2021
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Publication Embargo Indoor Autonomous Multi-Robot Communication System(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Gunawardhana, K.D.W.; Kularathana, D.G.D.P.; Welagedara, W.H.; Palihakkara, H.E.; Abeygunawardhana, P.K.W.; Wellalage, S.Robotics, and automation systems are a hot issue right now. Controlling multiple robots at the same time has become very popular. On paper, we propose that a wireless robot-to-robot communication infrastructure be implemented to accomplish some specific tasks. The major goal of this proposed project is to showcase communication infrastructure, a dual manipulator system and a mobile charging dock robot have been designed to achieve this. Special topics and services were employed for communication infrastructure. It is more precise than current communication systems. Existing manipulation situations are limited to a single manipulator task; however, in this case, a dual manipulator task has been designed to work corporately. The charging docking stations are the only places where mobile robots may recharge. We presented a Mobile Charging Dock for recharging mobile robots in this project. This proposed project is introducing a secure communication strategy which uses a ROS topic filtering mechanism.Publication Embargo Digital Preservation and Noise Reduction using Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Aravinda, K.P.; Sandeepa, K.G.H.; Sedara, V. V.; Chamodya, A.K.Y.L.; Dharmasena, T.; Abeygunawardhana, P.K.W.This paper proposes a digital preservation solution for Sinhala audios to conserve those as documents with noise reduction. The solution has implemented multiple noise reduction techniques as a pre-processing step to remove unwanted internal and external noises. A two-step, two-way noise reduction process is applied to produce clean audios based on Deep Convolutional Neural Network (DCNN) and adaptive filter-based techniques. This approach implements two separate noise reduction models for internal and external noises. After that, the speech recognition decoder recognizes the speech and converts it to a Unicode document by acoustic, language, and pronunciation models using extracted audio features from the denoised audio. Further, noise reduction models are decoupled from the preservation solution and exposed as a sub solution for multilingualism noise reduction, supporting English and Sinhala audios.