Signal recognition of the optical fiber vibration sensor based on two-level feature extraction

2015 ◽  
Author(s):  
Liang Wang ◽  
Yubin Guo ◽  
Tiegang Sun ◽  
Jiayu Huo ◽  
Le Zhang
Keyword(s):  
Feature Extraction ◽  
Optical Fiber ◽  
Vibration Sensor ◽  
Signal Recognition

scholarly journals Signal Recognition Basing on Optical Fiber Vibration Sensor

2013 ◽  
Vol 347-350 ◽  
pp. 743-747
Author(s):  
Hai Yan Xu ◽  
Zhuo Zhang ◽  
Xue Wu Zhang
Keyword(s):  
Optical Fiber ◽  
Pattern Matching ◽  
Optical Fiber Sensor ◽  
Continuous Distribution ◽  
Vibration Sensor ◽  
Signal Recognition ◽  
Long Distance ◽  
Signal Characteristics ◽  
Distributed Optical Fiber Sensor ◽  
Distributed Optical Fiber

Distributed optical fiber sensor can acquire the information of physical field along time and spatial continuous distribution. It plays an important role in long-distance oil and electricity transmission and security. In this paper, the author introduced the universal steps in triggering pattern recognition, which includes signal characteristics extracting by accurate endpoint detecting, templates establishing by training, and pattern matching. By training the samples acquired in the laboratory, three templates are established. And pattern matching had been done between templates and all the samples. The results show that, 87.5 percent of the samples are matched correctly with the triggering patterns they are belonging to.

scholarly journals A New Feature Extraction Algorithm Based on Entropy Cloud Characteristics of Communication Signals

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Jingchao Li ◽  
Jian Guo
Keyword(s):  
Feature Extraction ◽  
Cloud Model ◽  
Recognition Rate ◽  
Signal Recognition ◽  
Communication Signals ◽  
Low Snr ◽  
Feature Extraction Algorithm ◽  
Cloud Characteristics ◽  
Extraction Algorithm ◽  
New Feature

Identifying communication signals under low SNR environment has become more difficult due to the increasingly complex communication environment. Most relevant literatures revolve around signal recognition under stable SNR, but not applicable under time-varying SNR environment. To solve this problem, we propose a new feature extraction method based on entropy cloud characteristics of communication modulation signals. The proposed algorithm extracts the Shannon entropy and index entropy characteristics of the signals first and then effectively combines the entropy theory and cloud model theory together. Compared with traditional feature extraction methods, instability distribution characteristics of the signals’ entropy characteristics can be further extracted from cloud model’s digital characteristics under low SNR environment by the proposed algorithm, which improves the signals’ recognition effects significantly. The results from the numerical simulations show that entropy cloud feature extraction algorithm can achieve better signal recognition effects, and even when the SNR is −11 dB, the signal recognition rate can still reach 100%.

Vibration Sensing and Impact Location Detection Using Optical Fiber Vibration Sensor

2000 ◽  
Vol 183-187 ◽  
pp. 661-666
Author(s):  
Y.C. Yang ◽  
W. Hwang ◽  
Hyun Chul Park ◽  
Kyung Seop Han
Keyword(s):  
Optical Fiber ◽  
Vibration Sensor ◽  
Impact Location ◽  
Vibration Sensing ◽  
Location Detection

An etched optical fiber as a vibration sensor

2012 ◽  
Vol 55 (1) ◽  
pp. 75-79
Author(s):  
Putha Kishore ◽  
Dantala Dinakar ◽  
P. Vengal Rao ◽  
K. Srimannarayana
Keyword(s):  
Optical Fiber ◽  
Vibration Sensor

scholarly journals Fiber Optic Vibration Sensors

2021 ◽  
Author(s):  
Putha Kishore ◽  
Dantala Dinakar ◽  
Manchineellu Padmavathi
Keyword(s):  
Optical Fiber ◽  
Measurement Method ◽  
Fiber Optic ◽  
Sensor Response ◽  
Vibration Sensor ◽  
Vibration Measurement ◽  
Sensor Design ◽  
Plastic Optical Fiber ◽  
Intensity Modulated ◽  
Vibration Sensors

The sensors presented in this chapter are fiber optic intensity modulated vibrations sensors which are non-contact (extrinsic sensor) to the vibrating object. Three sensors presented make use of non-contact vibration measurement method with plastic fiber using distinct designs, improvement of the sensor response and advantages of one sensor over the other for diverse applications. First discussed about dual plastic optical fiber vibration sensor design and its response. Secondly, discussed about 1x2 fused coupler plastic optical fiber vibration sensor design with advantages over the first one. Finally, discussed about the 2x2 fused coupler plastic optical fiber vibration sensor design along with advantages than other two methods. At the end reported the final results with comparison.

Event Discrimination Method for Distributed Optical Fiber Intrusion Sensing System Based on Integrated Time/Frequency Domain Feature Extraction

2019 ◽  
Vol 39 (6) ◽  
pp. 0628002 ◽  
Author(s):  
彭宽 Kuan Peng ◽  
冯诚 Cheng Feng ◽  
王森懋 Senmao Wang ◽  
艾凡 Fan Ai ◽  
李豪 Hao Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Optical Fiber ◽  
Frequency Domain ◽  
Time Frequency ◽  
Sensing System ◽  
Distributed Optical Fiber ◽  
Discrimination Method
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