Browsing by Author "Tennekoon, S"

Now showing 1 - 5 of 5

Open 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.
Open Access
Fostering Communication and Social Engagement in Quiet Learners Within the Classroom: A Case Study
(School of Education, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Vayathmi, O.K.P.; Tennekoon, S
This study investigated some of the barriers faced by students when participating in the teaching learning process actively, such as social anxiety, lack of confidence, or difficulty in communication. The study focused on a student in a Grade 6 classroom who consistently remained silent, avoided discussions, group work, and peer interactions. This behavior not only affected the student’s learning but also influenced the classroom dynamics. Recognizing the importance of inclusive education, we conducted action research to identify the reasons behind the student’s silenceand to develop strategies to encourage his engagement through structured peer collaboration activities and lowpressure strategies. These strategies aimed to build confidence, foster connections with classmates, and create a supportive environment where the student felt safe to contribute. The efficacy of the strategies was assessed through teacher observations and by comparing the student’s participation before and after the intervention was implemented. Findings revealed that small-group interactions and non-verbal participation methods helped the student to gradually increase his engagement in the classroom activities. Over time, his willingness to communicateimproved, positively influencing classroom inclusivity. This study highlights the need to address silent learners who are often overlooked in traditional teaching approaches. By identifying individual challenges and adapting classroom practices, educators can ensure that all students feel valued and empowered. Therefore, this research underscores the importance of fostering an inclusive learning environment where every student’s voice can be heard.
Open 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.
Open Access
Object Recognition and Assistance System for Visually Impaired Shoppers
(Sri Lanka Institute of Information Technology, 2023-03-25) Tennekoon, S; Abhayasinghe, N; Wedasingha, N
Shopping is indeed effortless for many individuals. However, it could certainly be a struggle and chaotic experience for the visually impaired. Visual impairment causes many societal stigma and inconvenience to visually impaired individuals. Although shopping may sound extremely easy, this is a crucial social activity for many visually impaired (VI) individuals. Visually impaired (VI) shoppers always require assistance when shopping for product identification purposes. This may lead to greater inconvenience as delays, lack of information and product familiarity of shop assistants may occur. Therefore, allowing visually impaired shoppers to independently perform shopping regardless of size and position of the shopping mall is essential. This encourages them to participate in enhanced social activities and perform their daily chores in independence. Although many products have been developed to assist visually impaired shoppers at shopping malls, due to their drawbacks, some of these have seem to undergo failures in producing accurate information to the visually impaired shopper for object identification and caused inconvenience. This project proposes a feasible solution for visually impaired shoppers to perform their shopping at ease and independently. Object recognition has been made possible in order to identify garment items while shopping with no assistance of another individual. The Convolutional Neural Network (CNN) has been used to obtain a sufficiently good accuracy and precision with a validation accuracy of 90%. Some of the novel techniques such as Ensemble Modelling has also been performed in order to reduce any generalization errors of the prediction and achieve a greater accuracy while overcoming all of the drawbacks of the currently existing products in the market. The overall product is proposed to attain maximum consumer population of visually impaired shoppers with satisfaction, reliability, and low cost.
Open 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.