Faculty of Computing

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Author: search.filters.author.Amarasena, NHas files: Yes

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    CodeJr: Comprehensive Programming Application for Children
    (IEEE, 2022-12-09) Muthuthanthirige, M.D.C. J; Illangasinghe, U.P; Illangasinghe, D.N; Halgaswatta, I.; Samarakoon, U; Amarasena, N
    Since the beginning of the millennium, computer technology has been the key area of concern and developing essential programming knowledge and intellectual skills from the young age have proven that they will gain more success in their careers. The ideology behind this research is, the problem with absence of a complete multi-disciplinary and interactive programming application for children between the age of 10 - 15 years, to learn programming concepts with a well-established text-based programming language. There are 4 major approaches in this research. Gamification approach focuses on expressing knowledge about Python programming via a game while concentrating on low perfumers. Collaborative approach aims to deliver a brand-new experience for children by aggregating cooperative methodologies and Artificial Intelligence with learning to enforce mutual learning. This component is based on collaborative sessions which allow a group of students with similar interest to join to learn python programming. Drag-drop approach enables children to learn Python language through videos and will be given basic practice questions after finishing the course. Story telling approach guides children to learn programming concepts step by step using story telling. Focused on storytelling approach and interactivity via voice conversation to learn programming language for children.
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    License Detection and Accident Prevention System
    (IEEE, 2022-12-09) Dewmin, P.D.R; Yapa, P.S.H; Lokuge, S.D.; Pemadasa, M.G.N.M.; Amarasena, N
    According to census and statistics department of Sri Lanka, more than 30,000 accidents occurred from 2013 to 2019. More than two thousand are fatal in this time-period. When considering the top 10 causes of deaths in Sri Lanka, road accidents is at 10th place. Drink and drive, fatigue, drowsiness, distracted driving and driving without a valid license are root causes to these accidents. 20% of the accidents are caused by the drivers without a valid driving license. Currently there is no automated system built using IoT to verify driver’s license. A system to check drowsy driving, distracted driving and driver intoxication is also lacking in the society. By analyzing the data, smart license detection and accident prevention system was proposed to identify and validate driver’s license using RFID. The proposed system also facilitates sub-systems to check driver’s drowsiness, fatigue, alcohol level and driver distracted or not using Raspberry Pi camera module based on computer vision using TensorFlow Lite. An initialized sub-system detects the intensity of the brake pedal being engaged using pressure sensor. The system analyzes the pressure and indicate the intensity level accordingly using the brake light brightness. While these sub systems reduce the probability of occurring an accident, airbag detection sub system reduce the fatality rate of an accident by detecting the deployment of airbags and informing the nearest police station and hospital about an accident using GSM module and SMS Gateway API. The proposed system will reduce the number of accidents occurring throughout the year.
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    “Stonelia” – Prehistoric Stone Tool Identification Android App for Archaeological Researchers
    (IEEE, 2022-12-09) Perera, K. B. N.; Pathirana, M. K.; Wijayarathna, P. G. R. C.; Amarasena, N; Hewabathma, C. D.
    Prehistoric stone tools can be considered one of the oldest artifacts created by ancient humans. Lithic archeology’s study of stone tools provides important information about early humans’ technologies, agility, and mental and innovative abilities. A vital issue in lithic archeology is the identification and analysis of stone tools found at the excavation sites. Archeologists need to observe and analyze a stone tool under different aspects for a long time to verify whether it is a stone tool or a geofact, the techniques used to create it, and identify its rough relative date and functional value. This can be challenging for amateur scholars studying archeology since it requires a lot of experience and time to identify by a glance. As a solution, ‘Stonelia,’ a mobile-based android application, can be introduced to identify and analyze stone tools. The images captured through the mobile app are preprocessed using image processing. Using Convolutional Neural Network models identifies the stone artifact from a geofact, the mineral type, the rough relative date, techniques used to create the stone artifact, and its functional value. This mobile application provides prompt identification and analysis of stone artifacts within a short time and with higher accuracy.