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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ItemResource allocation for multiuser OFDM systems(University of Melbourne, 2006)
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ItemEfficient scheduling for radar resource management( 2019)Sensor scheduling and its application in radar has stemmed from the desire to achieve continued improvement in radar capability, particularly for multi-function radar technologies. Adaptive and cognitive radar represent the latest stage in radar evolution, invoking a closed-loop scheduling to replicate the perception-action cycle of cognition. Radar resources are dynamically selected to interrogate the scene before the reflected signals are analysed to inform action in future epochs. Whilst many authors have proposed systems for adaptive and cognitive sensing, the signal processing and computing aspects of modern radar make closed-loop scheduling schemes challenging to realise on the time scales of which radar operates. This thesis is focused on the implementation aspect of the sensor scheduling problem for radar. The work is presented in three parts that investigate problems related to this issue. In the first part, we consider linear frequency modulation (LFM) range-Doppler coupling in radar and the associated range bias in measurements using this waveform. A maximum likelihood based estimator that exploits this error is proposed to jointly estimate target range and range-rate using a train of diverse LFM pulses. Efficient methods to select diverse pulse trains based on established adaptive radar waveform cost functions are provided. Pipeline computing architectures provided by high bandwidth solutions comprising of multiple parallel processors are well suited to complex independent processing applications. Pipeline processing for radar has been previously utilised for computationally intensive applications such as space-time adaptive processing. In the second problem, we investigate the time costs associated radar signal processing and closed-loop sensor scheduling for a knowledge based diversity scheme. A universal cost for the processing activities is defined to recognising the delay and subsequent repercussion it can have on the feedback cycle of an adaptive system is investigated. We propose two alternate parallel processing architectures that alleviate the narrow time burden between measurement epochs for a sequential feedback loop. The performance degradation of the proposed architectures are investigated in an adaptive radar scenario for various time costs. Clutter represents the unwanted signals reflected from a radar scene. Efficient clutter modelling is important in implementation of adaptive radar as to minimise delay in the target detection process. In the open ocean, sea clutter can be represented using a compound-Gaussian clutter model. In the third problem, we propose a parsimonious parametric model for sea clutter texture that is suitable for high-resolution radar backscatter at low grazing angles in the open ocean. By relating the clutter by to its physical source, we exploit spatio-temporal relationships to propose an efficient algorithm for the estimation of the spectral components for the parametric texture model. Validation is performed by comparing the predictive fit for our estimator with a series of temporal estimators and a non-parametric estimator using measured sea clutter data from the Atlantic Ocean.
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ItemNon-invasive convulsive seizure assessment using wearable accelerometer device( 2018)Epilepsy can be characterized by recurrent and unprovoked episodes of dysfunctional neuronal activity in time coupled with a change in behavior and altered state of consciousness. Epilepsy is one of the most prevalent neurological disorders. The prevalence of epilepsy is approximately 50 million worldwide. One of the major disabilities attributed to epilepsy is the unpredictability of epileptic seizures (ES). A person cannot call for help during a seizure, often suffering injuries due to falls, burns, tongue biting, etc.; thus, independent living is impaired. A more serious consequence is epilepsy-associated mortality. The increased mortality in epilepsy is attributed mainly to direct causes, i.e., accidental death (drowning, motor vehicle accidents, serious head injuries) and sudden unexpected death in epilepsy (SUDEP). Evidence suggests that appropriate and timely intervention following a seizure can reduce the risk of epilepsy-associated injuries and mortality. Another class of seizures known as psychogenic non-epileptic seizures (PNES) are involuntary events that share diagnostic similarities with generalized epileptic tonic-clonic seizures (GTCS). PNES events have a causal association to sporadic attacks resulting from autonomic malfunction often linked to major psychosocial distress. PNES has a prevalence of 1-33 cases per 100,000, accounting for 5-20% of patients thought to have epilepsy. Patients with PNES require treatment tailored to address the associated psychosis. There is the potential for severe harm from the adverse effects of the anti-epileptic drugs (AEDs) prescribed to patients with PNES, as well as increased risk of morbidity and mortality due to intubation from prolonged seizures. In this thesis, we describe the development of a wrist-worn accelerometer (ACM)-based system for the automated detection and classification of seizures. The first section of this thesis describes the development of a wireless remote monitoring system based on a single wrist-worn ACM sensor. A novel seizure detection algorithm was proposed and validated on 5576 h of ACM data recorded from 79 patients admitted to the Epilepsy Monitoring Unit at Royal Melbourne Hospital, Melbourne, Australia. The wearable ACM sensor achieved high seizure detection sensitivity and specificity that correlated with the gold-standard diagnosis. The study showed that a single wrist-worn ACM sensor can efficiently detect different types of convulsive seizures and can differentiate seizures from activities of daily living. In addition, it demonstrated the feasibility of a unobtrusive system for continuous remote monitoring and assessment of patients with epilepsy. The second section describes novel features based on capturing the temporal variations in rhythmic limb movement during a seizure, to differentiate GTCS from convulsive PNES. We observed that the manifestation of GTCS can be characterized by an onset that involves increased muscle tone, usually accompanied by irregular and asymmetric jerking, followed by tremulousness that translates into clonic activity before subsiding gradually. By contrast, no clear distinction could be seen between different phases of convulsive PNES events. Based on these observations, we proposed two new indexes that capture the onset and subsiding behavior of an event: (1) tonic index (TI), and (2) dispersion decay index (DDI). The study showed that the TI and DDI can differentiate GTCS from convulsive PNES. Importantly, the study showed that different phases of a seizure contain clues for differential diagnosis of PNES, which is an expensive clinical procedure. In addition, these results highlight the feasibility of wearable ACM based device for outpatient diagnosis of convulsive PNES. Despite rapid technological advancement in surgical techniques and discovery of anti-epileptic medication one-third of the epileptic patients are forced to live with seizures. The unpredictability and risk of injury (falls, head injuries, etc.) associated with seizures are the major contributors to poor quality of life (QOL), requiring round-the-clock monitoring by caregivers. Therefore, in the third section of the thesis we present a novel algorithm for real-time onset detection of GTCS events using a single wrist-worn ACM-based device. The algorithm was tested on 5576 h of ACM data from 79 patients and detected 21 of 21 (sensitivity: 100%, FAR: 0.76/24 h) GTCS events from 12 patients at 7 s from onset. Taking into consideration the challenges to real-time onset detection of seizures, it is anticipated that the proposed wrist-worn ACM-based system would aid efficient real-time remote monitoring of epileptic patients, improving their (QOL) and acting as a seizure triggered alarm and therapeutic system.
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ItemProbabilistic Energy Management Systems in PV-Rich Communities( 2019)Increasing popularity of renewable and Distributed Energy Resources (DER) and introduction of smart meters are changing the way electricity distribution grids have been operated. The stochastic nature of renewable sources adds new challenges to distribution grid operations. Communities, which are defined as groups of individual customers that utilise renewable energy sources, are especially impacted by these challenges due to their lack of scale and know-how. In this thesis, we focus on PV-rich communities that have a number of end-users equipped with rooftop photovoltaic (PV) panels without any local storage. For such PV-rich communities, it would be beneficial to model and analyse the statistical properties of DERs and their demand. Historical data can help understand the stochastic behaviour of community DER and demand, and model them as random sequences. These random sequences are used as a basis for optimal decision-making on financial contracts between communities and energy generators. Unlike stochastic optimisation, forecasting, and the Monte Carlo simulation, our methodology enables PV-rich communities to conduct long-term planning, spot-market exposure risk analysis, fine-tuning power purchase agreements, and a good understanding of statistical properties of distribution networks utilising PV systems. Our approach benefits from data science and uses models and existing data in a computationally efficient manner. With the help of our proposed model-based tool, communities are able to plan their long term financial agreements without conducting a high number of simulations.