SLTC Repository

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Welcome to the Institutional Repository of SriLanka Technology Campus (SLTC). The repository is a digital service that collects, preserves, and distributes the institution's scholarly output. It provides open access to research publications by the institution's academic staff and students.

The repository includes journal articles, conference papers, books, book chapters, theses and dissertations, undergraduate research, conference proceedings, Webinars, and other scholarly materials.

This repository aims to increase the visibility, accessibility, and impact of the institution’s research and preserve its intellectual output for future generations.

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Recent Submissions

Survey on Energy Harvesting in Smart Grid Networks
(Sri Lanka Technology Campus, 2020) Jayasinghe, D. H. G. A. E.; Jayakody, Dushantha Nalin K.
The recent development of internet of things devices and communication technology enhancement has been a driving force to the Smart Grid concept in recent power system de-ployments. The smart grid is capable of delivering power more reliably and efficiently and also respond to system changes and disturbances. Wireless sensor networks have been identified as the most promising technology in smart grid communication ar-chitectures. Though it is considered a promising method powering up the sensor node becoming a challenge due to the recharging changing of batteries. The paper explores the comprehensive characteristics of a smart grid followed by different energy harvesting techniques. The existing energy harvesting techniques are been discussed in two categories considering the sources of power. Then Wireless Power Transfer (WPT) and Simultaneous Wireless Information and Power Transfer (SWIPT) techniques which can use for energy harvesting concepts also have been explored. The paper then explored the related work which has been conducted for energy harvesting techniques and finally we explored future research direction considering cybersecurity issue with WPT/SWIPT and also incorporating WPT/SWIPT with the smart grid as an EH technique for sensor nodes. Index Terms—Smart Grid, Energy Harvesting, Wireless Com-munication, Wireless Power Transfer (WPT), Simultaneous Wire-less Information and Power Transfer (SWIPT)
Automatic Classification, Visualization and Analysis of Errors in Machine Translation
(Sri Lanka Technology Campus, 2020) Jayaweera, Chathuri; Dias, Gihan
Although the quality of machine translation (MT) has improved in recent years, machine translated documents still contain errors. MT quality is often evaluated using a single numeric score. However, this may not adequately characterise the system. We provide an error visualizer, which shows differences between corresponding lines of two translations. In addition to insertions, deletions and substitutions, our system also shows transpositions. We also provide an error analyzer which gives statistics of each type of error in the document. In addition, it shows errors in context: the words commonly adjacent to each error, and also the adjacent parts of speech (POS). This feature - unique to our system - allows the identification of the context in which errors occur, so they can be rectified easily. The system was evaluated by three MT system developers, who identified useful features and provided feedback which was used to improve the system.
Performance Analysis of the SLIPT Architectures
(Sri Lanka Technology Campus, 2020) Morapitiya, Sumali S.; Leelarathna, T. W. W.; Jayakody, Dushantha Nalin K.; Weerasuriya, R. U.
Recently, The study builds on Simultaneous Light wave Information and Power Transfer (SLIPT) has become a hot topic among the research community. The importance of the SLIPT is to harvest energy using light sources while decoding the information. In this approach, we present the mathematical framework for the Power Splitting (PS) based SLIPT system and study the performance of the PS-SLIPT and time switching (TS)-SLIPT architectures. Moreover, we quantitatively studied the harvested energy with different Field of View (FoV) angles of the Light Emitting Diode (LED) and the Photodiode (PD). In addition, we considered the amount of harvested energy for different Direct Current (DC) values. Overall, this paper con cludes that the FoV and DC bias signals are directly affected by SLIPT systems. Using numerical simulations, we demonstrated the performance of the both architectures to enhance the QoS of data rate, amount of harvested energy and trustworthiness of the information.
A Deep Learning Based Approach for the Classification of Diabetic Retinopathy in Human Retina
(Sri Lanka Technology Campus, 2020) Vimukthi, Yasodha; Kodikara, Nihal; Nanayakkara, Lakshika
Diabetic Retinopathy, a common diabetes complication causes damages to the blood vessels of light sensitive tissues in the human retina. Due to the limitations in the manual screening process, there exists a compelling requirement of an automated approach for the Diabetic Retinopathy screening which can be applied regularly and in abundance in any kind of a healthcare environment. This paper suggests a Deep Learning based automated approach to classify retinal fundus images into five major severity levels while focusing on achieving the optimal accuracy-efficiency balance in performance. In the classification task, a lightweight Convolutional Neural Network (CNN) model with only 6 convolutional layers was suggested to classify retinal fundus images to five major severity levels. CNN refinements such as Hyperparameter Tuning, Regularization and Data Augmentation were applied to increase the model accuracy. The suggested model achieved an Accuracy of 72.28%, a Sensitivity of 71.12% and a Specificity of 93.1% for a testing dataset of 267 retinal fundus images from Kaggle and Messidor-2 datasets. By comparing with four pre-trained CNN models VGG16, ResNet50, InceptionV3 and Xception, it was observed that the accuracy of the suggested model is slightly lesser than that of VGG16 and ResNet50 models. However, the number of FLOPs in the suggested model is 23 times lesser than VGG16 and 6 times lesser than ResNet50, indicating that the suggested model is more efficient than the mentioned pre-trained models. The accuracy of the suggested model can be further improved without increasing the number of FLOPs by increasing the number of training data samples
New Approach for Channel Encoding and Decoding Using (8,4) Extended Hamming Code
(Sri Lanka Technology Campus, 2020) Mushﬁck, M. M. M.; Puspatheepan, A.; De Silva, Dilanka
In this paper, the Channel Encoding and Decoding process is implemented using the (8,4) Extended Hamming code, in Quartus II 13.0 design software through VHDL program ming language and ModelSim-Altera software for simulating the circuit designs. With a minimum Hamming distance of four, Single-Error Correction and Double-Error Detection (SEC DED) can be achieved. Dissipation of power through heat and high processing time are the major shortcomings of using non reversible logic gates. The combinational logic gate designs are built using reversible logic to reduce dissipation of heat and processing delay. FPGAs are preferred over micro-controllers for the implementation of the designs, due to low signal processing latency and high parallel processing capability. The detection and correction of errors is achieved by following a new effective concept/algorithm to achieve SEC-DED at the decoder. The hardware implementation of the prototype was done using the Altera DE0-Nano FPGA board