Microring resonator (MRR)-based channel dropping filters have been extensively explored because of the high quality factor, compact size, and easy integration of fabrication. In order to design an excellent MRR wavelength filter, optimization of the design parameters are essential. In this paper, the design trade-off of MRR-based channel dropping filter was statistically studied by employing the Taguchi method. Four control factors considered were width of rings and channels, radii of the microring, upper rib waveguide height, and gap size. The analysis of variance was adopted to analyze significant trends that occurred on the free spectral range (FSR) and insertion loss (IL) performance under different sets of control factor combinations. The best parametric combination of control factors was identified in order to achieve a balance performance between large FSR and low IL using Finite-Difference Time Domain (FDTD) simulation by RSoft Inc. After optimization, the value of FSR and IL obtained was 17 nm and 0.245 dB, respectively. Confirmation tests were carried out to verify the optimized parametric combinations and a new parametric combination considering both outputs were 16 nm and 0.215 dB. The optimal combinations were 6 mm ring radius with the separation gap of 50 nm and 350 nm  350 nm rib waveguide cross section.

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Nowadays, ester‐based transformer oil has been an interest in high voltage transformers as an insulating and cooling medium. In‐service transformer oil is subjected to electrical and thermal stresses, leading to degradation process of the oil. In this work, a tapered single mode‐multimode‐single mode (SMS) optical fiber structure was used to detect transformer oil aging based on the change in oil refractive index (RI). An optical sensor was fabricated by splicing technique and chemical etching. For comparison, five samples of transformer ester oils were examined. The results show that high‐level‐aging transformer oil possesses a greater RI than fresh transformer oil. A correlation analysis was conducted among sensor output signal, oil absorption spectrum test, and breakdown voltage test. The performance of the sensor demonstrated good sensitivity and linearity of 20.41 nm/RIU and 95.95%, respectively. The single mode‐multimode‐single mode sensor was easily fabricated, low cost and could be used for real‐time high voltage transformer monitoring.

In this article, fiber optic sensor (FOS) has been employed for food composition detection by using a Mach–Zehnder Interferometer (MZI) structure. The Single Mode–Multimode–Single Mode configuration with varied length of the sensing region with 4 cm and 8 cm were designed and analyzed. The FOS has been developed by using a fusion arc splicing technique. The sensor device has been tested with the major components of food compositions such as water, 1.0 mol sucrose solution and oil with the refractive index (RI) of 1.333, 1.384, and 1.464, respectively. The sensor device experienced a wavelength redshift as the RI increases. Meanwhile, it is also observed that the sensitivity of FOS is linearly proportional to the length of the sensor region. The best sensitivity achieved is 4.413 nm/RIU for 8 cm length of sensor region. The findings of this article can serve as a reference for improved sensitivity by manipulating the sensor's length.

In this paper, a polymer optical fiber (POF) is applied for temperature measuring of water solution using a light source of 650nm. The aim is to analyse the impact of temperature variations on the output of POF sensor device in terms of output power and sensitivity. From the study, the POF sensor shows a linear trend when the temperature is increased from 30°C to 80°C with the sensitivity of the polymer optical fiber for output power to the temperature are 0.00973 dBm/°C or 0.14797 nW/°C, for optical characterization and 0.0011 V/°C for electrical characterization. The integration of the Internet of Things to the system helps the user to monitor the temperature of various spaces anywhere at any time. The sensed values are controlled by Arduino Uno R3 and then sent to Blynk to provide wireless monitoring by the user.