PhD

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Theses and Dissertations of the Sri Lanka Institute of Information Technology (SLIIT) are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

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Open Access
The Role of Social Capital and ICTs in Inter-Organizational Collaboration in a Developing Economy: An Empirical Study of the Finance Industry in Sri Lanka
(Curtin University, 2017-09) Nawinna, Dasuni Priyanwada
In the contemporary world of business, organizations cannot rely solely on their internal strengths to survive. Forming inter-organizational partnerships is becoming one of the most popular strategies available to an organization to share risks, resources and other capabilities with partners. Collaborative business strategies are especially beneficial in the emerging economies where organizations are constrained with lack of resources, technology, skills and infrastructure. Accordingly, explaining why and how some organizations do better in inter-organizational relationships (IORs) than others is a dominant challenge in the study of IORs. Social capital (SC) is an influential concept in understanding why and how some organizations do better in inter-organizational relationships. It is recognized as an important factor in developing relationships of trust, forming the foundation for greater collaboration and successful collective action. Social capital is a multi-dimensional, relational concept that turns into a powerful tool when combined with the network analysis approach and tools to study inter-organizational relationships such as alliances and joint ventures or collaborations of any form. While social capital has been found to support different firm-level value creations, such as creation of intellectual capital, resource exchange, innovation, knowledge sharing and performance, it has significance as the basis for the development of stakeholder relationships, which are essential to Corporate Social Responsibility (CSR). CSR is touted as a key enabler of both organizational performance and of sustainable development, which are also essential for developing economies. Information Systems (IS) researchers have increasingly become interested in exploring social capital in relation to Information and Communications Technologies (ICT). It is evident that social capital and ICT are mutually complementary in the interorganizational-level. While the role of social capital in the development or acceptance of ICTs and the role of ICTs in the formation of Social Capital is widely explored, the combined effect of SC and ICT on the IOR in developing contexts remains unexplored. Very little is known about the effect of ICT enabled Social Capital in the inter-bank context. The aim of this empirical research is to develop a model of how ICT-enabled social capital affects inter-bank strategic collaboration in a developing context, Sri Lanka. The purpose of this study is to investigate how the multiple dimensions of social capital influence the strategic collaboration in the Sri Lankan banking context, and the enabling role of ICTs. In order to accomplish this objective, the researcher uses quantitative techniques, the structural modelling approach combined with network measurements. Data is gathered through a survey of high-level management of banks and from public sources such as annual reports and web sites. The network analysis tools (e.g. ORA) and the statistical analysis methods (PLS-SEM) and tools (e.g. SmartPLS) have been used to derive results. The results of this study suggest that structural, relational dimensions of social capital have a positive influence towards the degree of strategic collaboration of banks. It is also evident that higher ICT capabilities at the firm-level strengthen the effect of cognitive social capital on collaboration. The results of the other moderation tests indicate that firm-size, age, gender-ratio of directors, ownership, geographic spread, culture, organization structure and previous experience strengthen the effect of social capital on strategic collaboration. The results of further analysis indicate that the structural social capital is influential for the corporate social responsibility of banking organizations. Both the inter-organizational collaboration and the corporate social responsibility yield higher financial performance at the firm-level. The study also provides evidence that the use of network measurements as the indicators of social capital provides better predictability in comparison to regular indicators. These findings provide a valuable contribution to the theory of social capital, literature on ICT for development and and network theory, contributing to a more holistic perspective that incorporates social, technical and organizational aspects and provides insights useful for building effective strategies in similar developing contexts.
Open Access
Development of Real-Time, Self-Learning Artificial Intelligence-Based Algorithms for Non-Intrusive Energy Disaggregation in a Multi-Appliance Environment
(Faculty of Engineering Sri Lanka Institute of Information Technology, 2023-12) Herath, M
Electricity serves as a cornerstone in modern economies, with demand in residential and commercial sectors rapidly increasing in recent years. Enabling real-time monitoring of individual appliance-wise energy consumption and delivering user feedback is essential for future energy conservation initiatives. Energy disaggregation becomes imperative in furnishing consumption statistics for individual appliances. The acquisition of appliance-specific energy consumption in a non-intrusive manner, without the need for sensors on each device but by utilizing readings from the main household energy meter, highlights Non-Intrusive Load Monitoring (NILM) as a promising solution. NILM, leveraging the capabilities of smart meters and advancements in computational power, gains popularity for its effectiveness in disaggregating and analyzing energy consumption patterns. This study introduces an Artificial Intelligence (AI)-based NILM solution capable of disaggregating the energy consumption of multiple appliances while adapting to new appliances and their evolving behaviors. Among various NILM approaches, Neural Network (NN)-based models demonstrate promising disaggregation capabilities. However, the selection of the most suitable NN type or architecture poses a challenge due to the multitude of approaches in literature. To address this issue, the study standardizes and compares different NNs, with results showing that the Convolutional Neural Network (CNN) exhibits superior prediction accuracy and speed. This study also investigates the impact of different appliances and their consumption profiles on disaggregation performance, rigorously testing parameters such as NN architecture, input-output mapping topologies, data preprocessing, and hyperparameters. This leads to the development of guidelines for future NILM studies. Additionally, the study introduces a hierarchical plug-and-play modular-based model for appliance anomaly detection, extending the application of NILM and overcoming limitations in anomaly detection literature. This study investigates two-dimensional (2D) input-based NILM solutions for predicting appliance energy consumption profiles and classifying appliances. Unlike conventional NN-based models using 1D signals, representing the aggregate energy signal as a 2D image improves performance by leveraging feature extraction capabilities of NNs and preserving vital temporal information and signal amplitude relationships. Various TSS to 2D image conversion methods for NILM were tested, including Gramin Angular Summation Field (GASF), Gramin Angular Difference Field (GADF), Recurrent Plot (RP), and Markov Transition Field (MTF), with GADF outperforming other methods. In addition, the study introduces a simple yet powerful 2D input mechanism for time series data, specifically energy consumption data. This mechanism will be integrated into a CNN-based energy disaggregation model for the first time in the NILM domain, with the aim of improving overall performance. While the proposed method excels over 1D input-based models in training, it is observed that the novel 2D input method requires augmentation in training data volume, data mixing, NN depth, and hyperparameter tuning to achieve superior generalization capabilities. Furthermore, aggregate energy signal-based Voltage-Current (V-I) trajectory plots were investigated for fully non-intrusive appliance classification, demonstrating high accuracy. v The study proposes a single NN architecture named "One-Shot." This model exhibits the capability to simultaneously disaggregate multiple appliances, offering a more efficient alternative to the intricate and computationally demanding existing NN-based NILM models that necessitate separate NNs for each appliance. The efficacy of this approach is evaluated across multiple input-output mapping configurations, with the multi-point multi-bin model proving superior. To address challenges associated with manual model re-training for new appliances and adapting to evolving consumption patterns, a self-learning module is incorporated, enhancing the performance of the OneShot model. To overcome issues related to excessive hyperparameter tuning and insufficient training data, the study presents an unsupervised model based on Blind Source Separation (BSS), utilizing Independent Component Analysis (ICA) to separate appliance energy signals from the aggregate signal. Developing more reliable disaggregation models in local environments requires a local energy dataset. For this purpose, the study creates a local energy dataset from households using a custom-designed data logger, capturing both low and high-frequency energy data at appliance, circuit, and main energy meter levels. This dataset is verified using the One-Shot model developed in this study. In summary, this study advances the field of NILM by introducing AI-based solutions, innovative approaches, and comprehensive guidelines. Ultimately, these contributions aim to foster energy conservation and enhance efficiency in residential and commercial settings globally.
Open Access
Climate Policy Assessment for Climate Change Mitigation and Carbon Neutrality: A Case Study of Sri Lanka
(Department of Mechanical Engineering Sri Lanka Institute of Information Technology, 2023-12) Fernando, G.L
Climate change is one of the most significant challenges faced by mankind in the 21st century. Human activities, particularly in the energy supply and demand sectors, primarily cause an increase in greenhouse gas (GHG) emissions. The Paris Agreement's climate goal aims to limit global warming to a level well below 2°C above pre-industrial levels, with a specific target of limiting temperature rises to 1.5°C by the end of this century. Therefore, there has been an emphasis on achieving large-scale reductions in GHG emissions from the energy sector. After the initial stocktake in 2023, it is apparent that global emission pathways are not meeting the expected progress toward the Paris Agreement targets. Swift actions are necessary to readjust these pathways. Consequently, the reduction of greenhouse gas emissions in developing economies will be pivotal in reaching the desired global temperature targets. This study examines the case of Sri Lanka, a developing economy with low carbon intensity, to explore the role of similar economies in acachievingthe Paris targets Sri Lnaka has has a population of 22 million and a GDP of 84.5 billion USD in 2021. The predicted economic growth in the future could result in a rapid increase in energy demand in the country. This could result in an increase in fossil fuel use and subsequent carbon emissions. Sri Lanka has pledged to mitigate 14.5% of the GHG emissions conditionally and unconditionally by 2030 compared to its 2021 levels through its Nationally Determined Contributions. However, it aspires to achieve ambitious targets like carbon neutrality by 2050. Moreover, it also tries to increase the share of renewable energy in electricity generation from 45% in 2021 to 70% in 2030. However, it needs a pragmatic plan to facilitate a smooth transition towards reducing these emissions. A systematic analysis of different policy options and scenarios is required to determine a suitable policy for reducing GHG emissions. In doing so, Energy-EconomicEnvironmental models can provide the basis for such analysis. The development v of such models for Sri Lanka and the carrying out of scientific studies are still at an early stage. This thesis covers the analysis of different scenarios for climate change mitigation using an energy-economic-environmental model in the case of a developing economy with low carbon intensity. The scientific questions to be answered in this study are: 1) How is the energy environmental system of an developing economy modeled considering both energy consumption and supply sectors? 2) What is the impact of carbon taxes on reducing carbon emissions? 3) How could energy, economic, and environmental models be used to analyse climate futures? 4) What scenarios will lead the country to carbon neutrality? 5) How do efficient technologies, renewable energy sources, cleaner fuels, nuclear energy, carbon capture and storage technologies, and green hydrogen for power generation reduce emissions? 6) What are the marginal abetment costs of CO2 reduction for proposed emission mitigation actions? 7) What impacts do low-carbon scenarios have on energy security? 8) What are the other co-benefits of CO2 mitigation? The first objective of this study is to develop a bottom-up type of energy system model for a developing economy with low energy intensity. Sri Lanka has chosen as a case study, considering the economic and demographic factors to assess energy use and its environmental implications during a given period. This model comprehensively assessed the integrated reference energy system, encompassing energy supply and demand sectors during a planning horizon. It used a recursive dynamic cost optimization approach, minimizing the energy system's total cost each year during the planning period from 2015 to 2050. The AIM/Enduse model, a part of the Asia Pacific Integrated Modeling family, was used to develop an energy system model for the Sri Lankan energy sector. It considered a Business-As-Usual scenario (BAU) and other scenarios for achieving large-scale reductions in CO2 emissions. The BAU scenario assumes existing economic, demographic, and social trends throughout the modeling period. It assumes the continuity of current policy measures across all five energy sectors throughout the modeling period. According to the model results, the total vi primary energy supply in the BAU scenario is expected to increase almost threefold, from 11 Mtoe in 2015 to 34 Mtoe in 2050. The CO2 emissions associated with energy use will increase from 19 Mt in 2025 to 66 Mt in 2015 at an average annual growth rate of 7%. The increase in CO2 emissions is attributed to the use of fossil fuels, as their share is expected to increase from 53% in 2015 to 66% in 2050. The results indicate that if there is no policy intervention, the share of fossil fuels will continue to increase, resulting in a significant increase in CO2 emissions. The second objective of this study is to examine the impact of carbon taxes on achieving large-scale emissions reductions in the energy sector. It employed five carbon tax trajectories proposed by the MESSAGE-GLOBIOM Integrated Assessment Consortium to achieve five levels for the global mean temperature. These targets will be achieved by imposing five different carbon tax trajectories ranging between 2.3 US$/tCO2 and 436 US$/tCO2 in 2050. The reference scenario for Sri Lanka was assumed to be in the middle of the road pathway defined in the Shared Socioeconomic Pathways. According to the model results, CO2 emissions at these carbon tax levels could be reduced by 25% to 60% by 2050. It also has other benefits, such as reduced primary energy supply and final energy consumption by 2050. Nevertheless, the research findings imply that aggressive carbon mitigation measures and taxes are required to achieve significant emission reductions in developing economiew. One of the main objectives of this study was to develop scenarios for achieving carbon neutrality by 2050. It defined four countermeasures: namely, plausible, ambitious, challenging, and stringent scenarios involving the level of intervention on the energy demand and supply sides. These scenarios considered different technology options and policy measures, such as the diffusion of efficient technologies, the availability of renewable energy sources, the use of cleaner fuels, nuclear energy, carbon capture and storage technologies, and green hydrogen for power generation. The results of this study revealed that a stringent scenario that includes aggressive policy measures in both the energy supply and vii demand sectors, use of renewable energy for power generation, diffusion of efficient end-use devices, fuel switching, increasing the share of electric cars, and public transport achieves a near carbon-neutral scenario at a carbon tax trajectory of 32 US$/tCO2 in 2020 and 562 US$/tCO2 in 2050. The net energy import dependency will decrease to 13% in 2050 compared to the BAU scenario (65%) under the near carbon neutral scenario, which is a positive outcome from the energy security perspective. The fourth objective of the study was the development of future emission pathways and the estimation of energy and environmental implications for different emission pathways using the model. The fifth IPCC assessment report analysed the energy system and related emissions under five shared socioeconomic pathways representing possible climate futures. These pathways include SSP1: Sustainability Pathway, SSP2: Middle of the Road Pathway, SSP3: Regional Rivalry Pathway, SSP4: Inequality Pathway, and SSP5: Fossil-fueled Development Pathway. The findings of this study reveal that the SSP5, which reflects rapid economic growth, higher utilisation, inefficient and traditional enduse technologies, firm reliance on abundant fossil fuel resources, and a lower level of awareness of sustainability and the environment in the future, will provide the highest primary energy supply of 44.6 Mtoe in 2050. The lowest primary energy is recorded under the SSP4, and it was 26.5Mtoe in 2050. The CO2 emissions in 2050 were highest under SSP5 with 107Mt and lowest under SSP1 with 24Mt in 2050. Out of all scenarios, SSP5 had the highest energy intensity with 6MJ/US$ and a carbon intensity of 0.25kg/ US$ in 2050. The SSP1, which characterized a sustainable pathway, resulted in a primary energy consumption of 27Mtoe and 17Mt CO2 emissions in 2050. It developed different climate futures that could provide valuable insights into how energy and emissions change. The final objective of this study is to analyse the co-benefits of carbon reduction and to estimate the marginal abatement cost of CO2 reduction. This study examined the co-benefits of reducing CO2 emissions under these emission viii reduction targets. The co-benefits analysed include a reduction in primary energy supply, net energy import dependency, energy security, and the level of local air pollutants (NOx and SO2). Six different indices collectively define the country's energy security, including the diversity of primary energy demand, non-carbon fuel share, renewable fuel share, oil share, primary energy intensity, and carbon intensity. Mitigating 90% of CO2 emissions compared to BAU will result in 21% of net energy import dependency. It also provided a 1.8 Shannon index for the diversity of primary energy demand, indicating a higher diversity of energy types. Meeting this reduction target would result in carbon intensity levels of 0.01kg/US$ and energy intensity levels of 2.4MJ/US$ in 2050, representing approximately a 90% and 80% reduction, respectively, compared to 2015 levels. This study also analysed the economic costs of reducing CO2 emissions and developed sector-level marginal abatement cost curves. These play a critical role in deciding policy options for reducing CO2 emissions. Five countermeasure scenarios, with CO2 emission reduction targets between 10% and 90%, were used to develop marginal abatement cost curves. According to sectorial marginal abetment cost curves, the most economical CO2 emission mitigation option would be introducing efficient and hybrid road vehicles, using efficient residential technologies such as refrigerators and air conditioners, and biomass for residential cooking. The highest mitigation potential will be possible by introducing electric buses for public transport and large-scale wind and solar energy generation. The study's findings indicate that aggressive policies introducing clean energy and efficient technologies are required to reduce large-scale CO2 emissions. Renewables (solar and wind) and nuclear energy for power generation will significantly reduce emissions. Considering the limitations in land availability, biomass is expected to play a limited role. In addition, it would require efficient end-use devices, switching to alternative fuels such as liquified LNG, using electric cars, and expanding public transport. Nevertheless, it would bring additional advantages such as improved energy security, reduced energy imports, and ix reductions in the levels of local air pollutants. Reducing emissions will require a marginal abatement of carbon for Sri Lanka, which will vary from 197USD/tCO2 to reduce 10% to 1792USD/tCO2 to reduce 90% by 2050. The results indicate that the marginal abatement cost for CO2 reduction is higher than the global average for developing conomies
Open Access
An Autonomous Multiple Robot Registration and Control System: Design Implementation and Performance Evaluation
(2022-11) Rajapaksha, U. U. S. K
ROS is the most prominent middleware used by most researchers in robotic application development. Our research mainly depends on ROS technologies because most researchers currently work with ROS as middleware for many research projects. Controlling the robots through the Web interface is essential. Because in some instances, users may not be able to communicate with the robot directly because of some bad conditions in the environment where the robots are currently placed. Therefore, we have developed a Web interface to control all robots through the Internet. However, the ROS topics, nodes, and message formats used to subscribe and publish can differ from one robot to another when we work with multiple robots in the same environment. Therefore, when a user expresses high-level instructions through a Web interface, all multiple robots must understand instructions uniformly and take necessary actions accordingly without considering each robot’s internal software and hardware implementation. The first contribution of the research is to develop an algorithm to register all robots based on the main components of the ROS technology through the Web interface autonomously. The robot Registration Engine was developed with algorithms to complete the autonomous robot registration task. The second contribution is identifying the relevant ROS topics and nodes for each action when a user command gives through the Web interface. The ROS topic identification algorithm was developed successfully. The third contribution was to evaluate the system performance under different conditions and derive the equations for the delay in response time through the web interface, validating the equations derived. We have conducted several experiments to evaluate our system with delays in response time. The worst-case analysis was completed for all algorithms with Big O notation. Users and researchers can utilize Robot Registration Algorithm and ROS Topic Identification Algorithm to work with multiple robots through the Web interface. We have successfully implemented all algorithms in a simulated environment in Gazebo.
Open Access
Realtime line parameter estimation using synchrophasor measurements and impact of sampling rates
(Wichita State University, 2016) Hettiarachchige-Don, A. C. S
The installation of synchrophasor measurement units within the electrical grid system have provided utilities with the ability to monitor their transmission system in real time. These real time observations allow for better situational awareness and rapid responses to adverse system conditions. However, the real time impedance of the powerline is not one of the parameters that is transmitted to the control center and therefore, has to be calculated using the data received from multiple devices. This thesis proposes a simplified methodology for this analysis that requires lower computation power in comparison to most other proposed estimation techniques. Hence, this methodology is able to produce accurate results faster and by using a smaller quantity of stored data. Due to these reasons, this methodology can be implemented to provide near real time estimation and reporting of impedance values. For the purposes of this research, only the reactance information will be calculated but a similar approach can be used to obtain resistance information as well. The methodology consists of an algorithm to calculate and estimate the reactance of a line using the reported PMU data. It includes an outlier detection and elimination algorithm as well as a denoising technique that makes use of regularized least square estimation to accurately estimate the reactance over the analysis period. The methodology proposed is tested using real synchrophasor measurement data from a utility provider. The proposed mythology can easily be adapted and applied for the estimation and calculation of other parameters using PMU data.
Open Access
Dynamic line parameter estimation using Synchrophasor Measurements
(Wichita State University, 2021-05) Hettiarachchige-Don, A. C. S
The worldwide push towards a more intelligent, connected and reliable electric power delivery system has led to the propagation of a wide range of new technologies and ideas within the power grid infrastructure. Thus, the power grid is becoming more adaptable to changes and more reliable under distress. However, these benefits are only possible with vastly improved observability in the system. The traditional methods and technologies for grid monitoring were simply too slow and newer, faster and more accurate monitoring technologies became essential over the turn of the century. With the advancement of micro processing and communication technologies at an incredibly fast pace, this became possible in the form of smart monitoring devices. These devices include Intelligent Electronic Devices (IEDs), smart meters for homes and, at the transmission level, the use of Synchrophasor Measurement Units (PMUs). Over the past decade, transmission utilities were quick to adopt these PMU networks and they are now common among most major utilities. Compared to traditional monitoring systems, PMUs provide information at a much higher resolution and have the advantage of being time synchronized. The benefits of these networks are numerous, but they are not without certain drawbacks. PMU devices only report some basic system parameters from the field. While these are useful on their own, it is possible to use this data, in combination with other information, to extrapolate additional parameters about the grid. However, in this process, inherent errors present in PMU estimated data become an issue and renders the results of this extrapolated information unusable. In this work, of particular focus from these additional parameters is transmission line resistance. The fundamental cause of error will be investigated, and this knowledge will be applied to create a correction algorithm to output corrected transmission line resistance estimates that are more useful to utilities for a range of auxiliary applications such as dynamic line rating, determination of line sag, and conductor temperature estimation. This advancement would allow utilities to compound the economic benefits of their investment in PMU networks.
Open Access
Investigating the enabling role of web 2.0 technology for interactive e-learning in australian and sri lankan higher education
(RMIT University, 2015) Karunasena, A
Interactions are at the heart of e-learning as they enable learners to actively develop knowledge, acquire skills and develop the sense of belonging and satisfaction. Much attention is paid on developing interactive e-learning systems for facilitating active interactions between learners and learning resources, instructors and peer learners. Numerous technologies such as simulation technology and Web 2.0 technology are used to facilitate interactive e-learning to date. Those technologies support learners to interact with learning resources, instructors and peer learners to different extents. To facilitate interactive e-learning, it is important for educators and e-learning developers to understand how well technologies as above support interactions in e-learning. Web 2.0 technology has become popular around the world recently due to their ease of use, portability and high availability. Much research has been done on how Web 2.0 technology could be used for interactive e-learning. Existing research, however, has several limitations. For example, a majority of research has investigated how a specific Web 2.0 tool supports a specific kind of interactions in e-learning such as learner-learner interaction. Furthermore, much of existing research on Web 2.0 based interactive e-learning is conducted in developed countries. Whether Web 2.0 technology supports interactive e-learning in developing countries in a similar manner to developed countries, or whether developing countries could learn lessons from developed countries on using Web 2.0 technology for interactive e-learning are, therefore, not clear. This research aims to investigate the enabling role of Web 2.0 technology for interactive e-learning in higher education in Australia, a developed country and Sri Lanka, a developing country. To meet this aim, a quantitative research approach is adopted. Following this research approach, a conceptual framework on Web 2.0 based interactive e-learning developed based on a comprehensive review of the relevant literature, is validated using the survey data collected from learners in universities in Australia and Sri Lanka. The validation of the conceptual framework reveals that Web 2.0 technology supports the three major types of interactions in learning, namely, learner-learning resources, learner-instructor and learner-learner interactions in both Australia and Sri Lanka to a great extent. Furthermore, no significant differences are found on how Web 2.0 technology supports interactive e-learning in the above countries. The implication of these findings is that Web 2.0 tools could be used to improve the interactivity of e-learning. Another implication of this research is that new and more interactive e-learning systems can be developed by using Web 2.0 technology, in particular, for the purposes of managing learning resources, managing personal knowledge, delivering instructional support and collaborating in order to improve the effectiveness of e-learning. From a practical perspective, this study presents an in-depth investigation of how Web 2.0 technology can be used for improving the interactivity of e-learning in Australia and Sri Lanka. It also provides specific guidelines for developing interactive e-learning environments using Web 2.0 technology. From a theoretical perspective, this research finds that Web 2.0 technology could be used in developing countries and developed countries to improve the three major interactions in e-learning.
Open Access
Synthesis, characterization and applications of metal nanoparticles supported on porous carbon
(ProQuest LLC, 2017) Thambiliyagodage, C. J
Porous carbon incorporating metal nanoparticles has been synthesized by nanocasting. The main two methods of synthesis were used: the formation of nanoparticles during the carbonization of carbon, and the formation of nanoparticles by metal precursor infiltration and reduction on porous carbon. The catalytic activity of nickel nanoparticles incorporated onto hierarchically porous carbon monoliths for the reduction of p-nitrophenol was studied. p-Quinoimine was identified as the stable intermediate. Catalytic graphitization of monolithic hierarchically porous carbon by iron, cobalt and nickel nanoparticles was investigated. The catalytic graphitization of amorphous carbon increased with increasing pyrolysis temperature. Iron was capable of graphitizing carbon more effectively than cobalt and nickel, with cobalt being higher in activity than nickel. Oxygen and nitrogen rich mesoporous carbon were used to support gold nanoparticles and their catalytic activity was investigated for oxidation of benzyl alcohol in water. The catalysts showed significant catalytic activity, but loss of activity were found, resulting in decreasing conversion of benzyl alcohol on subsequent cycles.
Open Access
“AccessBIM” - A Model of Environmental Characteristics for Vision Impaired Indoor Navigation and Way Finding
(Curtin University, 2018-06) Jayakody, A
The navigation of indoor and outdoor environments play a pivotal role in the daily routine of humans. Navigation systems that provide path planning and exploration services for outdoor environments are readily available while navigation within a building is still a challenge due to limited information availability and the poor quality of GPS signals, which makes it difficult to capture characteristics within the indoor environment. Consequently, the use of GPS tracking devices for real-time map generation is not feasible. Indoor navigation is particularly difficult for people with vision impairment. According to the factsheet of the World Health Organization (WHO) as of October 2017, over 253 million people are estimated to be vision impaired: 36 million to be blind, and 217 to have poor vision. Currently, most blind and vision-impaired individuals use the white cane as an assistive tool and are often accompanied by care takers or voluntary helpers. Most modern indoor environments consist of complex architectural structures with varying arrangement of physical objects. Since retrieving indoor location information has been challenging for the vision impaired, it would be helpful if spatial information of doors, walls and staircases were made available. To address the above-mentioned problem, this thesis presents an improved schema design, an Accessible Building Information Model (AccessBIM) which could be used for generating an indoor map that could instruct vision impaired individuals in navigation, by the classification of real world objects and their locations. AccessBIM is a real-time relational database, which acts as the main component of the central system implemented to manipulate crowdsourced data such as the floor plan and architectural data along with semantic information within the built environment. The AccessBIM database stores information on the indoor arrangement of objects within buildings to facilitate the exchange and interoperability of real-time information. The database is equipped with an optimization algorithm that reduces the query execution time with the support of indexing, query re-writing, schema redesigning and a memory optimization technique introduced as “BIMcache”. vi In order to create a real-time map, the AccessBIM manipulates crowdsourced data from “smart devices” or AccessBIM users. The collection and storage of crowdsourced data, database optimization, API functions and the map construction algorithms were tested using a simulated test engine. The AccessBIM framework has the potential to play an integral role in assistive technologies related to localization and mapping, thus significantly improving the independence and quality of life for people with vision impairment whilst also decreasing the cost to the community related to support workers
Open Access
Reinforcement learning based trust framework for MANET environment
(Curtin University, 2018) Rupasinghe, L
Mobile Ad-hoc Networks (MANET) are design and implemented without the need for any infrastructure support. The properties of MANET inherently provide greater challenges in areas like security and reliability. This thesis presents three security protocols which were developed for addressing the MANET security needs. A novel trust calculation methodology and intelligent secure route prediction was designed to an existing MANET routing protocol. These protocols will help to implement a trustworthy MANET, providing a dynamic and secure environment.

PhD

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