Influence of Interface trap distributions over the device characteristics of AlGaN / GaN / AlInN MOS‐HEMT using Cubic Spline Interpolation technique

2021 ◽  
Author(s):  
Sandeep Viswanathan ◽  
Charles Pravin ◽  
Ramesh Babu Arasamudi ◽  
Prajoon Pavithran
Keyword(s):  
Spline Interpolation ◽  
Cubic Spline ◽  
Interface Trap ◽  
Cubic Spline Interpolation ◽  
Interpolation Technique ◽  
Spline Interpolation Technique

Multi-Axle Moving Train Loads Identification on Continuous Bridge from Bridge Displacement Responses

2012 ◽  
Vol 239-240 ◽  
pp. 670-674
Author(s):  
Yi Wang ◽  
Cong Shuang Luo
Keyword(s):  
Spline Interpolation ◽  
Time History ◽  
Element Model ◽  
Cubic Spline ◽  
Dynamic Displacement ◽  
Cubic Spline Interpolation ◽  
Influence Line ◽  
Interpolation Technique ◽  
Spline Interpolation Technique ◽  
Moving Train

A new method for multi-axle moving train loads identification on continuous bridge is presented in this paper. In order to improve the accuracy and efficiency for moving loads identification, both cubic spline interpolation technique and dynamic displacement influence line technique were employed. The time history displacements of the measurement stations under unit force were calculated with cubic spline interpolation technique based on the finite element model of the bridge. The dynamic displacement influence line was obtained to be used for identifying the moving train loads with simulated annealing genetic algorithm by minimizing the errors between the measured displacements and the reconstructed displacements from the moving train loads. A series of comparative studies were carried out to investigate effects of different span numbers with the same length, different length with the same span number and measurement noise on the proposed method. The result shows that the proposed method is an accurate and efficient method for multi-axle moving train loads identification on continuous bridge.

Study on Removing Baseline Drift in the Brain Blood Stream Signal Based on Cubic Spline Interpolation Technique

2011 ◽  
Vol 480-481 ◽  
pp. 862-867 ◽  
Author(s):  
Nan Quan Zhou
Keyword(s):  
Spline Interpolation ◽  
Blood Stream ◽  
Cubic Spline ◽  
Original Signal ◽  
Brain Wave ◽  
Cubic Spline Interpolation ◽  
Baseline Drift ◽  
Interpolation Technique ◽  
Spline Interpolation Technique ◽  
The Brain

The paper presents a new method to remove baseline drift in the brain blood stream signal based on cubic spline interpolation theory. Firstly, detect some characteristic points in the original signal, and then fit the drifting baseline based on the cubic spline interpolation technique. Finally the original signal minus the baseline is the steady signal of brain blood stream. Lots of simulation experiments and clinical tests had been done, and the baseline drift in LabVIEW software program was removed perfectly. Once the method to remove baseline drift is put in practice, its effect is good and its speed is fast, and it is easy to carry out. It has great application in brain wave monitor with embedded CPU and real time character, and it is also applied in other signal detectors.

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