Jozef Dubovan

This paper focuses on statistical sampling technique for the SNR-based threshold detection by using a small population sampling via cell averaging technique applied on a sliding wavelength window. We analyze and also demonstrate the threshold detection capability even with extremely small population sampling. This sampling technique is intended for fiber-optic transmission systems that share fiber Bragg gratings (FBG) sensing applications and telecommunication services in the same single mode fiber (SMF) simultaneously. This is demonstrated on two reference sets of data obtained by using two optical spectrum analyzers (OSA) with sparse and dense population samplings, respectively over the C-band fiber region. It has been shown that an appropriate sizing of statistical sampling has a potential to reduce demands on computational resources.

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.

Statistical denoising techniques have been well studied, however many challenges still remain. This 9 article focuses on determining the least necessary number of significant samples in a statistical pop-10 ulation sampling, while minimizing the impact on the statistical detection of the fiber Bragg grating 11 (FBG) power spectra. This is achieved by using Bayesian decision making, small population statis-12 tics while excluding false detection. First, we investigate the impact of two-sided sliding window 13 technique applied near the cell under the test. The window size varies from 8 to 60 samples. The 14 considered maximum size of population sampling is connected with the number of wavelength 15 samples within the bandwidth of the symmetrical FBG power spectra peaks. Regarding the sym-16 metry of FBG power spectra peaks and the possible reduction of computational demands, we inves-17 tigate the impact of a one-sided sliding window. This enables to achieve less processing latency for 18 real time applications thus brings benefits to FBG-based fiber optical sensing. Both, two- and one-19 sided small population sampling techniques are then experimentally verified. Detection results are 20 analyzed in depth via calculating detection thresholds by using the small size window sliding along-21 side the waveband. From results achieved, it was found that the normality three-sigma rule does 22 not need to be followed while using the small population sampling. Experimental demonstrations 23 and analyses showed that the proposed concept of denoising and statistical threshold detection 24 based on the sliding window does not depend on the prior knowledge of probability distribution 25 functions of the FBG power spectra peaks level and background noise. We have also found that the 26 thresholds’ adaptability mainly depends on the statistical numerical characteristics of the above ad-27 ditive mixture of the FBG power spectra peaks signals and background noise.

This paper describes design, theoretical analysis, and experimental evaluation of a π-Phase-Shifted Fiber Bragg Grating (π-PSFBG) inscribed in the standard telecom fiber for slow light generation. At first, the grating was designed for its use in the reflection mode with a central wavelength of 1552 nm and a pass band width of less than 100 pm. The impact of fabrication imperfections was experimentally investigated and compared to model predictions. The optical spectra obtained experimentally show that the spectral region used for slow light generation is narrower (less than 10 pm), thus allowing for too-low levels of slow light optical-output power. In the next step, the optimization of the grating design was conducted to account for fabrication errors, to improve the grating’s spectral behavior and its temporal performance, and to widen the spectral interval for slow light generation in the grating’s transmission mode. The targeted central wavelength was 1553 nm. The π-PSFBG was then commercially fabricated, and the achieved parameters were experimentally investigated. For the region of (1551–1554) nm, a 15-fold increase in the grating’s pass band width was achieved. We have shown that a pair of retarded optical pulses were generated. The measured group delay was found to be ~10.5 ps (compared to 19 ps predicted by the model). The π-PSFBG operating in its transmission mode has the potential to operate as tunable delay line for applications in RF photonics, ultra-fast signal processing, and optical communications, where tunable high precision delay lines are highly desirable. The π-PSFBG can be designed and used for the generation of variable group delays from tens to hundreds of ps, depending on application needs.

This paper targets a Denoising Dynamic Statistical Threshold (DDST) detection algorithm to detect the “presence” of Fiber Bragg Grating (FBG) spectral peaks in noise with changing Signal-to-Noise Ratio (SNR) in a sensing channel. Computing the DDST is based on statistical parameters of the background noise. The DDST is determined by adjusting it using the SNR via determining the targeted probability of false alarms ( p _FA ). The proposed algorithm implements background noise fluctuations, nonlinear signal attenuation of a Single-Mode Fiber (SMF), as well as influence of the short-term interference. The implemented sliding wavelength window technique in conjunction with the FBG spectral peaks power scaling allow automatic adjusting of p _FA and the DDST. During the possible FBGs resonant wavelengths overlap resulting from approaching/colliding spectral power responses of FBGs, the proposed algorithm also improves the detection robustness and resolving of these overlaps. The DDST marginally takes into account spectral shapes of FBGs resonant wavelength peaks. Advantageously, DDST wavelength resolving is independent of FBG spectral peaks shapes. Our DDST algorithm is also simple to implement. Measurements done by two Optical Spectral Analyzers (OSAs) confirmed significant improvements in the background noise reduction (i.e. signal denoising), noisy FBG spectral peaks shapes smoothing and SNR, improved adjacent FBG spectral peaks detectability and resolving. Our experiments also confirmed usability of the DDST algorithm under severe network conditions (with low reflected FBG power below $-$ 75 dB and low SNR $< $ 4 dB resulting a standard deviation of $\sigma >$ 7 dB in the background noise fluctuations) with resolution of 3.43 pm.

In recent years, the technology of optical fibers has rapidly gained ground in many areas of science and industry, including the construction industry. In this article, the technology of optical fibers based on a fiber Bragg grating (FBG) was used to determine tensile forces acting in a basal reinforcement of a scaled down physical model, which included piled embankment and basal reinforcement. Installing FBG sensors on the geogrid made monitoring of axial strains possible, thus allowing determination of the behavior of the basal reinforcement of the piled embankment. On the basis of three tests performed on the physical model, numerical model calibration with the physical model was carried out using the software PLAXIS 3D Tunnel 2.4. The results showed accurate predictions, especially for the low and middle part of the measured deformations where the numerical analysis proposed a solution that can be considered as safe. Installing FBG sensors on biaxial geogrids was a bold idea that was not easy to implement. However, other possibilities have been successfully tested, such as high-frequency measurements of the response of reinforced soil structure under dynamic loading.

Polarization mode dispersion is recognized as a key factor limiting optical transmission systems, particularly those fiber links that run at bit rates beyond 10 Gbps. In-line test and characterization of polarization mode dispersion are thus of critical importance to evaluate the quality of installed optical fibers that are in use for high-speed signal traffics. However, polarization-based effects in optical fibers are stochastic and quite sensitive to a range of environmental changes, including optical cable movements. This, in turn, gives rise to undesired variations in light polarization that adversely impair the quality of the signal transmission in the link. In this work, we elaborate on experimental testing and theoretical analysis to asses changes of polarization mode dispersion in optical fibers that are caused by environmental variations, here wind gusts in particular. The study was performed on commercially harnessed optical fibers installed within optical power ground wire cables, taking into account different weather conditions. More specifically, we showed that changes caused by wind gusts significantly influence the differential group delay and the principal state of polarization in those optical fibers. For this, we experimentally measured a number of parameters to characterize light polarization properties. Measurements were carried out on C-band operated fiber-optic link formed by 111-km-long power ground wire cables and 88 spectral channels, with a test time step of 1 min during 12 consecutive days. Variations in differential group delay allowed for sensitive testing of environmental changes with measured maxims up to 10 ps under the worst wind conditions. Moreover, measured parameters were used in a numerical model to assess the quality of transmitted high-bit-rate optical signals as a function of wind conditions. The analysis revealed a negligible impact of wind on a 10 Gbps transmission, while substantial influence was noticed for higher bit rates up to 100 Gbps. These results show promises for efficient sensing of environmental changes and subsequent monitoring of the quality of recently used fiber-optic link infrastructures.

This article is focused on the automatic classification of passing vehicles through an experimental platform using optical sensor arrays. The amount of data generated from various sensor systems is growing proportionally every year. Therefore, it is necessary to look for more progressive solutions to these problems. Methods of implementing artificial intelligence are becoming a new trend in this area. At first, an experimental platform with two separate groups of fiber Bragg grating sensor arrays (horizontally and vertically oriented) installed into the top pavement layers was created. Interrogators were connected to sensor arrays to measure pavement deformation caused by vehicles passing over the pavement. Next, neural networks for visual classification with a closed-circuit television camera to separate vehicles into different classes were used. This classification was used for the verification of measured and analyzed data from sensor arrays. The newly proposed neural network for vehicle classification from the sensor array dataset was created. From the obtained experimental results, it is evident that our proposed neural network was capable of separating trucks from other vehicles, with an accuracy of 94.9%, and classifying vehicles into three different classes, with an accuracy of 70.8%. Based on the experimental results, extending sensor arrays as described in the last part of the paper is recommended.