No abstract

This paper targets a Denoising Dynamic Statistical Threshold (DDST) detection algorithm of Fiber Bragg Grating (FBG) spectral peaks at the presence of 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 σ > 7 dB in the background noise fluctuations).

Semiconductor/electrolyte interfaces attract intense interest to convert solar energy to chemical fuels. Although many analytical models describing the photocurrent−voltage response of these devices exist, they have difficulty to reproduce full numerical simulations under small anodic bias. Herein, we derived an analytic model of a weakly absorbing n-type semiconductor/electrolyte interface with a slow rate of water oxidation reaction, fast recombination rate, and under small anodic bias. Excellent overlap of our model was demonstrated with full numerical simulations. The analytic model enabled us to derive an equation for impedance of the semiconductor/ electrolyte interface in the dark and under illumination. The comparison of analytic and measured impedance allows to extract the reaction rate for redox reaction from the dark impedance and the direct bulk recombination constant of the semiconductor from the impedance under illumination.

The telecommunications data transfer in singlemode (SM) optical fiber (OF) of a passive optical network (PON) is managed by dynamic bandwidth allocation (DBA). In most internet of things (IoT) sensor network applications, both raw sensing signals and telecommunication data are transmitted in SM OFs, too. At the present time, this is not done in a shared SM OF. This paper presents a novel concept to share FBG sensing and telecommunication services (TS) in the optical C-band of the shared transmission. This concept is based on statistical detection and monitoring of fiber Bragg gratings (FBGs) sensing signals. The key steps of the proposed concept are FBG power spectral peaks statistical detection, monitoring of the FBGs dynamics and periodical estimation of TChs occupancy and availability. The proposed concept was demonstrated and validated using sensor network with a deployed group of FBG-based sensors, by implementing various static or dynamic approaches. By doing so, we achieved telecommunication channels (TChs) bandwidth availability approaching 80 %, compared to previously wasted bandwidth with availability at most 15.6 %. Experimental results showed that a DBA system with implemented dynamic TChs occupancy is a reliable way to share fiber bandwidth between both FBG sensing and TChs.

No abstract