Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors
Abstract
:1. Introduction
2. Setup and Methods
2.1. Moving Averaging–Moving Differential
2.2. FDDA Refinement
- (1)
- Intensities within I1 and I2 (assuming that I1 = 0; I2 = 0.2). This region contains the noise of the photodetector. If it is averaged with the maximal window size, then its influence is practically zeroed. However, this will lead to an increase in the number of mathematical operations and a decrease in the algorithm speed. It will also blur the boundaries of neighboring (possibly useful) components of the spectrum. Thus, for these intensity components, it is preferable to set the moving window size to about 80% of the maximal allowable one ().
- (2)
- Intensities within I2 and I3 (I2 = 0.2; I3 = 0.4). Another type of unwanted component may appear in this area: various phantom peaks. The practice of operating inexpensive laser sources has shown that they appear quite often. Sometimes they are electrical in nature and pass through the entire signal in time, frequency, or spatial domains. Since the proposed algorithm will operate in 2D mode, it may help to solve this problem. It is assumed reasonable to use the maximal window size for this intensity region.
- (3)
- Intensities within I3 and I4 (I3 = 0.4; I4 = 1). Obviously, this signal contains the main useful part. Such frequency components should not be smoothed across the spectrum, so the averaging window size should be minimal (ideally zero).
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AFDA | Activation function dynamic averaging |
AOM | Acousto-optic modulator |
AWG | Arbitrary waveform generator |
DAS | Distributed acoustic sensor |
DAQ | Data acquisition |
DFB | Distributed feedback |
EDFA | Erbium-doped fiber amplifier |
FDDA | Frequency domain dynamic averaging |
GELU | Gaussian Error Linear Unit |
MA | Moving averaging |
MD | Moving differential |
MOS | Manual optical switch |
PZT | Piezoelectric transducer |
SNR | Signal-to-noise ratio |
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Laser Type | Event Frequency, kHz | SNR, dB | |||
---|---|---|---|---|---|
No Filtering | MA-MD | AFDA | MA-MD and AFDA | ||
Commercial | 0.5 | 18.8 | 27.3 | 22.5 | 29.2 |
8 | 19.6 | 19.6 | 23.3 | 23.4 | |
Homemade | 0.5 | 15.7 | 25.0 | 19.3 | 26.5 |
8 | 17.9 | 19.2 | 21.4 | 22.8 |
Algorithm | Computation Time, s | Spatial Resolution, m |
---|---|---|
MA-MD | 13.1 | 1 |
AFDA | 24.9 | 1 |
FDDA | 200.0 | 100 |
Algorithm | Most Suitable Event Type | Limitations | Potential Applications |
---|---|---|---|
MA-MD | Pointwise | Requires event frequency information; partial data loss; high frequencies reduce efficiency | Railway monitoring, pest registration, flow metering |
AFDA | Distributed; high-frequency | The signal fraction should be less than noise fraction; may require information on signal characteristics | Flaw detection, perimeter security, bee and plant monitoring |
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Turov, A.T.; Barkov, F.L.; Konstantinov, Y.A.; Korobko, D.A.; Lopez-Mercado, C.A.; Fotiadi, A.A. Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors. Algorithms 2023, 16, 440. https://doi.org/10.3390/a16090440
Turov AT, Barkov FL, Konstantinov YA, Korobko DA, Lopez-Mercado CA, Fotiadi AA. Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors. Algorithms. 2023; 16(9):440. https://doi.org/10.3390/a16090440
Chicago/Turabian StyleTurov, Artem T., Fedor L. Barkov, Yuri A. Konstantinov, Dmitry A. Korobko, Cesar A. Lopez-Mercado, and Andrei A. Fotiadi. 2023. "Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors" Algorithms 16, no. 9: 440. https://doi.org/10.3390/a16090440