Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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Subject: search.filters.subject.Algorithm

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Now showing 1 - 5 of 5
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    PublicationEmbargo
    Image Enhancement Algorithm for Remote Surveillance Using Low Cost Cameras in Unevenly Illuminated Environments
    (IEEE, 2021-12-07) Dharmasena, T; Abeygunawardhana, P. K. W
    The surveillance domain is becoming increasingly popular due to the demand for security and safety. In terms of the data being used, the cameras linked to them play an essential role. However, in poorly lit environments, the acquired image may appear to be underexposed when the camera adjusts itself to the light available in the scene, resulting in a loss of visual information in poorly lit regions. This could result in poor functionality in image based processing, as well as a negative impact on remote surveillance monitoring activities. This study offers a novel algorithm for improving the illumination of scenes that are unevenly lit using a selective pixels processing method to enhance the illumination of the environment. Rather than processing grayscale versions of red,green and blue channels to enhance the image, this approach uses value (v) channel of an HSV image.
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    PublicationOpen Access
    Virtual student advisor using NLP and automatic appointment scheduler and feedback analyser
    (www.ijser.org, 2016-02-02) Suvethan, N; Avenash, K; Huzaim, M. A. Q; Mathusagar, R; Gamage, M. P. A. W; Imbulpitiya, A
    Virtual Student Advisor is a research project that mainly concerned on addressing a comprehensive solution to overcome the difficulties faced by the academic departments of any academic institutions. According to the context, the role of the Student Advisor is focused on helping students with problems related to their academic carrier at the University and also answer the general queries made by students related to the procedures conducted at the Institute. This happens to be a tedious and a very inefficient task for the Academics as they have to repeat the same answers for many students and also students coming to meet the academic in ad hoc manner without proper appointments makes both the student and the academic face lot of problems. The Virtual Student Advisor system mainly consists of three components; A Natural language based inquiry management module, Priority wise automatic appointment scheduler and Feedback analyzer. The inquiry management module is responsible in handling user queries based on frequently asked questions. Users can get the answers for a query by entering the question in natural language. This is implemented as a mobile application as it is convenient for students to ask questions from any place at any time. The system will answer the queries and if it needs more explanation it will be directed to the relevant advisor. Auto appointment scheduler handles the student appointment requests and helps in managing the schedules. This will be accessed by both Academics to set their free time slots and the students to request an appointment according to urgency. Feedback analyzer handles the entire process of student feedback taken for each subject starting from preparing feedback forms till analyzing the collected information. The lecturer can prepare a feedback form using the question bank in the system and customize it further to fit the requirements and after conducting the feedback the system can process the data and provide reports on varies views of the data such as tables and pie charts. The system uses Natural Language Processing to handle students’ queries by tokenizing the sentences and extracting answers based on keywords and comparing synonyms WordNet lexical database. The appointment scheduler uses priority based Round Robin CPU Scheduling Algorithm to schedule the appointments according to urgency. The Virtual Student Advisor system consists of a mobile application for students and a web application for both students and lecturers to access the information and conducted their daily tasks related to academic administration effectively.
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    PublicationEmbargo
    Evolutionary Algorithm for Sinhala to English Translation
    (IEEE, 2019-10-08) Nugaliyadde, A; Joseph, J. K; Chathurika, W. M. T; Mallawarachchi, Y
    Machine Translation (MT) is an area in natural language processing, which focuses on translating from one language to another. Many approaches ranging from statistical methods to deep learning approaches are used in order to achieve MT. However, these methods either require a large number of data or a clear understanding about the language. Sinhala language has less digital text which could be used to train a deep neural network. Furthermore, Sinhala has complex rules, and therefore, it is harder to create statistical rules in order to apply statistical methods in MT. This research focuses on Sinhala to English translation using an Evolutionary Algorithm (EA). EA is used to identifying the correct meaning of Sinhala text and to translate it into English. The Sinhala text is passed to identify the meaning in order to get the correct meaning of the sentence. With the use of the EA the translation is carried out. The translated text is passed on to grammatically correct the sentence. This has shown to achieve accurate results.
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    PublicationEmbargo
    Tempcache: A Database Optimization Algorithm for Real-Time Data Handling in Indoor Spatial Environments
    (IEEE, 2018-08-08) Jayakody, A; Murray, I; Hermann, J; Lokuliyana, S; Dunuwila, V. R
    The unstable arrangement of modern indoor environments has made navigation within buildings a difficult task. Hence, this paper introduces the AccessBIM framework, which is an efficient real-time indoor navigation system that facilitates in generating a real-time indoor map by crowdsourcing spatial data through the sensors available in mobile devices of navigators. The framework is equipped with a database optimization algorithm known as “Tempcache” which reduces the time and cost of searching data by examining the AccessBIM database for previously navigated paths, thus enabling faster data retrieval through efficient query processing. A simulation of a virtual environment similar to an actual indoor environment was used to test the algorithm. The significance of the algorithm was validated by comparing the total map generation time before and after the algorithm was applied for which the results demonstrated a reduction in map generation time with the use of the algorithm. The framework is also capable of capturing localization information with the support of i-Beacons which is then stored in a cloud server.
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    PublicationOpen Access
    An algorithm for energy driven cluster head rotation in a distributed wireless sensor network
    (Colombo, 2005-12-15) Gamwarige, S; Kulasekere, E. C
    Wireless sensor networks (WSNs) consists of large number of inexpensive, low-power, sensors that can be placed in an ad hoc fashion to form a communication network. Efficient techniques for inter sensor communication and information gathering is critical for prolonging the lifetime of the sensor network. Clustering provides an effective way for extending the lifetime of a sensor network. In this paper we propose a distributed and energy driven clustering algorithm where the cluster heads are selected based on relative residual energy level of sensors. Furthermore the cluster head candidacy selection phase, and the cluster head candidacy rotation among phases is triggered only when any of cluster heads energy drops below a dynamic threshold computed by the algorithm. As a result, the overheads in the inter sensor communications will be reduced and thereby the proposed algorithm will favor more powerful nodes over the weaker ones to prolong the lifetime of the entire sensor network in both homogeneous and heterogeneous sensor networks. The results have shown that the the proposed algorithm performs better when compared to existing algorithms such as LEACH, SEP, HEED and ANTCLUST based on the Percentage Node Alive (PNA) and the First Node Dies (FND) metrics.