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In this paper, we present the development and testing of an optical-based sensor for monitoring the corrosion of reinforcement rebar. The testing was carried out using an 80% etched-cladding Fibre Bragg grating sensor to monitor the production of corrosion waste in a localized region of the rebar. Progression of corrosion can be sensed by observing the reflected wavelength shift of the FBG sensor. With the presence of corrosion, the etched-FBG reflected spectrum was shifted by 1.0 nm. In addition, with an increase in fringe pattern and continuously, step-like drop in power of the Bragg reflected spectrum was also displayed.

This paper reports the design, characterization and implementation of a Fiber Bragg Grating (FBG)-based temperature sensor for an Insulted-Gate Bipolar Transistor (IGBT) in a solar panel inverter. The FBG is bonded to the higher Coefficient of Thermal Expansion (CTE) side of a bimetallic strip to increase its sensitivity. Characterization results show a linear relationship between increasing temperature and the wavelength shift. It is found that the sensitivity of the sensor can be categorized into three characterization temperature regions between 26 °C and 90 °C. The region from 41 °C to 90 °C shows the highest sensitivity, with a value of 14 pm/°C. A new empirical model that considers both temperature and strain effects has been developed for the sensor. Finally, the FBG-bimetal temperature sensor is placed in a solar panel inverter and results confirm that it can be used for real-time monitoring of the IGBT temperature.

Ripe oil palm fresh fruit bunch allows extraction of high-quality crude palm oil and kernel palm oil. As the fruit ripens, its surface color changes from black (unripe) or dark purple (unripe) to dark red (ripe). Thus, the surface color of the oil palm fresh fruit bunches may generally be used to indicate the maturity stage. Harvesting is commonly done by relying on human graders to harvest the bunches according to color and number of loose fruits on the ground. Non-destructive methods such as image processing and computer vision, including object detection algorithms have been proposed for the ripeness classification process. In this paper, several object detection algorithms were investigated to classify the ripeness of oil palm fresh fruit bunch. MobileNetV2 SSD, EfficientDet (Lite0, Lite1 and Lite2) and YOLOv5 (YOLOv5n, YOLOv5s and YOLOv5m) were simulated and compared in terms of their mean average precision, recall, precision and training time. The models were trained on a dataset with four main ripeness classes: ripe, unripe, half-ripe, and over-ripe. In conclusion, object detection algorithms can be used to classify different ripeness levels of oil palm fresh fruit bunch, and among the different models, YOLOv5m showed promising results with a mean average precision of 0.842 (0.5:0.95).