Research Papers - Dept of Computer Systems Engineering
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Publication Embargo A Secure and Intelligent Smart Home Controlling System(IEEE, 2022-12-26) Hettiarachchi, M. C; Abeysiriwardhana, W. D.C; Aththanayaka, A. M. R. E; Panangala, L. D; Jayawardena, C; Udara, IAs a result of technological progress, people’s standard of living has improved and is continuing to do so in a variety of ways. Automation is the central idea in contemporary technology, and increasingly, automation technologies are taking the place of human operators. There are currently 4.8 billion people using the internet, or 60% of the global population, and around 87500 people join the internet every single day. The concept of an interconnected network of devices known as the “internet of things” expanded swiftly along with the expansion of the internet user population. The proliferation of IoT-enabled smart home gadgets is largely responsible for this shift. Although technology has advanced, it is still entirely dependent on human input. In addition, the current crop of smart home control systems is narrowly focused, mostly on a handful of security-related tasks such as monitoring, management, and the like. Research into existing solutions revealed that end customers wished for a less complex, more accessible, and cheaper method of controlling their smart homes; hence, we propose a smart home control system that meets these needs while being affordable and user-friendly. This paper presents cutting-edge investigation into the concept of a smart house, which can not only be used to manage appliances but also to open and close gates based on the user’s GPS location and to minimize energy consumption by automatically switching on and off equipment. Furthermore, the produced solution has increased security by implementing a biometric authentication system, a mobile application has been developed on the android platform, and user testing has shown the solution’s significance and validated the proof of concept.Publication Open Access Design, Implementation, and Performance Evaluation of a Web-Based Multiple Robot Control System(Hindawi, 2022-05-30) Rajapaksha, U. U. S; Jayawardena, C; MacDonald, B. AHeterogeneous multiple robots are currently being used in smart homes and industries for different purposes. The authors have developed the Web interface to control and interact with multiple robots with autonomous robot registration. The autonomous robot registration engine (RRE) was developed to register all robots with relevant ROS topics. The ROS topic identification algorithm was developed to identify the relevant ROS topics for the publication and the subscription. The Gazebo simulator spawns all robots to interact with a user. The initial experiments were conducted with simple instructions and then changed to manage multiple instructions using a state transition diagram. The number of robots was increased to evaluate the system’s performance by measuring the robots’ start and stop response time. The authors have conducted experiments to work with the semantic interpretation from the user instruction. The mathematical equations for the delay in response time have been derived by considering each experiment’s input given and system characteristics. The Big O representation is used to analyze the running time complexity of algorithms developed. The experiment result indicated that the autonomous robot registration was successful, and the communication performance through the Web decreased gradually with the number of robots registered.Publication Embargo Adding Common Sense to Robots by Completing the Incomplete Natural Language Instructions(IEEE, 2022-07-18) De Silva, G. W. M. H. P.; Rajapaksha, S; Jayawardena, CThis system is developed to identify and complete the human’s instructions or incomplete sentences given by a user as a command. It would facilitate the interaction between the human and mobile service robots. However, when humans give the instruction, there can be incompleteness or else missing the information related to the environment. That is because humans, generally based on common sense, depending on the environment. Then the human brain can complete all those incomplete sentences by using common sense knowledge. This paper itself introduced a model of a service robot who can compete with the given incomplete instructions, display the related sentences or words, and finally move to the related objects in the environment. First, it will consider and identify the objects in the environment and then consider the given natural language instruction by humans. As a first step of the approach, complete the incomplete sentences. Those sentences are coming as natural language instructions. By parsing it into as the frame can identify the related words by using the created model or can call as language model and here used some identify words from the human common sense also, then the service robot will learn about the commonsense knowledge automatically from the parsing sentences as a speaker. Considering all the parsing sentences, it calculates and measures the accuracy of this service robot model. Simply this is a commonsense reasoning model. The result of the provided solution can enable the robot model that works in a ROS environment to identify and automatically perform the tasks.Publication Embargo Identifying Objects with Related Angles Using Vision-Based System Integrated with Service Robots(IEEE, 2022-07-18) Pasindu Lakshan, K. K; Rajapaksha, U. U. S; Jayawardena, CManipulation of an object can be done with the collaboration of a human to a robot by properly introducing the object. To do this easily, we can model the object inside the robot’s head and let the robot identify it using some sensors and cameras. But when it comes to the real world, robots should have some mechanism to recognize objects in a perspective frame with angels. In this research authors will present a strategy to identify the unknown objects using a vision-based system and with the perspective angles of the detected object and the system is integrated with service robots. This will go in a way when the robot should be able to identify the objects around the robot in an asynchronous manner with rotational angles and the pitch and roll angles, perspective to the robot standing surface. The research will be based on Artificial intelligence, Machine learning, and Robotics.For the identification process, a few ways can be used. Vision-based identification using color and depth images from an RGB camera, and this research is mainly based on this RGB and depth feature integrated with YoloV5. And there are some other ways to identify objects like using LiDAR laser scanner. However, this learning process, should have a stable object to model and train the object. After the object recognition, by using the proposed methodology robots can calculate and estimate the rotational angles and pitch and roll angles of an object.Publication Embargo Artificial Intelligence Based Smart Library Management System(IEEE, 2021-12-01) Jayawardena, C; Reyal, S; Kekirideniya, K. R; Wijayawardhana, G. H. T; Rupasinghe, D. G. I. U; Lakranda, S. Y. R. MThe concept of a smart library system is to operate in a library with minimal human intervention. The proposed system handles basic library functions such as issuing books, returning books, reserving books, collecting late return fines, antitheft detection, and managing booking inventory using IoT technologies such as RFID and Raspberry Pi. The first key criterion is shelves management, which provides a unique key and uses RFID technology to identify and arrange the books. The succeeding category is providing users with book recommendations by penalizing hidden layer activations to encourage only a few nodes to activate when a single sample is an input. The dimensionality reduction neural network method was used to select the optimal seating arrangement. The LSTM algorithm will be used to make predictions to provide an efficient service to library users.Publication Embargo Learning platform for visually impaired children through artificial intelligence and computer vision(IEEE, 2018-02-19) Balasuriya, B. K; Lokuhettiarachchi, N. P; Ranasinghe, A. R. M. D. N; Shiwantha, K. D. C; Jayawardena, CThe topic Visual Disabilities and Computer Vision are the most researched topics of recent years. Researchers have been trying to combine two topics to create most usable systems to the visually disabled to aid them in their day to day tasks. In this research, we are trying to create an application which is targeting children between the age of 6-14 who suffers from visual disabilities to aid them in their primary learning task of learning to identify objects without a supervision of a third-party. We are trying to achieve this task by combining latest advancements of Computer Vision and Artificial Intelligence technologies by using Deep Region Based Convolutional Networks (R-CNN), Recurrent Neural Networks (RNN) and Speech models to provide an interactive learning experience to such individuals. The paper discusses.Publication Embargo TreeSpirit: Illegal logging detection and alerting system using audio identification over an IoT network(IEEE, 2018-02-19) Kalhara, P. G; Jayasinghearachchi, V. D; Dias, A. H. A. T; Ratnayake, V. C; Jayawardena, C; Kuruwitaarachchi, NIllegal logging has been identified as a major problem in the world, which may be minimized through effective monitoring of forest covered areas. In this paper, we propose and describe the initial steps to build a new three-tier architecture for Forest Monitoring based on Wireless Sensor Network and Chainsaw Noise Identification using a Neural Network. In addition to detection of chainsaw noises, we also propose methodologies to localize the origin of the chainsaw noise.Publication Open Access Forecasting accuracy of Holt-Winters Exponential Smoothing: evidence from New Zealand.(New Zealand Journal of Applied Business Research, 2020) Dassanayake, W; Ardekani, I; Jayawardena, C; Sharifzadeh, H; Gamage, NFinancial time series is volatile, dynamic, nonlinear, nonparametric, and chaotic. Accurate forecasting of stock market prices and indices is always challenging and complex endeavour in time series analysis. Accurate predictions of stock market price movements could bring benefits to different types of investors and other stakeholders to make the right trading strategies. Adopting a technical analysis perspective, this study examines the predictive power of Holt-Winters Exponential Smoothing (HWES) methodology by testing the models on the New Zealand stock market (S&P/NZX50) Index. Daily time-series data ranging from January 2009 to December 2017 are used in this study. The forecasting performance of the investigated models is evaluated using the root mean square error (RMSE], mean absolute error (MAE) and mean absolute percentage error (MAPE). Employing HWES on the undifferenced S&P/NZX50 Index (model 1) and HWES on the differenced S&P/NZX50 Index (model 2) we find that model 1 is the superior predictive algorithm for the experimental dataset. When the tested models are evaluated overtime of the sample period we find the supportive evidence to our original findings. The evaluated HWES models could be employed effectively to predict the time series of other stock markets or the same index for diverse periods (windows) if substantiate algorithm training is carried out.Publication Embargo Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction(IEEE, 2019-12-05) Aryal, S; Nadarajah, D; Kasthurirathna, D; Rupasinghe, L; Jayawardena, CForecasting the financial time series is an extensive field of study. Even though the econometric models, traditional machine learning models, artificial neural networks and deep learning models have been used to predict the financial time series, deep learning models have been recently employed to do predictions of financial time series. In this paper, three different deep learning models called Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Temporal Convolution Network (TCN) have been used to predict the United States Dollar (USD) to Sri Lankan Rupees (LKR) exchange rate and compared the accuracy of the models. The results indicate the superiority of CNN model over other models. We conclude that CNN based models perform best in financial time series prediction.Publication Embargo Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction(2019-12-05) Nadarajah, D; Aryal, S; Kasthurirathna, D; Rupasinghe, L; Jayawardena, CForecasting the financial time series is an extensive field of study. Even though the econometric models, traditional machine learning models, artificial neural networks and deep learning models have been used to predict the financial time series, deep learning models have been recently employed to do predictions of financial time series. In this paper, three different deep learning models called Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Temporal Convolution Network (TCN) have been used to predict the United States Dollar (USD) to Sri Lankan Rupees (LKR) exchange rate and compared the accuracy of the models. The results indicate the superiority of CNN model over other models. We conclude that CNN based models perform best in financial time series prediction.