Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT Application
Hilbert-Huang transform is widely used in signal analysis. However, due to its inadequacy in estimating both the maximum and the minimum values of the signals at both ends of the border, traditional HHT is easy to produce boundary error in empirical mode decomposition (EMD) process. To overcome this...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2015-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/862807 |
| _version_ | 1797714449031757824 |
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| author | Xianzhao Yang Gengguo Cheng Huikang Liu |
| author_facet | Xianzhao Yang Gengguo Cheng Huikang Liu |
| author_sort | Xianzhao Yang |
| collection | DOAJ |
| description | Hilbert-Huang transform is widely used in signal analysis. However, due to its inadequacy in estimating both the maximum and the minimum values of the signals at both ends of the border, traditional HHT is easy to produce boundary error in empirical mode decomposition (EMD) process. To overcome this deficiency, this paper proposes an enhanced empirical mode decomposition algorithm for processing complex signal. Our work mainly focuses on two aspects. On one hand, we develop a technique to obtain the extreme points of observation interval boundary by introducing the linear extrapolation into EMD. This technique is simple but effective in suppressing the error-prone effects of decomposition. On the other hand, a novel envelope fitting method is proposed for processing complex signal, which employs a technique of nonuniform rational B-splines curve. This method can accurately measure the average value of instantaneous signal, which helps to achieve the accurate signal decomposition. Simulation experiments show that our proposed methods outperform their rivals in processing complex signals for time frequency analysis. |
| first_indexed | 2024-03-12T07:52:08Z |
| format | Article |
| id | doaj.art-c264b37c46764d959d89a4dd26dab5c0 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T07:52:08Z |
| publishDate | 2015-10-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-c264b37c46764d959d89a4dd26dab5c02023-09-02T20:33:08ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-10-011110.1155/2015/862807862807Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT ApplicationXianzhao Yang0Gengguo Cheng1Huikang Liu2 Engineering Research Center of Metallurgical Automation and Measurement Technology, Ministry of Education, Wuhan 430081, China Engineering Research Center of Metallurgical Automation and Measurement Technology, Ministry of Education, Wuhan 430081, China Engineering Research Center of Metallurgical Automation and Measurement Technology, Ministry of Education, Wuhan 430081, ChinaHilbert-Huang transform is widely used in signal analysis. However, due to its inadequacy in estimating both the maximum and the minimum values of the signals at both ends of the border, traditional HHT is easy to produce boundary error in empirical mode decomposition (EMD) process. To overcome this deficiency, this paper proposes an enhanced empirical mode decomposition algorithm for processing complex signal. Our work mainly focuses on two aspects. On one hand, we develop a technique to obtain the extreme points of observation interval boundary by introducing the linear extrapolation into EMD. This technique is simple but effective in suppressing the error-prone effects of decomposition. On the other hand, a novel envelope fitting method is proposed for processing complex signal, which employs a technique of nonuniform rational B-splines curve. This method can accurately measure the average value of instantaneous signal, which helps to achieve the accurate signal decomposition. Simulation experiments show that our proposed methods outperform their rivals in processing complex signals for time frequency analysis.https://doi.org/10.1155/2015/862807 |
| spellingShingle | Xianzhao Yang Gengguo Cheng Huikang Liu Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT Application International Journal of Distributed Sensor Networks |
| title | Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT Application |
| title_full | Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT Application |
| title_fullStr | Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT Application |
| title_full_unstemmed | Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT Application |
| title_short | Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT Application |
| title_sort | improved empirical mode decomposition algorithm of processing complex signal for iot application |
| url | https://doi.org/10.1155/2015/862807 |
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