SU-FF-T-91: Unusually Large Fluctuation of Monitor Unit (MU) Efficiency in Large-Field IMRT Plans Using Sliding-Window Technique

2005 ◽  
Vol 32 (6Part7) ◽  
pp. 1970-1970
Author(s):  
H Song ◽  
Z Chen ◽  
Y Fan ◽  
J Deng ◽  
M Ahmad ◽  
...  
Keyword(s):  
Sliding Window ◽  
Large Field ◽  
Monitor Unit ◽  
Large Fluctuation ◽  
Window Technique

Overestimation of Low-Dose Radiation in Intensity-Modulated Radiotherapy with Sliding-Window Technique

2009 ◽  
Vol 185 (12) ◽  
pp. 821-829 ◽  
Author(s):  
Hilke Vorwerk ◽  
Daniela Wagner ◽  
Björn Seitz ◽  
Hans Christiansen ◽  
Hendrik A. Wolff ◽  
...  
Keyword(s):  
Low Dose ◽  
Intensity Modulated Radiotherapy ◽  
Sliding Window ◽  
Low Dose Radiation ◽  
Intensity Modulated ◽  
Window Technique ◽  
Dose Radiation

scholarly journals Doppler reduction using Sliding Window technique in WP-OFDM system

2011 ◽  
Vol 8 (9) ◽  
pp. 689-694
Author(s):  
Haitham J. Taha ◽  
M. F. M. Salleh
Keyword(s):  
Sliding Window ◽  
Ofdm System ◽  
Window Technique

scholarly journals Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine

2018 ◽  
Vol 7 (3.3) ◽  
pp. 218 ◽  
Author(s):  
D Senthil ◽  
G Suseendran
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Association Rule ◽  
Association Rule Mining ◽  
Time Series Data ◽  
Sliding Window ◽  
Series Data ◽  
Support Vector ◽  
Rule Mining ◽  
Window Technique

Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.  

Feature based sliding window technique for face recognition

2010 ◽  
Author(s):  
Muhammad Younus Javed ◽  
Syed Maajid Mohsin ◽  
Muhammad Almas Anjum
Keyword(s):  
Face Recognition ◽  
Sliding Window ◽  
Window Technique ◽  
Feature Based
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