  
Spectral Collocation ◽  
Burgers Equations ◽  
Grid Points ◽  
Linearization Techniques

AbstractIn this paper, we introduce the multi-variate spectral quasi-linearization method which is an extension of the previously reported bivariate spectral quasi-linearization method. The method is a combination of quasi-linearization techniques and the spectral collocation method to solve three-dimensional partial differential equations. We test its applicability on the (2 + 1) dimensional Burgers’ equations. We apply the spectral collocation method to discretize both space variables as well as the time variable. This results in high accuracy in both space and time. Numerical results are compared with known exact solutions as well as results from other papers to confirm the accuracy and efficiency of the method. The results show that the method produces highly accurate solutions and is very efficient for (2 + 1) dimensional PDEs. The efficiency is due to the fact that only few grid points are required to archive high accuracy. The results are portrayed in tables and graphs.

Sign in / Sign up

Export Citation Format

Share Document