Group-sparse mode decomposition: A signal decomposition algorithm based on group-sparsity in the frequency domain

2021 ◽  
pp. 103375
Author(s):  
Nasser Mourad
Keyword(s):  
Frequency Domain ◽  
Signal Decomposition ◽  
Decomposition Algorithm ◽  
Group Sparsity ◽  
Mode Decomposition

scholarly journals Recursive Variational Mode Decomposition Algorithm for Real Time Power Signal Decomposition

2015 ◽  
Vol 21 ◽  
pp. 540-546 ◽  
Author(s):  
Soman K.P. ◽  
Prabaharan Poornachandran ◽  
Athira S. ◽  
Harikumar K.
Keyword(s):  
Real Time ◽  
Signal Decomposition ◽  
Decomposition Algorithm ◽  
Variational Mode Decomposition ◽  
Mode Decomposition

scholarly journals A research on fiber-optic vibration pattern recognition based on time-frequency characteristics

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881346 ◽  
Author(s):  
Tabi Fouda Bernard Marie ◽  
Dezhi Han ◽  
Bowen An ◽  
Jingyun Li
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Time Domain ◽  
Fiber Optic ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Vibration Pattern ◽  
The Time Domain

To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.

Empirical Mode Decomposition Algorithm Research & Application of Mie Lidar Atmospheric Backscattering Signal

2009 ◽  
Vol 36 (5) ◽  
pp. 1068-1074 ◽  
Author(s):  
郑发泰 Zheng Fatai ◽  
华灯鑫 Hua Dengxin ◽  
周阿维 Zhou Awei
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Algorithm ◽  
Research Application ◽  
Mode Decomposition ◽  
Mie Lidar

An Empirical Mode Decomposition Algorithm Based on Cross Validation and Its Application to Lidar Return Signal De-Noising

2014 ◽  
Vol 41 (10) ◽  
pp. 1014001
Author(s):  
王欢雪 Wang Huanxue ◽  
刘建国 Liu Jianguo ◽  
张天舒 Zhang Tianshu ◽  
董云升 Dong Yunsheng
Keyword(s):  
Empirical Mode Decomposition ◽  
Cross Validation ◽  
Decomposition Algorithm ◽  
Mode Decomposition ◽  
Lidar Return
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