Browsing by Author "Perera, D"

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Embargo
The advanced remote PC management suite
(IEEE, 2011-08-16) Wijekoon, J; Wijesundara, M; Dassanayaka, T; Samarathunga, D; Dissanayaka, R; Perera, D
Developing a system that helps system administrators to perform their administration task more effectively and efficiently is of great importance to reduce downtime, cost and man power requirement. The Advanced Remote PC Management Suite facilitates centralized management of PC infrastructure employing the Intel Active Management Technology (AMT). This technology enables the system administrators to monitor and manage computers via a dedicated channel regardless of whether the computer is powered on. This is known as Out-of-Band (OOB) management. Currently AMT is available in Desktops and Laptops with The 2nd generation Intel Core vPro processors. Using features of AMT, the The Advanced Remote PC Management Suite provides a real-time and intelligent asset management facility in addition to monitoring and administration capabilities. The system also features automated operating system deployment and centralized disk cloning mechanisms. It is also possible to isolate any computer in the network using the system, during incidents such as virus infections. Therefore, this system is able to drastically reduce the number of desk-side-visits by system administrators to setup and troubleshoot PCs in large enterprise networks.
Open Access
Challenges of Mobility and Access to Transport for People with Visual Impairment and Blindness: An Exploratory Study
(Emerald Publishing, 2022-12-01) Suraweera, T; Bandara, S; WickramaArachchi, C; Perera, D
The ability to move around to get things done to fulfill one’s wants, and needs is critical for independent living, irrespective of his or her age or existence of impairments or disabilities. Safe and efficient mobility with confidence is widely recognized as influential factors of the positive wellbeing of persons with visual impairment and blindness. Inability to confidently move around pushes persons with visual impairment and blindness to a risk of social exclusion. Even though research into this area is significant, such research in developing countries like Sri Lanka is quite limited. While aiming at bridging the said knowledge gap, this research intended to explore challenges and obstacles of mobility and transport experienced by people with visual impairment and blindness in Sri Lanka. This study adopted a qualitative method through in-depth interviews. Fifteen persons with visual impairment and blindness were purposively chosen to represent a wide range of social and economic boundaries and interviewed in May-August 2021. With the prior consent of respondents, interviews were recorded and transcribed. The interview transcriptions were analyzed using a thematic approach. Data triangulation was done through focus group discussion and observation methods. The results confirmed that persons with visual impairment and blindness confronted challenges and obstacles in mobility and transport that is discussed in six themes: deficient infrastructure, access to information, improper attitudes of sighted persons, service provider issues, personal attitudes, and support. The findings urge the importance of implementing the existing provisions while introducing relevant public policies and social awareness.
Embargo
Computer Vision for Autonomous Driving
(IEEE, 2021-12-09) Kanchana, B; Peiris, R; Perera, D; Jayasinghe, D; Kasthurirathna, D
Computer vision in self-driving vehicles can lead to research and development of futuristic vehicles that can mitigate the road accidents and assist in a safer driving environment. By using the self-driving technology, the riders can be roamed to their destinations without using human interaction. But in recent times self-driving vehicle technology is still at the early stage. Mostly in the rushed areas like cities it becomes challenging to deploy such autonomous systems because even a small amount of data can cause a critical accident situation. In Order to increase the autonomous driving conditions computer vision and deep learning-based approaches are tended to be used. Finding the obstacles on the road and analyzing the current traffic flow are mainly focused areas using computer vision-based approaches. As well as many researchers using deep learning-based approaches like convolutional neural networks to enhance the autonomous driving conditions. This research paper focused on the evaluation of computer vision used in self-driving vehicles.
Embargo
Computer-Vision Enabled Waste Management System for Green Environment
(IEEE, 2021-12-09) Hewagamage, P; Mihiranga, A; Perera, D; Fernando, R; Thilakarathna, T; Kasthurirathna, D
Waste management has become a critical requirement to maintain a green environment in Sri Lanka as well as other countries. Town councils have to regularly collect different types of wastes to clean cities/towns. Hence managing the waste of the cities is a challenging task. However, most of the urban councils currently use a manual approach to managing waste. However, it results in many difficulties for the people and cleaning staff who involve in the process by following strict guidelines. Issues due to waste contamination, no proper information management of waste collection, and no punctuality in removing waste from the garbage bins are some of the significant issues arising from the manual process. Due to the drawbacks of the manual approach, social issues, environmental issues, health issues can occur easily. This paper proposes a better solution to replace this manual system with an automated system to overcome these issues. Hence, the main objective of this research is to introduce an ICT-based innovative design that can be used to develop an effective waste management system in town councils. In the proposed model, we will introduce a Computer Vision-based smart waste bin system with real-time monitoring that incorporates various technologies such as computer vision, sensor-based IoT devices, and geographical information system (GIS) related technologies. Our proposed solution consists of a waste bin system, which is capable of automated waste segregation. Our design facilitates the admin users to expand the waste bin kit by adding more waste categories in a user-friendly manner, making our product adaptive in any environment. At the same time, waste bins can notify the real-time waste status. Our system generates the optimum collection routing path and displays it in a mobile app using those real-time status details. We also demonstrate a low-cost prototype.
Embargo
Contact Tracing Of Covid-19 Patients Using Tweets
(IEEE, 2022-02-23) Perera, D; Bamunusinghe, J
Covid-19 has had an impact on everyone’s lives in the recent past. Presently the field of healthcare uses social media as a tool for professional education and communication. The benefits and drawbacks of these networks have been widely discussed in different research papers. Due to the Covid-19 pandemic, the biggest problem that the government is facing is identifying the close contacts of the Covid-19 patients. Most of the people avoid revealing the truth about the places they visit and people they met in the recent past to the Covid-19 controlling bodies. But people use social media in their day to day life to post/ share their life experiences. Some people use twitter to share their experiences related to Covid-19. In this research paper we focus on tracing the Covid-19 close contacts using tweets. The proposed approach creates a dataset using a twitter API and filters the covid-19 positive users using sentimental analysis. After filtered positive users from the dataset, we have used a set of keywords to filter individual users’ tweets and then we have applied name entity recognition to identify the connected people and places. After gathering each tweet user’s information, we have visualized the relevant relationships of each close contact in a network diagram. Our proposed model indicates 75% accuracy by tracing down Covid-19 positive users and close contacts.
Embargo
COVID 19 Navigator Taxi Application for Urban Mobility during Pandemic Period
(IEEE, 2022-02-23) Wickramarathne, J; Perera, D; Kanchana, B. C; Peiris, R
This research paper focuses on increasing the awareness between taxi users and can help to protect themselves in COVID-19 and take precautions. Since 2019 the global pandemic of Covid-19 is spreading at an astonishing rate and causes a negative impact for economic, social, and cultural factors. Government agencies are warning people to reduce the transportation and to maintain social distance. To stop the spread of COVID-19 required to identify persons who are susceptible to infection and need to trace the COVID-19 positive first contacts. Most people are reduced to using public transportations and taxi services due to unidentifiable health conditions in earlier users. The proposed approach can be used to track the taxi drivers and their passengers previous COVID-19 status as well as navigate the safest route by showing the COVID-19 contamination areas. By using this approach users can be aware of earlier users of the taxi service and COVID-19 status of the taxi driver or passenger before taking the trip as well as if they get touch with any COVID-19 patients, can take immediate precautions. This application helps to increase the usage of taxis by making users trust and confidence against COVID-19 infection. By testing with real users our system was able to trace down 45 passengers and 16 drivers within 3 months.
Embargo
A hybrid tool to combine multi-objective optimization and multi-criterion decision making in designing standalone hybrid energy systems
(Elsevier, 2013-07-01) Amarasinghage, T; Perera, D; Attalage, R. A; Perera, K. K. C. K; Dassanayake, Vishwanath P C
Hybrid energy systems (HESs) are becoming popular for standalone applications due to global concern regarding green house gas (GHG) emissions and depletion of fossil fuel resources. Research in the optimal design of HESs is ongoing, with numerous optimization techniques giving special emphasis to Pareto optimization, incorporating conflicting objectives. The subsequent decision-making process including the non-dominant set of solutions has yet to be addressed. This work focuses on combining multi-objective optimization with a multi-criterion decision making (MCDM) technique to support decision makers in the process of designing HESs. Four different objectives, i.e., levelized energy cost (LEC), unmet load fraction, wasted renewable energy (WRE) and fuel consumption are used to obtain the Pareto front. A decision support tool based on Fuzzy TOPSIS and level diagrams is proposed to analyze the Pareto front and support the subsequent decision-making activity. A case study is used to illustrate the applicability of the proposed method. The study shows that the novel method is useful when determining the relative weights of objectives, providing a detailed picture of the objective space to the designer when coming up with the optimum system. The technique proposed in this study can be further extended to analyze similar problems in energy system design where MCDM is necessary after multi-objective optimization.
Open Access
A hybrid tool to combine multi-objective optimization and multi-criterion decision making in designing standalone hybrid energy systems
(Elsevier, 2013-07-01) Amarasinghage, T; Perera, D; Attalage, R. A; Perera, K. K. C. K; Dassanayake, V. P. C
Hybrid energy systems (HESs) are becoming popular for standalone applications due to global concern regarding green house gas (GHG) emissions and depletion of fossil fuel resources. Research in the optimal design of HESs is ongoing, with numerous optimization techniques giving special emphasis to Pareto optimization, incorporating conflicting objectives. The subsequent decision-making process including the non-dominant set of solutions has yet to be addressed. This work focuses on combining multi-objective optimization with a multi-criterion decision making (MCDM) technique to support decision makers in the process of designing HESs. Four different objectives, i.e., levelized energy cost (LEC), unmet load fraction, wasted renewable energy (WRE) and fuel consumption are used to obtain the Pareto front. A decision support tool based on Fuzzy TOPSIS and level diagrams is proposed to analyze the Pareto front and support the subsequent decision-making activity. A case study is used to illustrate the applicability of the proposed method. The study shows that the novel method is useful when determining the relative weights of objectives, providing a detailed picture of the objective space to the designer when coming up with the optimum system. The technique proposed in this study can be further extended to analyze similar problems in energy system design where MCDM is necessary after multi-objective optimization.
Embargo
Individualized Edutainment and Parent Supportive Tool for ADHD Children
(IEEE, 2020-12-10) Thennakoon, A; Perera, D; Sugathapala, S; Weerasingha, S; Samarasinghe, p; Dahanayake, D; Piyawardana, V
Attention-Deficit/Hyperactivity Disorder (ADHD) is a comorbid disorder that can impact a child and his/her family. ADHD children have considerable obstacles in managing time, understanding instructions, and paying attention to the activities. To address these perplexities, this research has designed a mobile application to help parents to have better interaction with the children and for the children to enjoy their learning activities. The specialty of this application is the models are trained on individual child skills and needs. Issues with time management are handled by the Scheduler component while the Instruction Predictor module supports the parent in recognizing the child's understandability level. Furthermore, the children are provided with edutainment activities based on their attention and ability levels. Different models have been used in predicting the results through these modules and the prediction result accuracy exceeds 90% in most of the cases. Out of the many models, The Random Forest model resulted in the best overall performance. The application was tried by many parents and health professionals and received satisfactory and commendable reviews.
Open Access
IOT Based Smart Microgreen Sprouter
(Springer, Cham, 2022-01) Rankothge, V; Kehelella, P; Perera, D; Kanchana, B. C; Peiris, R; Madushan, K
This research paper focuses on enhancing indoor farming technologies with an emerging technology: Internet of Things (IoT). The proposed approach creates a microgreen sprouter unit that automates the process of monitoring and providing optimum growing conditions with the minimum human supervision. This model can be used to achieve healthy growth of microgreen by providing ideal ventilation, moisture, humidity, light, and temperature levels, which prevents sprouts from ultraviolet radiation and pest attacks. Users can track the growth rate of sprouts and change the moisture, humidity, light and temperature levels. Our prototype implementation has been tested for mung-beans sprouts and validated for its accuracy and efficiency
Embargo
Optimum Music: Gesture Controlled, Personalized Music Recommendation System
(IEEE, 2021-12-09) Wijekoon, R; Ekanayaka, D; Wijekoon, M; Perera, D; Samarasinghe, P; Seneweera, O; Peiris, A
Music plays an important role in everyone’s life since it helps to relax the mind when appropriate music is played. This paper presents a music recommendation system based on the user’s current emotions, activities as well as demographic information such as age, gender, and ethnicity. In addition, the system can be controlled by hand gestures and vocal commands. Unsupervised learning methods in were used to recommend music according to the demographic data and emotions of the user. Finally, the important idea is to recommend music based on all of the user’s data, such as demographics, emotions, and activities. The overall system performance was manually tested and evaluated with a group of individuals, yielding a 70% satisfaction rate for the recommendation; additionally, supporting models such as demographic identification, emotion identification, and hand gesture identification have received a higher proportion of accuracies, contributing to the research’s success. Unlike other systems, ours utilizes all of the user’s information while making music recommendations.
Embargo
Review On Hand Gesture Recognition for Bengali Sign Language
(IEEE, 2022-02-23) Perera, D; Kanchana, B. M; Peiris, R; Madushan, K; Kasthurirathna, D
Communication becomes difficult when interaction between the disabled and the general public are required. People with disabilities of various races communicate using various sign languages. For persons who are deaf or hard of hearing sign language is their primary mode of communication. However, the majority of our community does not understand sign language, taking them out in public is incredibly challenging. In order to make sign language understandable to the general public, computer vision-based methods are now widely used. Recognition of hand gesture is one of the computer vision based technologies for recognizing sign language, and it is attracting a lot of attention from analysis. For a long time, it has been a popular research area. In the area of hand gesture recognition in computer vision, some recent research has achieved outstanding improvements by employing deep learning techniques. In this paper we have discussed the previous research methods, technologies, datasets and models used in Bengal sign language gestures that are interconnected in terms of achieving a successful result. Therefore, this review article tried to reveal the independent techniques which are used to overcome the challenges in research.
Embargo
Review On Hand Gesture Recognition for Bengali Sign Language
(IEEE, 2022-04-14) Perera, D; Kanchana, B; Peiris, R; Madushan, K; Kasthurirathna, D
Communication becomes difficult when interaction between the disabled and the general public are required. People with disabilities of various races communicate using various sign languages. For persons who are deaf or hard of hearing sign language is their primary mode of communication. However, the majority of our community does not understand sign language, taking them out in public is incredibly challenging. In order to make sign language understandable to the general public, computer vision-based methods are now widely used. Recognition of hand gesture is one of the computer vision based technologies for recognizing sign language, and it is attracting a lot of attention from analysis. For a long time, it has been a popular research area. In the area of hand gesture recognition in computer vision, some recent research has achieved outstanding improvements by employing deep learning techniques. In this paper we have discussed the previous research methods, technologies, datasets and models used in Bengal sign language gestures that are interconnected in terms of achieving a successful result. Therefore, this review article tried to reveal the independent techniques which are used to overcome the challenges in research.
Embargo
SMART Garbage Bin Kit Expandable and Intelligent Waste Management System using Deep Learning and IoT for Modern Organizations
(IEEE, 2021-12-02) Hewagamage, P.; Perera, D; Thilakarathna, T; Kasthurirathna, D; Fernando, R; Mihiranga, A
According to published statistics, Sri Lanka produces garbage around 7000MT per day, and every organization directly contributes this national amount depending on the waste management practices. 'Waste contamination' is a critical issue that affects waste management, and it should be addressed during the garbage collection process. This has led to environmental hazards resulting in health and other social issues. Hence, it is a responsibility of an organization to separate the garbage during the collection process using a suitable technique. In this paper, we are proposing a smart garbage bin kit that automates the separation of garbage collection, which minimizes human error using AI-based technologies. IoT-based devices connected to a smart garbage bin kit guide the user to the correct bin. At the same time, our proposed system can be easily expanded for new special waste categories as well. The other important issue of the current garbage management is improper time management of the garbage removal process in organizations. This happens due to the lack of real-time data on waste bins, and collection is based on the fixed time interval irrespective of the status and location of garbage bins. In the proposed system of SMART Garbage Bin Kit, the group of all interconnected garbage bins is monitored in real-time to identify the optimum collection path considering the location and the status of garbage bins using an optimized algorithm. Hence, the study presented in this paper integrates several intelligent approaches together with IoT based network to build a cutting-edge device, declared as SMART Garbage Bin kit. The prototype system has been built as a part of the research study to demonstrate its feasibility and sustainability.
Embargo
Sustainable tourism: Application of optimization algorithms to schedule tour plans
(IEEE, 2019-01-31) Perera, D; Rathnayaka, C; Siriweera, L; Dilan, S; Rankothge, W
One of the challenging problems in the tourism industry is to maintain the environmental sustainability of the tourists attracted locations while giving a better user experience for the tourists. The proposed platform for sustainable tourism management system consist with following modules: A prediction module to predict an approximate value on tourist arrival for each location, an optimization algorithm module to decide the number of tourists that can be accommodated in each location considering the environmental sustainability, and an optimal path generating module to show the best route to each location. The optimization algorithm module is developed to decide the number of tourists for each location based on two approaches: Genetic Algorithms and Iterated Local Search. Next the optimal path generating module is developed based on traveling salesman problem.In this paper, the performances of the optimization algorithm module and the optimal path generating module is presented. Results show that, using the suggestions given by the algorithms help the tourist to enjoy a better experience in travelling while ensuring the sustainability in the tourism industry.