Browsing by Author "Jayasinghe, Jeevani"

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    A Miniaturized Printed Dipole with Non-Uniform Meander Lines for Ultra High Frequency – Radio Frequency Identification Passive Tags
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Wickramasinghe, Shyama; Jayasinghe, Jeevani; Senadeera, Melaka
    A non-uniformly meandered printed dipole antenna is proposed to be used as a passive Ultra High Frequency – Radio Frequency Identification (UHF RFID) chip-based sensor tag. A printed, modified dipole antenna, with an overall size of 130 × 25 × 1.6 mm3 was used as a reference antenna, and its size was reduced by 32% by folding dipole arms and employing a non-uniformly meandered structure. A double T-match structure was introduced to match the conjugate impedance of the RFID chip, i.e. Rocky100. Simulations were carried out in ANSYS High-Frequency Structure Simulator (HFSS), etching the antenna on FR4 substrate. The meandered antenna, with an overall dimension of 88 × 25 × 1.6 mm3, exhibits better performance than the reference antenna. The bandwidth of the antenna covers the whole UHF spectrum from 860 – 960 MHz while exhibiting an omnidirectional radiation pattern. The theoretical read range of the tag according to the Friis transmission equation is 10.6 m at EIRP of 4 W. The proposed tag can be used to develop chip-based passive UHF RFID sensor tags by integrating sensors to the Rocky100 chip.
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    Relationships between climatic factors to the paddy yield in the North-Western Province of Sri Lanka
    (Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Wickramasinghe, Lasini; Jayasinghe, Jeevani; Rathnayake, Upaka
    Climate variation is one of the major impacting issues for paddy cultivation. It also highly impacts the harvest. Therefore, many researchers try to understand the relationships between climatic factors and harvest using numerous methods. Sri Lanka is still titled as a country with an agricultural-based economy and thus identifying the impact of climate variability on agriculture is very important. However, previous studies reveal a little information in the context of Sri Lanka on the impact of climate variabilities on agriculture. Therefore, this study showcases an artificial neural network (ANN) framework; that is an ordinary machine learning algorithm based on the model of the human neuron system, to evaluate the relationships among the climatic components and the paddy harvest in the North-Western province of Sri Lanka. This on-going study helps to analyze the relationships between the paddy harvest of the North-Western province and climate, including rainfall minimum atmospheric temperature and maximum atmospheric temperature. Correlation coefficient (R) and mean squared error (MSE) are used to test the performance of the ANN model. The results obtained from the analysis revealed that the predicted and real paddy yields have a significant correlation with rainfall, maximum temperature and minimum temperature.