Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo Sinhala Sign Language Interpreter Optimized for Real – Time Implementation on a Mobile Device(2021-08-11) Dhanawansa, V; Rajakaruna, TThis paper proposes a framework for a vision based Sinhala Sign Language interpreter targeted for implementation on a portable device, optimized for real-time use. The translator is aimed at enabling conversation between a hearing-impaired and a non-signing individual. The scope covers both static and dynamic signs, portrayed using the right hand. Skin segmentation and contour extraction followed by a combination of hand detection and tracking algorithms isolate the signing hand against varied background conditions. A Convolutional Neural Network model was developed to extract and classify the features of the chosen static signs. A standard, expandable dataset of Sinhala static signs was prepared for this task. Dynamic signs were modeled as a tree data structure using a sequence of static signs. The model was optimized using motion based temporal segmentation between consecutive signs, to minimize the processing overhead. The interpreter recorded an average accuracy of 99.5% and 81.2% on the static sign dataset, and combined dataset of static and dynamic signs, respectively. A response time of333 ms was resulted between the occurrence and prediction of a sign, demonstrating the effectiveness of the framework for real-time use.Publication Embargo Sinhala Sign Language Interpreter Optimized for Real–Time Implementation on a Mobile Device(IEEE, 2021-08-11) Dhanawansa, I. D. V. J; Rajakaruna, R. M. T. PThis paper proposes a framework for a vision based Sinhala Sign Language interpreter targeted for implementation on a portable device, optimized for real-time use. The translator is aimed at enabling conversation between a hearing-impaired and a non-signing individual. The scope covers both static and dynamic signs, portrayed using the right hand. Skin segmentation and contour extraction followed by a combination of hand detection and tracking algorithms isolate the signing hand against varied background conditions. A Convolutional Neural Network model was developed to extract and classify the features of the chosen static signs. A standard, expandable dataset of Sinhala static signs was prepared for this task. Dynamic signs were modeled as a tree data structure using a sequence of static signs. The model was optimized using motion based temporal segmentation between consecutive signs, to minimize the processing overhead. The interpreter recorded an average accuracy of 99.5% and 81.2% on the static sign dataset, and combined dataset of static and dynamic signs, respectively. A response time of333 ms was resulted between the occurrence and prediction of a sign, demonstrating the effectiveness of the framework for real-time use.Publication Embargo CEYLAGRO: Information Technological Approach for an Optimized and Centralized Agriculiture Platform(IEEE, 2020-12-10) Kaushalya, T. V. H; Wijewardana, B. Y. S; Karunasena, A; Kavishika, M. G. G; Gamage, S. T. A; Weerasinghe, LSri Lankan Agriculture sector can be considered as a crucial component as it contributes 18% of country GDP. As native farmers still cling to inapplicable traditional theorems and practices to track customer's vegetable consumption trends, they failed to assure a “good price” for their harvest. Also, the plants are prone to many diseases and pests' attacks which causes loss of the harvest. Unreliable problem identification, poor knowledge on application of fertilizers and pesticides have caused the farmers to lose their profits. As a solution to mitigate these problems, this study has built a computerized system with a vegetable price prediction system and a plant disease, pest identification system. Taking Potato as an example, the parameters of the time series model were analyzed through experiment and has built the price predictor using ARIMA model. Also, with advanced Image processing and CNN techniques Plant disease, pest identifier has built. Desirable results of the entire system have been achieved with more than 94%-97% rate of accuracy. The ultimate goal of this study is to achieve the optimal growth of the sector by navigating the users for a quality and effective decision making by reliable market trends and problem identification.