We present a high-temperature interferometric pressure sensor using a simple-design and easy-to-fabricate pure silica four-hole novel microstructured optical fiber. The asymmetric geometry of the fiber allows hydrostatic pressure to induce stress at the optical fiber core, which converts to an interferometric shift. The large core of the fiber supports the propagation of several modes. Multimode interference created between different pairs of modes is used to sense the temperature and pressure change. The use of pure silica fiber is motivated by the ability of this fiber to operate up to high temperature as dopant diffusion is avoided. The sensor is demonstrated to measure pressure at a temperature up to 800 °C. We demonstrate temperature compensation using a Fourier approach to monitor different interference pairs and their phase response to pressure and temperature change. Experimental results show that the sensor has a linear response and excellent stability with a detection limit of 8.86 kPa at 800 °C temperature. This simple, compact, and potentially low-cost sensor is promising for harsh environment applications to improve quality control, operation efficiency, and safe working conditions.

Optical fiber pressure sensing is of significant interest for industrial process monitoring and acoustic sensing. However, the direct detection of pressure changes along an optical fiber with minimal crosstalk is a significant challenge due to the low stress-optic coefficient of glass and interferences from temperature, bending, and strain. Here, we demonstrate that deep learning combined with microstructured optical fiber specklegram sensing is an effective approach to overcoming environmental crosstalk for the challenging application of pressure sensing. Specklegrams created from multimode interference are inherently sensitive to both the measurand of interest and other environmental perturbations. By employing a deep neural network, namely a multilayer perceptron, we show that the environmental crosstalk of the specklegram-based pressure sensor can be mitigated. Furthermore, we demonstrate the practical implementation of the machine learning-based pressure sensor where we need to continuously update the model. We show that the network can learn and reject environmental disturbances and predict pressure values correctly through continuous training updates. This technique approaches the instrument error of our calibration pressure gauge, with an error below 3 kPa over a range of 1 MPa.

To meet the increasing demand of bandwidth and speed, Free Space Optics (FSO) has been emerged as a promising wireless communication technique leading behind the conventional copper wire and fiber optic communications. The capacity of this cost-effective and rapidly deployable FSO system can be increased using Wavelength Division Multiplexing (WDM) technique. Atmospheric turbulence and pointing error are two constant phenomena in FSO that affect transmitted signal strength along with different critical weather like haze, rain, snow and fog. This paper presents a complete overview of WDM-FSO system in terms of link range and bit rate considering all the impediments of Free-Space channel i.e. how long the signal can propagate and how much bit rate the WDM-FSO system can support maintaining the acceptable value of Bit Error Rate (BER) of 10 ^-9 and Quality factor of 6 in an atmospheric turbulent channel with pointing error under various weather conditions like haze, moderate rain and foggy weather. It is found that using 5 × 10 ^-15 m ^-2/3 as the value of the index of refraction structure for atmospheric turbulence and respective maximum allowable pointing error, the maximum attainable link range is 5.4km, 3.1km and 1.1km for haze, moderate rain and fog respectively at a constant bit rate of 2.5Gbps and the maximum attainable bit rate is 9Gbps, 6.5Gbps and 2.5Gbps for haze, moderate rain and foggy weather respectively at a constant link range of 2.2km for haze and moderate rain and 1.1km for fog.

This research investigates a dual-polished surface plasmon resonance sensor based on dual-core photonic crystal fiber, featuring an innovative design aimed at enhancing hemoglobin concentration detection in blood, providing a valuable tool for diagnosing numerous health issues, such as chronic obstructive pulmonary disease. The sensor makes use of an external sensing mechanism and utilizes gold (Au) coating as the plasmonic material, chosen for its strong plasmonic response and excellent chemical stability, ensuring robust performance across the 1.31–1.42 refractive index range. The electromagnetic characteristics and efficacy of the designed sensor were thoroughly investigated using the finite element method. Our proposed sensor demonstrates outstanding performance metrics, attaining peak amplitude sensitivity of about 734 RIU⁻¹, and wavelength sensitivity of 74,000 nm/RIU along with 1.35 × 10⁻⁶ RIU wavelength resolution. It also exhibits a notable Figure of Merit value of 667 for a corresponding Full width at Half Maximum value of 111 nm. Finally, a machine learning model based on linear regression was employed that enables the prediction of any hemoglobin concentration levels corresponding to analyte RI values. These exceptional performance metrics highlight the potential of our sensor as a reliable, cost-effective and highly sensitive solution for real-time biosensing applications.