Department Publications

Highly flexible FPAA and FPGA based universal sensor node design targeting researchers is discussed in this paper. The reconfigurable system is capable of supporting up to three low power analog sensors and two digital sensors in parallel. This facilitates dynamic configurability and remote monitoring by connecting the device to the internet. The design can be utilized in development of remote sensing devices to be establish at locations where frequent repairing and maintenance is difficult. Compensability for aging, adjustability to different environments, flexibility are key advantages of this system.

There exists some common bottlenecks among the many available Software Defined Networking (SDN) controllers. Of these, the current analysis uses the Floodlight software controller to identify bottlenecks. The analysis has implemented these bottlenecks in a Field Programmable Gate Array (FPGA) in order to assess its feasibility. Thereafter, a Hybrid SDN controller has been developed. Generally Hybrid SDN refers to a networking approach where traditional networking and SDN protocols are used and operated in the same environment. Hybrid in this paper context refers to a controller which is developed both on software and hardware platforms in order to achieve a higher scalability which is a current issue existing in software controller, and thereby overcoming the identified bottlenecks. The analysis has offloaded computational intensive models to an FPGA and tested for performance. These performance figures have then been compared against the performance figures as of a stand-alone software controller.

The aim of this study is to design a Home Automation system by interfacing Power Line Communication (PLC) with GSM communication which would enable the user to control appliances by sending commands via SMS. A communication model based on Star Topology is utilized in this home automation system which can be scaled at a low cost. The communication system facilitates secured communication by using BlumGoldwasser cryptosystem. The communication system employs a master Central Control System placed at the switch board and slave receiver nodes in household appliances. This system was planned from the outset to make as simpler and cost effective as possible so that it would be affordable to more households in low income countries. The system was tested in a lab environment at a maximum distance of 20 m and the operation was verified. Subsequently, Signal levels were obtained at …

This paper presents a comprehensive framework to analyze the impact of aggregate interference from an underlay clustered Device-to-Device (D2D) network on the performance of its primary cellular network, when different power control schemes are used at the D2D network. A single base station, located at the center, operates with a cellular network and a D2D network, modeled using a homogeneous spatial Poisson Point Process and a Poisson Cluster Process, respectively. Using this system, the cellular network performance is examined under three power control schemes used by the D2D network namely, no power control, path loss inversion and channel inversion. For each scheme, the coverage probability of the cellular network is analyzed. Numerical results indicate that the cellular network performance is greatly dependent on the D2D system parameters such as density, size of a cell, number of …

Pulse Transit Time (PTT)/Pulse Wave Velocity (PWV) measurement based non-invasive blood pressure estimation is of much interest at present, as it enables continuous monitoring of blood pressure in a more comfortable manner compared to traditional cuff based methods, without hindering natural user behavior. In this study we have investigated the possibility of estimating blood pressure using PTT without the need of external calibrations. The parametric model developed in our previous research is improved eliminating the need of initial calibration which requires external blood pressure measurement from a reference device. The results suggest that there is a potential of developing calibrationless blood pressure estimation methods based on PTT/PWV which may require further testing and validation.

A complex activity is a temporal composition of sub-events, and a sub-event typically consists of several low level micro-actions, such as body movement of different actors. Extracting these micro actions explicitly is beneficial for complex activity recognition due to actor selectivity, higher discriminative power, and motion clutter suppression. Moreover, considering both static and motion features is vital for activity recognition. However, optimally controlling the contribution from static and motion features still remains uninvestigated. In this work, we extract motion features at micro level, preserving the actor identity, to later obtain a high-level motion descriptor using a probabilistic model. Furthermore, we propose two novel schemas for combining static and motion features: Cholesky-transformation based and entropy-based. The former allows to control the contribution ratio precisely, while the latter obtains the optimal …

Power control schemes are proposed for a full-duplex (FD) device-to-device (D2D) network operating as an underlay of a primary FD network. As networks with different quality of service (QoS) requirements may coexist in future heterogeneous networks, it is assumed that the underlay D2D network is operating with best effort QoS provisioning, while the primary FD network requires guaranteed QoS. A centralized power control (CPC) solution is proposed by casting the power control problem as a convex optimization problem. Furthermore, a heuristic distributed power control (DPC) solution is proposed to reduce information feedback overhead. It is found that the heuristic DPC achieves D2D user data rate that is within 87% and 80% of that provided by the CPC scheme in 3-node and 2-node FD primary network, respectively.

This extended abstract critically investigates the possible new compliance standards of academic and research ethics that may result from the implementation ofthe Right to Information Act (RTIA) ofSri Lanka. The RTIA was enacted in quick succession to the 19lh Amendment of the 1978 Constitution of Sri Lanka enacted on 4th August 2016. The 19th Amendment made the right to information from public bodies a constitutional right (Article 14A). The provisions of the RTIA could have profound legal ramifications on the way in which research is conducted in Sri Lanka: especially with respect to non medical research, that involves private data.

This paper describes a demand management system based on narrow band Power line communication and GSM technologies which can be used by the electricity provider to reduce electrical load by turning off nonessential equipment in establishments during high demand periods where enough electricity cannot be supplied. This solution is thought to have less impact on the lives of the public. The system uses Blum-Goldwasser cryptosystem for secure power line communication and to mitigate rogue devices. The system does not require internet connectivity. Therefore, ideal for countries with less internet coverage.

Pose tracking for outdoor rovers is generally a complex task which is further complicated in conditions where a Global Positioning System (GPS) signal is denied such as in planetary exploration, underground mines and covered areas. In these conditions the rover's pose needs to be calculated purely based on the rover's current environment observations. However, conventional wheel odometry is not reliable on rough terrain where wheels are prone to slip and the wheels do not have a common plane of motion due to suspension systems. This paper proposes a Fast State Variable Extension (Fast-SVE) method in which 2D state variables (x, y, yaw) are extended to the full 3D state (x, y, z, roll, pitch, yaw) to achieve effective real time 3D pose tracking of the rover. A particle filter implementation incorporating the Fast-SVE method is used to track the 3D pose of the rover with roll and pitch values used for weighting. An …

Voice commands have been used as the basic method of interaction between humans and robots over the years. Voice interaction is natural and require no additional technical knowledge. But while using voice commands humans frequently use uncertain information. In the case of object manipulation on a table, frequently used uncertain terms “Left,“Right”,“Middle”,“Front”... etc. These terms fail to depict an exact location on the table and the interpretation is governed by the robots point of view. Depending solely on vocal cues is not ideal as it requires the users to explain the exact location with more words and phrases making the interaction process cumbersome and less human like. However, using hand gestures to pinpoint the location is as natural as using the voice commands and frequently used when manipulating items on a surface. When compared to voice commands use of hand gestures is a more direct and less cumbersome approach. But when used alone hand gestures can result in errors while extracting the pointed location making the user dissatisfied. This paper proposes a multi-modal interaction method which uses hand gestures combined with voice commands to interpret uncertain information when placing an object on a table. Two fuzzy inference systems have been used to interpret the uncertain terms related to the two axes of the table. The proposed system has been implemented on an assistive robot platform. Experiments have been conducted to analyze the behaviour of the system.

A smart city is a vision for urban development which integrates multiple information and communication technology (ICT) and Internet of Things (IoT) solutions to manage a city's assets and services. Urban population is constantly growing, and this implies an inevitable increase in the resource consumption within the city. The realization of intelligent solutions is therefore essential to optimize the usage of resources, minimize the environmental impact and improve the quality of life of the city's inhabitants.

The development of computer based systems expands the usage of computer based application in human life. It can be observed that illegal activities such as unauthorized data access, data theft, data modification and various other intrusion activities are rapidly growing during last decade. Hence, deployment and continuous improvement of Intrusion Detection Systems (IDS) are of paramount importance. Training, testing and evaluation of IDS with real network traffic is significant challenge, so most of IDS evaluation is based on intrusion datasets. Therefore, analysis of intrusion datasets are of paramount importance. In this paper, we evaluated Aegean Wi-Fi Intrusion Dataset (AWID) with different machine learning techniques. Feature reduction techniques such as Information Gain (IG) and Chi-Squared statistics (CH) were applied to evaluate dataset performance with feature reduction. Results of experiments show …

Higher compression efficiency in HEVC encoders comes with increased computational complexity, making real time encoding of high resolution videos a challenging task. This challenge can be addressed by software, yet hardware solutions are more appealing due to their superior performance and low power consumption. This paper presents an FPGA based hardware implementation of an all intra HEVC encoder, which can encode 8 bits per sample, 1920×1080 resolution, 30 frames per second raw video, that is viable in real time even at low operating frequencies. A major obstacle to real time encoding in available architectures is the dependency created by reference generation. Moreover, each coding unit (CU) has to be processed in multiple configurations to determine the most efficient split and prediction mode representation, based on the bit stream generated. We propose a new three stage architecture to …

The P300 wave is an Event Related Potential that is elicited in the brain when a subject is presented with a familiar stimulus. The Guilty Knowledge Test is used to determine if certain information is stored in the brain by detecting the P300 wave. In this paper, an improved Guilty Knowledge Test has been conducted on 14 subjects. Although a variety of feature extraction and classification algorithms for detecting the P300 have been used thus far, this paper introduces the use of deep learning techniques for the Guilty Knowledge Test. Two deep learning techniques; Deep Belief Networks and Stacked Autoencoders, have been compared. The input to these classifiers was the filtered EEG signal without any feature extraction. A mean accuracy of 86.9% was achieved for the Deep Belief Network and 86.01% for the Stacked Autoencoder on our dataset, outperforming the conventional Support Vector Machine.

Intrusion detection is only a starting step in securing IT infrastructure. Prediction of intrusions is the next step to provide an active defense against incoming attacks. Current intrusion prediction methods focus mainly on prediction of either intrusion type or intrusion category and do not use or provide contextual information such as source and target IP address. In addition most of them are dependant on domain knowledge and specific scenario knowledge. The proposed algorithm employs a bag-of-words model together with a hidden Markov model which not depend on specific domain knowledge. Since this algorithm depends on a training process it is adaptable to different conditions. A key advantage of the proposed algorithm is the inclusion of contextual data such as source IP address, destination IP range, alert type and alert category in its prediction, which is crucial for an eventual response. Experiments conducted using a public data set generated over 2500 alert predictions and achieved accuracy of 81% and 77% for single step and five step predictions respectively for prediction of the next alert cluster. It also achieved an accuracy of prediction of 95% and 92% for single step and five step predictions respectively for prediction of the next alert category. The proposed methods achieved a prediction accuracy improvement of 5% for alert category over existing variable length Markov chain intrusion prediction methods, while providing more information for a possible defense.

This paper presents on the stability analysis for RAVAN twin boom H-Tail Medium Scale UAV which has been developed by University of Moratuwa. It is required to understand the UAV behavior for the small perturbations in high altitudes while it is flying to be invisible. Indeed, without doing a proper stability analysis in different flying conditions, there is a high risk to fail the UAV for perturbations. Not only that but also it will help to optimize aerodynamic behavior for prolonging the flight endurance and effective deployment. The proper stability analysis will facilitate to increase the control flexibility.XFLR5 computational fluid dynamic software is used to do this stability analysis.

Vehicular ad-hoc networks (VANETs) play a vital role in enabling intelligent transport services (ITS). The state of art dedicated short range communication (DSRC), which is envisioned to facilitate the communication in VANETs, requires dedicated hardware. This hinders the penetration of ITS especially in developing countries. This paper studies the suitability of Wi-Fi Direct (WD), which is readily available on many smart phones, as an alternative communication technology for VANETs. Performance measures such as throughput, end-to-end delay, packet receiving/loss ratios for both WD and DSRC are presented through simulations, using network simulator NS3, and the traffic simulator Simulation of Urban Mobility (SUMO). Both single-hop and multi-hop communication is considered. The results show the potential of WD as an alternative communication technology for VANETs.

Visible Light Communication (VLC) is emerging as a next generation data transmission method for short-range communication applications. In this paper we implement and characterize two prototype stereo audio streaming methods utilizing VLC. A software architecture is developed to process and stream data. The software architecture is bridged with a hardware section, which facilitates free-space VLC channel, over a Universal Serial Bus (USB) to serial interface. A particularly attractive feature of our system is that it uses commonly available, low-cost components which enables its implementation in everyday applications.

In the face of conflict governments often face the dilemma of protecting national security as well as fundamental rights. Often, it requires infringing on fundamental rights such as the due process of arrest and detention. Additionally governments are compelled to invade privacy and curtail freedom of speech, expression and information. This is further complicated by the tendency of the enemy to masquerade as innocent civilians. This paper investigates how this has been handled in the past by foreign nations with special attention to nations that practice consitutionalism and judicial review. Based on these past actions it is possible to formulate the concept of reasonable consequentialism. Reasonable consquentialism can be used as a legal maxim to determine when a government has acted excessively in an attempt to safeguard national security by compromising fundamental rights. Furthermore, these limits can act as checks and balances between the legislature/executive and judiciary to prevent misuse by an authoritarian government.

Intrusion detection is only a starting step in securing IT infrastructure. Prediction of intrusions is the next step to provide an active defense against incoming attacks. Most of the existing intrusion prediction methods mainly focus on prediction of either intrusion type or intrusion category. Also, most of them are built based on domain knowledge and specific scenario knowledge. This thesis proposes an alert prediction framework which provides more detailed information than just the intrusion type or category to initiate possible defensive measures. The proposed algorithm is based on hidden Markov model and it does not depend on specific domain knowledge. Instead, it depends on a training process. Hence the proposed algorithm is adaptable to different conditions. Also, it is based on prediction of the next alert cluster, which contains source IP address, destination IP range, alert type and alert category. Hence …

In this work we propose and analyze a possible hardware architecture for experimentation on fine-grained reconfigurable supercomputing based on modern Field Programmable Gate Array (FPGA) technology. It is proposed a scalable cubic array of elementary computational units which are interconnected according to a tridimensional toroidal mesh network. Each computational unit is essentially formed by an FPGA plus an onboard external RAM memory. While the adjacent units in the bulk are directly interconnected through the regular FPGA IOs; the external units at opposite faces of the cubic array are interconnected by mean of high speed serial links in order to grant homogeneous data transfer bandwidth among all topologically adjacent units. Among several relevant issues we discuss the feasibility, scalability and portability.

This paper introduces a model to study both single tier and multitier wireless communication systems consisting of a multitude of wireless access points (AP), and operating according to the classical opportunistic beamforming framework. The AP locations in the proposed network model are determined by using planar Poisson point processes. The extreme value distribution of signal-to-interference-plus-noise-ratio (SINR) on a beam is of fundamental importance for obtaining performance bounds for such an opportunistic communication system. Two tight distribution approximation results are provided for the distribution of maximum SINR on a beam, which is hard to obtain due to correlation structure of the underlying inter-AP interference field, using key tools from stochastic geometry. These approximations hold for general path loss models that satisfy some mild conditions. Simulations and numerical evaluations …