Research Papers - Department of Electrical and Electronic Engineering

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Open Access
A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-07-17) Tennekoon, S; Wedasingha, N; Welhenge, A; Abhayasinghe, N; Murray, I
Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the algorithmic development of a lightweight, vision-based doorway detection and alignment module with contextual awareness. It integrates channel and spatial attention, semantic feature fusion, unsupervised depth estimation, and doorway alignment that offers real-time navigational guidance to the wheelchairs control system. The model achieved a mean average precision of 95.8% and a F1 score of 93%, while maintaining low computational demands suitable for future deployment on embedded systems. By eliminating the need for depth sensors and enabling contextual awareness, this study offers a robust solution to improve indoor mobility and deliver actionable feedback to support safe and independent doorway traversal for wheelchair users.
Open Access
Accessbim model for environmental characteristics for vision impaired indoor navigation and way finding
(2012 International Conference on Indoor Positioning and Indoor Navigation, 2012-11) Jayakody, J. A. D. C. A; Abhayasinghe, N; Murray, I
Most blind people require assistance to navigate within buildings as there is often insufficient information about the buildings available to them. To address this problem, this paper describes the “AccessBIM” model as an approach to facility management in which a digital representation of the indoor building features is used to facilitate the exchange and interoperability of real-time information in digital format which can assist blind people to independently access unfamiliar building indoor environments. This paper discusses conceptual communication model driven architecture that can be implemented for way finding and data synchronization, generating, in real-time, an AccessBIM for a remote user.
Open Access
Accurate Pedometer for Smartphones
(2013) Jayalath, S; Abhayasinghe, N; Murray, I
Accuracy of step counting is one of the main problems that exist in current Pedometers, especially when walking slowly on flat lands and performing different activities, such as climbing up and down stairs and walking on inclined planes. Although accelerometer based pedometers provide a reasonable accuracy when walking at higher speeds, the accuracy of them are not sufficient at slow walking speeds and performing different activities. This paper proposes a novel algorithm to detect steps using single-point gyroscopic sensors embedded in mobile devices. Preliminary analysis of data collected in different environments with the involvement of male and female volunteers indicated that gyroscope alone provides sufficient information necessary for accurate step detection. Algorithm was developed based on the gyroscopic data in conjunction with zero crossing and threshold detection techniques. The results proved that gyroscope based step detection algorithm provide a high accuracy when performing different activities and at slow paced walking.
Open 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.
Embargo
Alignment parameter calibration for IMU using the Taguchi method for image deblurring
(Elsevier, 2015-04-01) Chan, K.Y; Rajakaruna, N; Engelke, U; Murray, I; Abhayasinghe, N
Inertial measurement units (IMUs) utilized in smartphones can be used to detect camera motion during exposure, in order to improve image quality degraded with blur through long hand-held exposure. Based on the captured camera motion, blur in images can be removed when an appropriate deblurring filter is used. However, two research issues have not been addressed: (a) the calibration of alignment parameters for the IMU has not been addressed. When inappropriate alignment parameters are used for the IMU, the camera motion would not be captured accurately and the deblurring effectiveness can be downgraded. (b) Also selection of an appropriate deblurring filter correlated with the image quality has still not been addressed. Without the use of an appropriate deblurring filter, the image quality could not be optimal. This paper proposes a systematic method, namely the Taguchi method, which is a robust and systematic approach for designing reliable and high-precision devices, in order to perform the alignment parameter calibration for the IMU and filter selection. The Taguchi method conducts a small number of systematic experiments based on orthogonal arrays. It studies the impact of the alignment parameters and appropriate deblurring filter, which attempts to perform an effective deblurring. Several widely adopted image quality metrics are used to evaluate the deblurred images generated by the proposed Taguchi method. Experimental results show that the quality of deblurred images achieved by the proposed Taguchi method is better than those obtained by deblurring methods which are not involved with the alignment parameter calibration and filter selection. Also, much less computational effort is required by the Taguchi method when comparing with the commonly used optimization methods for determining alignment parameters and deblurring filter.
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Analysis and interpretation of dissolved gases in transformer oil: A case study
(IEEE, 2012-09-23) Hettiwatte, S. N; Fonseka, H. A
Condition monitoring plays a vital role in any asset management plan. Dissolved gas analysis is a routine test carried out on power transformers to monitor their condition. Four power transformers selected from a repository of power transformers due to their dissolved gas levels exceeding the normal levels are analyzed using the Key Gas Method, the Roger's Ratio Method and the Duval Triangle Method to diagnose any faults. The results show that for some transformers all three diagnosis methods agree on the type of fault, whilst for others it is not so straightforward in diagnoses. In this study, the condition of each power transformer is predicted using the above methods.
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An analysis of the time of use electricity price in the residential sector of Bangladesh
(Elsevier, 2017-12-01)
Time of Use (TOU) pricing is a cost-reflective electricity pricing scheme; it has proven to be an effective approach for reducing peak electricity demand in the residential sector around the world, especially in developed countries. The implementation of TOU pricing in low and lower-middle income economies is less appealing than in other settings. This is mainly because a traditional TOU pricing scheme may increase the cost of electricity for low income consumers. The lack of a suitable TOU pricing strategy for these countries results in high peak demand, poor utilization of network infrastructure and, consequently, higher electricity prices than necessary. The purpose of this study is to analyse and propose a TOU pricing scheme for the residential sector that will be suitable for countries with a high percentage of low income household consumers. In this study, Bangladesh will be used as an exemplar of a lower-to-middle income developing country. In Bangladesh, the residential sector is responsible for half the country's total electricity consumption, and constitutes an even greater proportion of the peak demand. Residential consumers currently pay inclining block usage rates that provide no financial incentive for them to shift their electricity usage from peak to non-peak periods. The proposed TOU pricing scheme is a combination of the traditional TOU and inclining block usage pricing schemes, based on a realistic load shifting capacity that is applicable to Bangladesh, and to other similar developing countries. Analysis of this pricing system for different income levels of residential consumers shows that the proposed scheme effectively reduces the peak demand, while ensuring minimum impact on consumer monthly energy bills and comfort levels.
Embargo
An analytical framework for cluster distribution of EDCR class of algorithms in Wireless Sensor Networks
(IEEE, 2009-12-28) Gamwarige, S; Kulasekere, E. C
Energy Driven Cluster-Head Rotation (EDCR) class of algorithms are energy aware distributed clustering techniques for effective ad hoc deployed Wireless Sensor Network (WSN) organization. The application of this class of algorithms requires the setting of salient parameters at the design stage of the WSN to achieve desired results. Two such parameters which should be known in advance are the cluster density and the distance between neighbouring cluster heads (CHs). In this research we analyze the effect of algorithm design based on these two parameters. Simulation techniques are given to support and verify the analytical results.
Open Access
Animal Classification System Based on Image Processing & Support Vector Machine
(Scientific Research Publishing, 2016-01-15) Seneviratne, L; Shalika, A. W. D. U
This project is mainly focused to develop system for animal researchers & wild life photographers to overcome so many challenges in their day life today. When they engage in such situation, they need to be patiently waiting for long hours, maybe several days in whatever location and under severe weather conditions until capturing what they are interested in. Also there is a big demand for rare wild life photo graphs. The proposed method makes the task automatically use microcontroller controlled camera, image processing and machine learning techniques. First with the aid of microcontroller and four passive IR sensors system will automatically detect the presence of animal and rotate the camera toward that direction. Then the motion detection algorithm will get the animal into middle of the frame and capture by high end auto focus web cam. Then the captured images send to the PC and are compared with photograph database to check whether the animal is exactly the same as the photographer choice. If that captured animal is the exactly one who need to capture then it will automatically capture more. Though there are several technologies available none of these are capable of recognizing what it captures. There is no detection of animal presence in different angles. Most of available equipment uses a set of PIR sensors and whatever it disturbs the IR field will automatically be captured and stored. Night time images are black and white and have less details and clarity due to infrared flash quality. If the infrared flash is designed for best image quality, range will be sacrificed. The photographer might be interested in a specific animal but there is no facility to recognize automatically whether captured animal is the photographer’s choice or not.
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The application of “Off-the-shelf” components for building IMUs for navigation research
(IEEE, 2014-10-27) Abhayasinghe, N; Murray, I
Inertial measurement units (IMU) are commonly used in pedestrian and robotic navigation applications and research. Although many IMUs are commercially available, almost all of them are non-customizable and they process the collected raw data before presenting them to the user. However, this creates a limitation for researchers due to the fact that they have to rely on a set of per-processed data. Further, available resources and features such as SD card slots, wireless connectivity, available in the IMU may not suit one's research. This paper provides a survey on availability and usage of different off-the-shelf devices to build a custom made IMU. The authors considered open-source microcontroller platforms, low cost MEMS sensors and low cost accessories in this survey so that the IMUs will be affordable to many people. A range of sensors, their features, available processor options and different types of wired and wireless communication options available are discussed. Particular emphasis is made on the ability to modify or add functionality to commonly available hardware. Possible technical issues in assembling the IMU and calibrating sensors are also discussed in this paper. Technologies available for constructing a housing and mounting systems for the IMU best suited to the application are also discussed in this paper. As an example, IMUs developed and implemented by the authors with different housing designs specifically created for particular applications are presented. This survey indicated that off-the-shelf components can effectively be used to build custom-made IMUs to suit the particular research interest or application best.
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The application of “Off-the-shelf” components for building IMUs for navigation research
(IEEE, 2014-10-27) Abhayasinghe, N; Murray, I
Inertial measurement units (IMU) are commonly used in pedestrian and robotic navigation applications and research. Although many IMUs are commercially available, almost all of them are non-customizable and they process the collected raw data before presenting them to the user. However, this creates a limitation for researchers due to the fact that they have to rely on a set of per-processed data. Further, available resources and features such as SD card slots, wireless connectivity, available in the IMU may not suit one's research. This paper provides a survey on availability and usage of different off-the-shelf devices to build a custom made IMU. The authors considered open-source microcontroller platforms, low cost MEMS sensors and low cost accessories in this survey so that the IMUs will be affordable to many people. A range of sensors, their features, available processor options and different types of wired and wireless communication options available are discussed. Particular emphasis is made on the ability to modify or add functionality to commonly available hardware. Possible technical issues in assembling the IMU and calibrating sensors are also discussed in this paper. Technologies available for constructing a housing and mounting systems for the IMU best suited to the application are also discussed in this paper. As an example, IMUs developed and implemented by the authors with different housing designs specifically created for particular applications are presented. This survey indicated that off-the-shelf components can effectively be used to build custom-made IMUs to suit the particular research interest or application best.
Open Access
Application of Parzen Window estimation for incipient fault diagnosis in power transformers
(The Institution of Engineering and Technology, 2018-12) Islam, M. D. M; Hettiwatte, S. N; Lee, G
Accurate faults diagnosis in power transformers is important for utilities to schedule maintenance and minimises the operation cost. Dissolved gas analysis (DGA) is one of the proven and widely accepted tools for incipient fault diagnosis in power transformers. To improve the accuracy and solve the cases that cannot be classified using Rogers’ Ratios, IEC ratios and Duval triangles methods, a novel DGA technique based on Parzen window estimation have been presented in this study. The model uses the concentrations of five combustible hydrocarbon gases: methane, ethane, ethylene, acetylene and hydrogen to compute the probability of transformers fault categories. Performance of the proposed method has been evaluated against different conventional techniques and artificial intelligence-based approaches such as support vector machines, artificial neural networks, rough sets analysis and extreme learning machines for the same set of transformers. A comparison with other soft computing approaches shows that the proposed method is reliable and effective for incipient fault diagnosis in power transformers.
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Application of the edcr algorithm in a cluster based multi-hop wireless sensor network
(IEEE, 2006-10-18) Gamwarige, S; Kulasekere, E. C
The energy driven cluster head rotation algorithm proposed in (Gamwarige, S and Kulasekere, E, 2005) and analyzed in (Gamwarige, S and Kulasekere, E, 2006) is based on a single hop communication model where the data messages from each cluster head (CH) node is sent directly to the base station (BS). As a result when the wireless sensor network (WSN) dimensions are large, the nodes located far away from the BS die much faster. In this paper a method of extending the lifetime of the WSN based on a multihop communication model applied to the EDCR algorithm is proposed. The modified EDCR (EDCR-MH) relays all CH to BS messages via other CH nodes by computing a shortest path based on local heuristic information. Further, the EDCR-MH also has provisions to minimize the burden due to excessive data relay on nodes closer to the BS. The results indicate that the proposed algorithm out performs algorithms like under similar conditions
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Assessment of System Reliability in Presence of Cyber Attack Risk on PMU Data
(IEEE, 2018-12-21) Hettiarachchige-Don, A. C. S; Manoharan, A. K; Pedaprolu, L. G; Pedaprolu, V
This paper provides an assessment of the effect of Synchrophasor Measurement Units in improving power system reliability. First, an overview of cyber threats to PMU networks is given where the possible vulnerabilities of the network to malicious data manipulation are studied. The addition of PMUs to the power system infrastructure would offer greater observability in the system and thus mitigate the risk of transmission line failure due to thermal overloading. However, the addition of PMUs opens the system to cyber threats that might, in fact, lower system reliability. The analysis in this work looks at the effect of these threats on the overall system reliability of a real system layout based on real PMU data obtained from a utility. The benefit to a utility in including PMUs under the varying levels of cyber threat is also studied.
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Autoencoder based data clustering for identifying anomalous repetitive hand movements, and behavioral transition patterns in children
(Springer Science and Business Media Deutschland GmbH, 2025-01-21) Wedasingha, N; Samarasinghe, P; Senevirathna, L; Papandrea, M; Puiatti, A
The analysis of repetitive hand movements and behavioral transition patterns holds particular significance in detecting atypical behaviors in early child development. Early recognition of these behaviors holds immense promise for timely interventions, which can profoundly impact a child’s well-being and future prospects. However, the scarcity of specialized medical professionals and limited facilities has made detecting these behaviors and unique patterns challenging using traditional manual methods. This highlights the necessity for automated tools to identify anomalous repetitive hand movements and behavioral transition patterns in children. Our study aimed to develop an automated model for the early identification of anomalous repetitive hand movements and the detection of unique behavioral patterns. Utilizing autoencoders, self-similarity matrices, and unsupervised clustering algorithms, we analyzed skeleton and image-based features, repetition count, and frequency of repetitive child hand movements. This approach aimed to distinguish between typical and atypical repetitive hand movements of varying speeds, addressing data limitations through dimension reduction. Additionally, we aimed to categorize behaviors into clusters beyond binary classification. Through experimentation on three datasets (Hand Movements in Wild, Updated Self-Stimulatory Behaviours, Autism Spectrum Disorder), our model effectively differentiated between typical and atypical hand movements, providing insights into behavioral transitional patterns. This aids the medical community in understanding the evolving behaviors in children. In conclusion, our research addresses the need for early detection of atypical behaviors through an automated model capable of discerning repetitive hand movement patterns. This innovation contributes to early intervention strategies for neurological conditions
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Blended induction program for electronic engineering freshmen
(Sage Publications, 2018-10) Dayawansa, I; Wijenayake, C; Edussooriya, C; Samarasekara, T; Karunasekara, C; Dias, D; Samarasinghe, K; Kulasekere, E. C; Rodrigo, R; Dayananda, N
This paper presents learning and teaching experiences gathered from an induction program conducted at the Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka for newly enrolled undergraduate students. The induction program, named as the pre-academic term, was an experimental effort to explore the applicability of blended leaning concepts to enhance the learning experience of Engineering freshmen, while enabling smooth transition from high school to university education. In addition to standard lectures, an integral part of the program was in-class demonstrations, where electronic circuits were assembled from scratch on a printed circuit board and demonstrated using a projected camera during the lectures. Organization, learning objectives, brief description of the example projects are presented along with a summary of formal student feedback. More than 85% of the freshmen indicated that the induction program helped them to smoothly begin the university education. A qualitative assessment in the laboratory work indicated that about 98% of the students successfully completed the assigned hands-on activities.
Open Access
Bluetooth embedded inertial measurement unit for real-time data collection for gait analysis
(2013 International Conference on Indoor Positioning and Indoor Navigation, 2013-10) Chandrasiri, R; Abhayasinghe, N; Murray, I
—Inertial Measurement Units (IMUs) are often used to measure motion parameters of human body in indoor/outdoor localization applications. Most of commercially available low-cost IMUs have limited number of sensors and are often connected to a computer by a wired connection (usually by USB). The disadvantage of using wired IMUs in human gait measurement is that, the wires disturb the natural gait patterns. The existing IMUs with wireless connectivity solve that problem, but are relatively high cost. This paper describes the development and testing of a miniature IMU that can be connected to a Windows based computer or an Android based mobile device through Bluetooth. The IMU consists of a 3-axis accelerometer, 3-axis gyroscope, 3-axis magnetometer, a temperature sensor, a pressure sensor and an ambient light sensor. Sensors are sampled at a frequency configurable by the user with a maximum set at 100 Hz. Raw sensor data are streamed through the integrated Bluetooth module to the host device for further processing. The IMU is also equipped with a microSD card slot that enables on-board data logging. The power usage of the Bluetooth transmitter is optimized because only the sampled sensor data are transmitted. The windows application can be used to view sensor data, plot them and to store them into a file for further processing. Android application can be used to view data as well as to record data into a file. The small size of the device enables it be attached to any part of lower or upper human body for the purpose of gait analysis. Comparison of the performance of the device with a smartphone indicated that the output of the IMU is comparable to the output of smartphone.
Open Access
Bluetooth Embedded Portable Oscilloscope
(2013) Seneviratne, H. M. D. B; Abhayasinghe, K. N
Portable oscilloscopes currently in the market are very expensive, less power efficient and have small low resolution displays. This paper presents the design and implementation of a low cost, portable, lightweight; low power, dual-channel oscilloscope, consisting of a hardware device and a software application. The device is equipped with a Bluetooth module to provide connectivity to a device with Bluetooth, running the Android operating system (OS), in order to display the waveforms. Android OS is selected because there are a decent number of Android device users and most of these devices satisfy the requirements of the oscilloscope’s software application. The hardware device includes circuitry to capture the input voltage signals and an embedded Bluetooth module for transmitting the captured signal information to an Android device for displaying the waveform. The Software application developed for Android receives the data transmitted from the hardware device and plots the waveform according to the display settings configured by the user. These display configurations are transmitted to the hardware device once they are set by the user, and are used by the hardware device to set the sampling rate and the values of samples. For optimal use of the available bandwidth, the application provides two modes of operation, namely single channel mode where only channel 1 is operational and dual channel mode where both channels 1 and 2 are operational. The user can select a mode from the application, which in turn sends a message to the microcontroller which then changes the sampling frequency accordingly: a higher sampling rate for single channel and half of that for dual channel.
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Brewing plastics: OCT reveals microplastic release from nylon tea bags in simulated brewed tea infusions
(Royal Society of Chemistry, 2026-02-12) Jayasekara, P.M; Abhishek, P; Kahandawala,B.S; Damith, N; Weerasinghe, M; Kahatapitiya, N.S; Silva, B.N; Karunaratne, S; Wijesinghe, R.E; Wijenayake, U
The release of microplastics (MPs) from nylon tea bags poses a critical concern for human exposure; however,their detection and quantification remain challenging especially in beverage matrices, and hence, this study pioneers the use of high-resolution optical coherence tomography (OCT) integrated with an image processing algorithm to rapidly detect and quantify the size and count of the MPs directly in the water extractions simulating tea brewing. The water extractions prepared by simulating tea brewing conditions, hot (100 °C, 1–5min), cold (2 °C, 1 h), and ambient (30 °C, 1 h), were observed employing OCT imaging and validated through Nile Red (NR) staining and digital microscopy. The nylon tea bags steeped in hot water for 5 minutes released 16 000 to 24 000 LMPs (>30 mm) and SMPs (12–30 mm) per millilitre. The estimated daily intake (EDI) of MPs indicates a higher exposure for children (ranging from 0.201 to 0.349 mm3 kg−1 day−1 ) compared to adults (0.046 to 0.080 mm3 kg−1 day−1 ). In contrast, cold brewing for 1 hour released fewer LMPs but an equal quantity of small MPs (SMPs) compared to hot brewing. This OCT-based approach offers a rapid, versatile platform for the detection and quantification of MPs from diverse packaging materials and provides a powerful tool for comprehensive risk assessment when combined with chemical and toxicological analyses.
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Calculating a health index for power transformers using a subsystem-based GRNN approach
(IEEE, 2017-11-07) Islam, M; Lee, G; Hettiwatte, S. N; Williams, K
A power transformer is one of the most crucial items of equipment in the electricity supply chain. The reliability of this valuable asset is strongly dependent on the condition of its subsystems such as insulation, core, windings, bushings and tap changer. Integration of various measured parameters of these subsystems makes it possible to evaluate the overall health condition of an in-service transformer. This paper develops an artificially intelligent algorithm based on multiple general regression neural networks to combine the operating condition of various subsystems of a transformer to form a quantitative health index. The model is developed using a training set derived from four conditional boundaries based on IEEE standards, the literature and the knowledge of transformer experts. Performance of the proposed method is compared with expert classifications using a database of 345 power transformers. This shows that the proposed method is reliable and effective for condition assessment and is sensitive to poor condition of any single subsystem.