Electrical and Electronic Engineering - Theses
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ItemPolarization effects and high speed transmission in coherent optical OFDM systems(University of Melbourne, 2010)
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ItemBiological learning mechanisms in spiking neuronal networks(University of Melbourne, 2009)
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ItemDesign and implementation of millimeter-wave transceivers on CMOS(University of Melbourne, 2008)
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ItemMillimeter wave CMOS VCO and PLL design and phase noise mitigation techniques(University of Melbourne, 2008)
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ItemNovel all-optical signal processing schemes and their applications in packet switching in core networks(University of Melbourne, 2007)
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ItemResource allocation for multiuser OFDM systems(University of Melbourne, 2006)
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ItemSensor processing for localization with applications to safety( 2017)Heavy industries such as construction, mining and transport typically have dangerous work environments, where injuries and fatalities are rampant despite all the rules and regulations. Such mishaps are largely due to human negligence and improper monitoring of the work place. Injuries are also more likely when man and machine operate together. To ensure safety, a framework is needed capable of tracking moving objects around a user with centimeter accuracy. The sensor should be small enough to be easily incorporated in workers safety equipment, and robust against all the random movements of the user and the objects in the surrounding area. This thesis addresses the issues in developing a framework of a low cost smart helmet for workers in dangerous work environments. The techniques developed for safety helmets are also directly applicable to light-weight navigation systems needed for tiny drones. At its core, we have developed a framework and algorithms using simple and cheap continuous wave (CW) Doppler radars to obtain the precise location of static and dynamic obstacles around a user. CW Doppler radars only provide relative radial velocity, so the first issue is to determine the conditions under which the position of a target is observable. We have also designed, compared and analyzed different nonlinear trackers to determine which works better under certain scenarios. We explore how instantaneous frequency measurements can be obtained from rate of phase change in returned waves of CW radars. To this end, we performed various simulations with different order models and results showed that we can successfully localize walls with sub-centimeter accuracy. Moreover, we show that random human head movements and walking do not pose much threat to estimation accuracy and can be easily handled through added noise in the system.
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ItemVoltage stability issues in power grids: analysis and solutions( 2017)Voltage Stability (VS) is gaining increasing significance in today's power systems, which are undergoing sizeable growth in power consumption and higher integration of renewables. Economic and environmental barriers impede new investment on network infrastructure to keep up with the load growth and renewables' intermittency. As a result, many of the power systems around the world are being operated close to their VS limits. This has made voltage instability an ever-existing operational problem for many power systems, and reveals the need for smarter and more efficient approaches to analyse and ensure VS. The significance of VS has been well demonstrated by many evidences of real-life incidents of power system instability which have been associated with VS. From an analytical perspective, with the increasing variability of today's power systems, with higher levels of intermittent renewables integrated into the grid, more frequent evaluation of power system's VS condition is imperative. Hence, more efficient VS evaluation tools, in terms of speed, accuracy, and automated applicability are needed. Also, from a practical point of view, prohibitive cost of upgrading power systems' infrastructure necessitates taking smarter, more efficient alternative approaches to ensure VS of power systems. This includes operating the existing power system components through intelligent, active network management (ANM) schemes. Continuation power flow (CPF) is the conventional, mostly used approach of steady-state VS analysis. CPF algorithm and all its improved versions, however, suffer from high complexity and relatively long execution time. Considering the need for more frequent VS analysis in today's renewable-rich power systems, in this thesis, a more efficient approach of plotting the P-V curves, and identifying VS limits, i.e. saddle-node bifurcation (SNB) and limit-induced bifurcation (LIB) points, of power systems is proposed. The method is based on standard Newton-Raphson power flow (NR-PF) algorithm and, thus, relaxes all the complexities of the existing CPF methods. It offers much reduced execution time, high accuracy, automated applicability, and ease of implementation and comprehension. Several novel, simple techniques are used in the proposed approach to identify both SNB and LIB points. The method is tested on several, including a large-scale, power systems and its performance is compared with some established CPF methods. Modal Analysis (MA) is another commonly-used approach that can be used to identify the weak areas of a power system, from a VS viewpoint. This thesis proposes two improved MA methods, applicable to radial distribution systems. The proposed MA methods, unlike the original MA, do not ignore active power variation and allow taking into account any combination of active and reactive power variations. As a result, the proposed methods improve the accuracy of the original MA, in identifying the best buses to apply active or reactive compensation, with the aim of improving the distribution system's voltage stability margin (VSM). On the other hand, the ongoing technological advances in energy storage systems (ESSs) has made the grid integration of these devices technically and economically more viable. Accordingly, in this thesis, optimal placement and operation of ESSs in power systems with possible embedded wind farms, with a VSM improvement viewpoint, is carried out. The probabilistic nature of the wind is taken into account, through the probability density function (PDF) of the wind farm's output power. A combination of MA and CPF is used to identify the best placement of ESS in the network. A new method of power sharing between the ESSs, based on their effect on system's VSM, is proposed too. The required power injection of ESSs, at an optimal power factor (PF), to ensure a pre-specified minimum required VSM, is also calculated at all load-wind levels. Furthermore, in this thesis, the problem of ESS placement is formulated as a probabilistic optimization framework, through which optimal placement, sizing, and operation of ESS devices in wind-embedded distribution systems are carried out. The main objective of the allocation problem is to minimize the required power and energy ratings of ESSs to be installed, such that a desired level of VSM is always ensured. The reactive power loss and reactive power import from the upstream network are also minimised through a multi-objective optimization framework. Wind uncertainty is accounted for through optimally generated wind power scenarios and using risk-based stochastic optimization approach. Besides, ANM tools, such as tap position of on-load tap changers (OLTCs), modelled by using a new method, and reactive power capabilities of both ESS devices and wind farms, are used as additional means to reduce the required ESS size. Finally, dynamic simulation is carried out to demonstrate the effectiveness of ESS devices to dynamically improve VS of power systems. The effects of induction motor (IM) loads, fixed speed induction generator (FSIG)-based wind turbines (WTs), and over-excitation limiter (OEL) of synchronous generators (SGs), on the power system's short term voltage stability (ST-VS) are evaluated. Then, the use of ESSs to provide dynamic voltage support (DVS) to power system during and after large disturbances, as a countermeasure against short term voltage instability, is investigated. In order to do so, systematic control of ESS, to inject any desired active and reactive powers into the system, is carried out. The effects of implementing fault ride through (FRT) and time-overload (TOL) capabilities of ESS, as well as the ESS's PF, on ST-VS are also analysed.
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ItemEnergy and carbon footprint of ubiquitous broadband( 2017)This thesis concerns ubiquitous broadband in Australia. We use a comparative-static computable general equilibrium model to analyse the economic effects, and to derive the environmental effects of the National Broadband Network (NBN) in the short term and long term. While investment is significantly increased due to NBN deployment in the short term, overall economic activity increases marginally. We find that national greenhouse gas (GHG) emissions are effectively unchanged by the construction of the NBN. We run model long-run simulations to analyse the impact of new services and new ways of working that are enabled by the NBN. The simulation results are dependent on our estimates of the incremental impact of the NBN on service delivery. For this purpose, we map the coverage of broadband in Australian regions using an open-source geographical information system (GIS). We then define two sets of service requirements and determine service availability across regions with and without the NBN. The results show that the NBN produces substantial benefit when services require higher bandwidths than today’s offerings to the majority of end users. In this scenario, the economic effects of productivity improvements facilitated by electronic commerce, telework or telehealth practice made widely available through the NBN will be sufficient to achieve a net improvement to the Australian economy over and above the economic cost of deploying the NBN itself. If, on the other hand, the NBN has a significant effect only on the availability of entertainment services, then the net effect will not be sufficient to outweigh the cost of deployment. We find that national GHG emissions increase with service availability and are higher with the NBN. We construct an NBN power consumption model to estimate the purchased electricity and GHG emissions of the NBN network in the long term post NBN deployment. We find that the NBN network increases energy demand and GHG emissions marginally. The main contributions resulting from this thesis relate to the model simulations. Detailed analysis of the economic and environmental effects of the NBN on the Australian economy provides policymakers and researchers new insights based on a state-of-the-art methodology. Beyond the regional scope of this thesis, the results provide fresh evidence of the rebound effect and GHG emissions abatement potential of ubiquitous technologies such as broadband. While this thesis points at the possible trade-offs when evaluating economic policy faced by various individuals or groups, an efficient way to achieve a more sustainable outcome is to address externalities related to GHG emissions directly by way of implementing appropriate environmental policies.
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ItemMedical image processing with application to psoriasis( 2017)Psoriasis is a chronic, auto-immune and long-lasting skin condition, with no clear cause or cure. Psoriasis affects people of all ages, and in all countries. According to the International Federation of Psoriasis Associations (IFPA), 125 million people worldwide have psoriasis. The severity of psoriasis is determined by clinical assessment of affected areas and how much it affects a person's quality of life. The most common form is plaque psoriasis (at least 80% of cases), which appears as red patches covered with a silvery white build-up of dead skin cells. The current practice of assessing the severity of psoriasis is called "Psoriasis Area Severity Index" (PASI), which is considered the most widely accepted severity index. PASI has four parameters: percentage of body surface area covered, erythema, plaque thickness, and scaliness. Each measure is scored for four different body regions: head, trunk, upper-limbs, and lower-limbs. Although, PASI scores guide the dermatologists to prescribe a treatment, significant inter- and intra- observer variability in PASI scores exist, and are a fact of life. This variability along with the subjectivity and time required to manually determine the final score make the current practice inefficient and unattractive for use in daily clinics. Therefore, developing a computer-aided diagnosis system for psoriasis severity assessment is highly beneficial and long over due. Although, research in the area of medical image analysis has advanced rapidly during the last decade, notable advances in psoriasis image analysis and PASI scoring have been limited and only recently have started to attract the attention. In this thesis, we present the framework of a computer-aided system for PASI scoring using 2D digital skin images by exploring advanced image processing and machine learning techniques. From one side, this will greatly help improve access to early diagnosis and appropriate treatment for psoriasis, by obtaining consistent, precise and reliable severity scoring as well as reducing the inter- and intra- observer variations in clinical practice. From the other side, this can improve the quality of life for psoriasis patients. The framework consists of (i) a novel preprocessing algorithm for removing skin hair and side clinical markers in 2D psoriasis skin images, (ii) psoriasis skin segmentation method, (iii) a fully automated nipple detection approach for psoriasis images, (iv) a semi-supervised approach for erythema severity scoring, (v) a robust, reliable and fully automated superpixel-based method for psoriasis lesion segmentation, and (vi) a new automated scale scoring method using bag of visual words model with different colour and texture descriptors.