Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing
Many techniques have been studied for recovering information from shared media such as optical fiber that carries different types of communication, sensing, and data streaming. This article focuses on a simple method for retrieving the targeted information with the least necessary number of signific...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/7/2285 |
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| author | Gabriel Cibira Ivan Glesk Jozef Dubovan Daniel Benedikovič |
| author_facet | Gabriel Cibira Ivan Glesk Jozef Dubovan Daniel Benedikovič |
| author_sort | Gabriel Cibira |
| collection | DOAJ |
| description | Many techniques have been studied for recovering information from shared media such as optical fiber that carries different types of communication, sensing, and data streaming. This article focuses on a simple method for retrieving the targeted information with the least necessary number of significant samples when using statistical population sampling. Here, the focus is on the statistical denoising and detection of the fiber Bragg grating (FBG) power spectra. The impact of the two-sided and one-sided sliding window technique is investigated. The size of the window is varied up to one-half of the symmetrical FBG power spectra bandwidth. Both, two- and one-sided small population sampling techniques were experimentally investigated. We found that the shorter sliding window delivered less processing latency, which would benefit real-time applications. The calculated detection thresholds were used for in-depth analysis of the data we obtained. It was found that the normality three-sigma rule does not need to be followed when a small population sampling is used. Experimental demonstrations and analyses also showed that novel denoising and statistical threshold detection do not depend on prior knowledge of the probability distribution functions that describe the FBG power spectra peaks and background noise. We have demonstrated that the detection thresholds’ adaptability strongly depends on the mean and standard deviation values of the small population sampling. |
| first_indexed | 2024-04-24T10:35:10Z |
| format | Article |
| id | doaj.art-f795f13f8c174ff9b526b5d0f8bc4320 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T10:35:10Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-f795f13f8c174ff9b526b5d0f8bc43202024-04-12T13:26:40ZengMDPI AGSensors1424-82202024-04-01247228510.3390/s24072285Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical SensingGabriel Cibira0Ivan Glesk1Jozef Dubovan2Daniel Benedikovič3Institute of Aurel Stodola, Faculty of Electrical Engineering and Information Technology, University of Zilina, Komenskeho 843, 03101 Liptovsky Mikulas, SlovakiaDepartment of Multimedia and Information-Communication Technologies, Faculty of Electrical Engineering and Information Technology, University of Zilina, Univerzitna 1, 01026 Zilina, SlovakiaDepartment of Multimedia and Information-Communication Technologies, Faculty of Electrical Engineering and Information Technology, University of Zilina, Univerzitna 1, 01026 Zilina, SlovakiaDepartment of Multimedia and Information-Communication Technologies, Faculty of Electrical Engineering and Information Technology, University of Zilina, Univerzitna 1, 01026 Zilina, SlovakiaMany techniques have been studied for recovering information from shared media such as optical fiber that carries different types of communication, sensing, and data streaming. This article focuses on a simple method for retrieving the targeted information with the least necessary number of significant samples when using statistical population sampling. Here, the focus is on the statistical denoising and detection of the fiber Bragg grating (FBG) power spectra. The impact of the two-sided and one-sided sliding window technique is investigated. The size of the window is varied up to one-half of the symmetrical FBG power spectra bandwidth. Both, two- and one-sided small population sampling techniques were experimentally investigated. We found that the shorter sliding window delivered less processing latency, which would benefit real-time applications. The calculated detection thresholds were used for in-depth analysis of the data we obtained. It was found that the normality three-sigma rule does not need to be followed when a small population sampling is used. Experimental demonstrations and analyses also showed that novel denoising and statistical threshold detection do not depend on prior knowledge of the probability distribution functions that describe the FBG power spectra peaks and background noise. We have demonstrated that the detection thresholds’ adaptability strongly depends on the mean and standard deviation values of the small population sampling.https://www.mdpi.com/1424-8220/24/7/2285statistically small population samplingtwo-sided samplingone-sided samplingthreshold detectionfiber Bragg gratingsFBG sensing |
| spellingShingle | Gabriel Cibira Ivan Glesk Jozef Dubovan Daniel Benedikovič Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing Sensors statistically small population sampling two-sided sampling one-sided sampling threshold detection fiber Bragg gratings FBG sensing |
| title | Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing |
| title_full | Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing |
| title_fullStr | Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing |
| title_full_unstemmed | Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing |
| title_short | Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing |
| title_sort | impact of reducing statistically small population sampling on threshold detection in fbg optical sensing |
| topic | statistically small population sampling two-sided sampling one-sided sampling threshold detection fiber Bragg gratings FBG sensing |
| url | https://www.mdpi.com/1424-8220/24/7/2285 |
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