High-speed microlithography aerial image simulation without four-dimensional singular-value decomposition

2008 ◽  
Author(s):  
Charlie Chung-Ping Chen
Keyword(s):  
Singular Value Decomposition ◽  
High Speed ◽  
Singular Value ◽  
Aerial Image ◽  
Image Simulation ◽  
Value Decomposition

scholarly journals Vision-Based Faint Vibration Extraction Using Singular Value Decomposition

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Xiujun Lei ◽  
Jie Guo ◽  
Chang’an Zhu
Keyword(s):  
Singular Value Decomposition ◽  
High Speed ◽  
Singular Value ◽  
Vibration Measurement ◽  
Displacement Sensor ◽  
Remote Measurement ◽  
Engineering Structures ◽  
High Speed Rail ◽  
Sound Barrier ◽  
Value Decomposition

Vibration measurement is important for understanding the behavior of engineering structures. Unlike conventional contact-type measurements, vision-based methodologies have attracted a great deal of attention because of the advantages of remote measurement, nonintrusive characteristic, and no mass introduction. It is a new type of displacement sensor which is convenient and reliable. This study introduces the singular value decomposition (SVD) methods for video image processing and presents a vibration-extracted algorithm. The algorithms can successfully realize noncontact displacement measurements without undesirable influence to the structure behavior. SVD-based algorithm decomposes a matrix combined with the former frames to obtain a set of orthonormal image bases while the projections of all video frames on the basis describe the vibration information. By means of simulation, the parameters selection of SVD-based algorithm is discussed in detail. To validate the algorithm performance in practice, sinusoidal motion tests are performed. Results indicate that the proposed technique can provide fairly accurate displacement measurement. Moreover, a sound barrier experiment showing how the high-speed rail trains affect the sound barrier nearby is carried out. It is for the first time to be realized at home and abroad due to the challenge of measuring environment.

Improving TF-IDF with Singular Value Decomposition (SVD) for Feature Extraction on Twitter

2017 ◽  
Author(s):  
Ammar Ismael Kadhim ◽  
Yu-N Cheah ◽  
Inaam Abbas Hieder ◽  
Rawaa Ahmed Ali
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition
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