RESUMEN
A D-shaped polarization-maintaining fiber (PMF) as fiber optic sensor for the simultaneous monitoring of strain and the surrounding temperature is presented. A mechanical end and edge polishing system with aluminum oxide polishing film is utilized to perform sequential polishing on one side (lengthwise) of the PMF in order to fabricate a D-shaped cross-section. Experimental results show that the proposed sensor has high sensitivity of 46 pm/µÎµ and 130 pm/°C for strain and temperature, respectively, which is significantly higher than other recently reported work (mainly from 2013) related to fiber optic sensors. The easy fabrication method, high sensitivity, and good linearity make this sensing device applicable in various applications such as health monitoring and spatial analysis of engineering structures.
RESUMEN
A multiwavelength Brillouin/Raman distributed Bragg reflector fiber laser operating in the S-band region is proposed and demonstrated. The laser uses a 7.7 km long dispersion-shifted fiber with an effective mode area of 15 µm(2) as the Brillouin and Raman gain media simultaneously. Two 1420 nm laser diodes with a combined power of 372 mW are used as pump sources, while a fiber Bragg grating with a center wavelength of 1500 nm is used as a reflector in the cavity. The setup is capable of generating 6 clearly defined Stokes lines at the highest pump power, spanning from 1499.8 to 1500.3 nm with the even Stokes having relatively higher peak powers, between 1.4 and 3.5 dBm as compared to the odd Stokes, which have peak powers between -4.7 and -5.0 dBm. The output of the laser is very stable and shows little to no fluctuations over a monitoring period of 50 min.
RESUMEN
A highly stable tunable dual-wavelength fiber laser (TDWFL) using graphene as a means to generate a highly stable output is proposed and generated. The TDWFL comprises a 1 m long, highly doped erbium-doped fiber (EDF) acting as the linear gain medium, with a 24-channel arrayed waveguide grating acting as a wavelength slicer as well as a tuning mechanism to generate different wavelength pairs. The tuned wavelength pairs can range from 0.8 to 18.2 nm. A few layers of graphene are incorporated into the laser cavity to induce the four-wave-mixing effect, which stabilizes the dual-wavelength output by suppressing the mode competition that arises as a result of homogenous broadening in the EDF.
RESUMEN
A fiber based bend sensor using a uniquely designed Bend-Sensitive Erbium Doped Fiber (BSEDF) is proposed and demonstrated. The BSEDF has two core regions, namely an undoped outer region with a diameter of about 9.38 µm encompassing a doped, inner core region with a diameter of 4.00 µm. The doped core region has about 400 ppm of an Er2O3 dopant. Pumping the BSEDF with a conventional 980 nm laser diode gives an Amplified Spontaneous Emission (ASE) spectrum spanning from 1,510 nm to over 1,560 nm at the output power level of about -58 dBm. The ASE spectrum has a peak power of -52 dBm at a central wavelength of 1,533 nm when not spooled. Spooling the BSEDF with diameters of 10 cm to 2 cm yields decreasing peak powers from -57.0 dBm to -61.8 dBm, while the central wavelength remains unchanged. The output is highly stable over time, with a low temperature sensitivity of around ~0.005 dBm/°C, thus allowing for the development of a highly stable sensor system based in the change of the peak power alone.
RESUMEN
As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010-2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.