Mohd Nazari Abu Bakar

The world demand of energy is increasing continuously, and solar energy is one of the renewable energies that can be utilized to fulfil the demand. Over the years, there are many researches that have been carried out on hybrid air solar collector. Hybrid-PV system or PV/T system is an integration of photovoltaic (PV) cells and solar thermal components. It is proven in many researches that combined PV and thermal system gives higher efficiency than individually working system. Therefore, this paper discusses the development of mathematical model of a photovoltaic/thermal (PV/T) solar collector. The effects of various parameters on the performance of the solar collector are also discussed. Since there is a drop of efficiency of the photovoltaic cells as the operating temperature increases, this study proposes a design of PV/T solar collector with an air-cooling system with a set of fins attached to improve its overall performance. A mathematical model representing the system by using one dimensional energy balance equations is developed. A simulation has been carried out by using MATLAB to determine the effect of parameters such as solar radiation and temperature on the performance of the solar collector. It is found out that when the temperature of the collector is reduced by removing heat through working fluid, its performance can be improved. Therefore, when air mass flow rate increases, the electrical and thermal efficiencies of the collector increase. Meanwhile, the collector efficiency also increases with the increase in solar radiation.

Edge detection is important in image analysis to form the shape of an object. Edge is the boundary between different textures, which helps with object segmentation and recognition. Currently, several edge detection techniques are able to identify objects but are unable to localize the shape of an object. To address this problem, this paper proposes a fusion of selected edge detection algorithms with mathematical morphology to enhance the ability to detect the object shape boundary. Edge detection algorithm is used to simplify image data by minimizing the amount of pixel to be processed, whereas the mathematical morphology is used for smoothing effects and localizing the object shape using mathematical theory sets. The discussion section focuses on the improved edge map and boundary morphology (EmaBm) algorithm as a new technique for shape boundary recognition. A comparative analysis of various edge detection algorithms is presented. It reveals that the LoG’s edge detection embedded in EmaBM algorithm performs better than the other edge detection algorithms for fruit shape boundary recognition. Implementation of the proposed method shows that it is robust and applicable for various kind of fruit images and is more accurate than the existing edge detection algorithms.