Research Papers - Department of Electrical and Electronic Engineering

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Author: search.filters.author.Abhayasinghe, NStart date: 2020

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Now showing 1 - 5 of 5
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    PublicationOpen Access
    A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-07-17) Tennekoon, S; Wedasingha, N; Welhenge, A; Abhayasinghe, N; Murray, I
    Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the algorithmic development of a lightweight, vision-based doorway detection and alignment module with contextual awareness. It integrates channel and spatial attention, semantic feature fusion, unsupervised depth estimation, and doorway alignment that offers real-time navigational guidance to the wheelchairs control system. The model achieved a mean average precision of 95.8% and a F1 score of 93%, while maintaining low computational demands suitable for future deployment on embedded systems. By eliminating the need for depth sensors and enabling contextual awareness, this study offers a robust solution to improve indoor mobility and deliver actionable feedback to support safe and independent doorway traversal for wheelchair users.
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    PublicationOpen Access
    Domestic Energy Saver
    (SLIIT, 2022-02-11) Kumarasinghe, R; Abhayasinghe, N
    Most of the activities in the modern world are heavy power consumers. Therefore, wasting of power is identified as a critical factor which is badly impacted the environmental and economic growth. Using electrical appliances such as ceiling fans and bulbs in the commercial and domestic environment unnecessarily is identified as a main way of wasting power. As a solution, this paper proposes the designing and producing of a power saving device for ceiling fans and bulbs. Variables such as room temperature, light intensity and the number of people present in the location were considered as parameters to control ceiling fans and bulb automatically, so as to save power by changing the speed and turning off when not necessary. This device is consisting of six main units namely a main control unit using Arduino Nano microcontroller, bidirectional visitor counter unit using two Passive Infrared motion sensors, temperature and light intensity measuring unit using a LM35 temperature sensor and a light dependent resistor sensor, an output display unit using 1604 LCD display, ceiling fan controller unit using a fan regulator that was constructed using two RC circuits, a relay module and a bulb controller unit. Ceiling fan speed and the delay time to switch off the appliances is controlled using a fuzzy logic controller. Inputs of the fuzzy logic controller are temperature difference with the set temperature, rate of change of temperature difference within a five-minute interval, difference of the number of people inside the location with the set number of people and the rate of change of number of people within a five-minute interval. It was observed that, for the test cases, an average of 318Wh per day could be saved from a single bulb and a ceiling fan while avoiding unnecessary usage of appliances.
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    PublicationOpen Access
    Support Vector Machine Based an Efficient and Accurate Seasonal Weather Forecasting Approach with Minimal Data Quantities
    (SLIIT, 2022-02-11) Chandrasekara, S; Tennekoon, S; Abhayasinghe, N; Seneviratne, L
    Climate change makes a big impact in our daily activities. Therefore, forecasting climate changes prior to its actual occurrences is important. Even though highly accurate weather prediction systems throughout the world are available, they require mass amounts of data exceeding thousands of data points to obtain a significant accuracy. This study was aimed at proposing a Support Vector Machine based approach to carryout seasonal weather predictions up to thirty-minute intervals, the results of which would be considerably effective with respect to predictions carried out with models trained with annual datasets. The model was trained utilizing a dataset corresponding to the district of Kandy which consisted of 136 samples, 20 features, and 5 labels. By means of carrying out numerous data preprocessing steps, the model was trained, and the relevant hyperparameters were optimized considering the grid search algorithm to yield a maximum accuracy of 86%, once tested via the k-fold cross validation. The performance of the Support Vector Machine was also then compared for the same dataset with that of the K-Nearest Neighbor algorithm which consumed relatively fewer computing resources. An optimal accuracy of 61% was observed for this model for a K-value of 27. This approach supported the concept of a Support Vector Machine’s ability to perceive time series forecasts to a relatively higher degree and its ability to perform effectively in higher dimensional datasets with smaller number of samples. As per the future work, the Receiver Operating Characteristic analysis is proposed to be carried out to evaluate the performance of the model and the dataset size is proposed to be further enhanced to a maximum of a thousand samples to yield the best performance results.
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    PublicationOpen Access
    Low Cost – Remote Passive Sensory Based Weather Prediction System with Internet of Things
    (SLIIT, 2022-02-11) Tennekoon, S; Chandrasekara, S; Abhayasinghe, N
    Climate effects many major daily aspects of the society, from the food sources and transport infrastructure to the choice of fashion and certain daily routines. Due to these reasons, the demand for means to accurately foresee climatic changes have increased. Weather forecasting, especially in Sri Lanka, has been hampered due to numerous reasons and this has resulted in erroneous predictions that has adversely affected many areas of development ranging from agriculture, irrigation, and the tourism industry to certain branches of engineering. Many researchers have analyzed and proposed solutions to these problems. However, the need for accurate predictions prevails due to the hardship of accurate data acquisition, processing, and transmission. To address these problems, in this paper, a system that adheres to the rules and regulations set forth by the World Meteorological Organization (WMO) to carry out well informed and reliably accurate weather predictions based on the data attained from a wireless passive remote sensory medium has been implemented. This task was carried out by means of feeding the relevant climatic parameter readings measured via multiple wireless passive remote sensory nodes placed within the proximity of a considered area to a selected computational model, which in turn was implemented to yield considerably accurate predictions compared to the weather prediction systems currently available in the market. The paper comprises of the implementation of the category, Low-Cost Automatic Weather Station (LC-AWS) specified by the WMO and Internet of Things (IoT), one of the latest technologies, for the transmission of attained data even in the absence of Wi-Fi. The research was further conducted to perform an analytical comparison between highly accurate weather stations and the implemented low-cost weather station when compromising accuracy due to low cost. The hardware and related software implementation yielded an acceptable success rate and was concluded successfully.
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    PublicationEmbargo
    Human Gait Modeling, Prediction and Classification for Level Walking Using Harmonic Models Derived from a Single Thigh-Mounted IMU
    (MDPI, 2022-03) Abhayasinghe, N; Murray, I
    The majority of human gait modeling is based on hip, foot or thigh acceleration. The regeneration accuracy of these modeling approaches is not very high. This paper presents a harmonic approach to modeling human gait during level walking based on gyroscopic signals for a single thighmounted Inertial Measurement Unit (IMU) and the flexion–extension derived from a single thighmounted IMU. The thigh angle can be modeled with five significant harmonics, with a regeneration accuracy of over 0.999 correlation and less than 0.5◦ RMSE per stride cycle. Comparable regeneration accuracies can be achieved with nine significant harmonics for the gyro signal. The fundamental frequency of the harmonic model can be estimated using the stride time, with an error level of 0.0479% (±0.0029%). Six commonly observed stride patterns, and harmonic models of thigh angle and gyro signal for those stride patterns, are presented in this paper. These harmonic models can be used to predict or classify the strides of walking trials, and the results are presented herein. Harmonic models may also be used for activity recognition. It has shown that human gait in level walking can be modeled with a harmonic model of thigh angle or gyro signal, using a single thigh-mounted IMU, to higher accuracies than existing techniques.