Research Publications

Now showing 1 - 3 of 3

Open Access
Highly Efficient 3D Object Transmission System for HTC Services in 6G Networks
(Faculty of Engineering, 2026-03) Svechnikov, D; Volkov, A; Marochkina,A; Muthanna, A; Kouhceryavy, A
In recent years, advancements in technology have brought forth a new frontier in visual communication. Holography is a technique that captures and reproduces three-dimensional (3D) images with an unprecedented level of realism and depth, has emerged as a groundbreaking method for conveying visual information. Unlike traditional images and videos, holography recreates scenes with full parallax, enabling viewers to perceive objects from various angles. The transmission of holographic images presents both exciting possibilities and unique challenges. To this end, this article conducts a comparative analysis of a previously developed application system for transmitting dynamic 3D human movements with a ready-made solution for transmitting 2D video streams in order to provide conference calling services. The network characteristics of the systems were collected and compared. The opportunities that programs currently provide and will provide in the future are examined.
Open Access
Enhancement of Images Under Low Light Conditions Using Artificial Intelligence
(Sri Lanka Institute of Information Technology, 2023-03-25) Marzook, M; Herath, M; Liyanage, M; Thilakanayake, T
Images taken in low light conditions do not contain all the information well-lit images contain. Various features including the colours of objects, details and the quality are lost. Extracting these features from images is very important for any kind of application of it. This study proposes a model to enhance the features of the image taken under low light conditions, by delivering a solution which improves the quality of the image through Artificial Intelligence. Through the proposed method, the clarity of the image is improved, making it closer to a well-lit image equivalent. Both Image Processing and Deep Learning based techniques are explored, including Convolutional Neural Network (CNN) based generative models. The Generative models considered are Autoencoders (AE) and Generative Adversarial Networks (GANs). The study has been carried out by using several datasets combined together, which include image pairs of well-lit and low light images. A comparison between the two CNN-based generative models is carried out. Through the study, it is quantitatively found, by the Structural Similarity Index and supported by the Peak Signal to Noise Ratio, that the proposed CNNbased Autoencoder model overrides the proposed CNN-based GAN model. This is further supported by qualitative observations of the image results. Both models, however, greatly enhance the low light images, bringing to light features that were not visible beforehand, and also provide results with good colour accuracy. Through this research study, the methods and solutions to enhance low light images have been addressed, as well as providing a comparison between two suitable models, Autoencoders and GANs. The proposed solution is able to address many of the limitations existing in the extent literature.
Open Access
Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka
(IEEE, 2023-01) Rathnayake, N; Rathnayake, U; Chathuranika, I; Dang, T. L; Hoshino, Y
The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFIS-based model outperforms the other algorithms. The correlation coefficient of each algorithm’s predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.

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