Electrical and Electronic Engineering - Theses

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    Biological learning mechanisms in spiking neuronal networks
    Gilson, Matthieu. (University of Melbourne, 2009)
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    Design and implementation of millimeter-wave transceivers on CMOS
    Ta, Chien Minh. (University of Melbourne, 2008)
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    Novel all-optical signal processing schemes and their applications in packet switching in core networks
    Gopalakrishna Pillai, Bipin Sankar. (University of Melbourne, 2007)
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    Efficient scheduling for radar resource management
    Ing, Keith ( 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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    Non-invasive convulsive seizure assessment using wearable accelerometer device
    Kusmakar, Shitanshu ( 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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    Flexibility and Grid Services from Distributed Multi-energy System
    Wang, Han ( 2019)
    The energy system is in the transition towards a low carbon future. Large-scale renewable energy resources (RES) and distributed energy resources (DER) are replacing conventional generators, which places great challenges on the tra-ditional energy systems. The intermittent and uncontrollable nature of RES and lack of visibility of DER create more imbalances of supply and demand, which increase the demand for frequency control. On the other hand, the withdrawal of synchronous generators which are traditional grid services providers, further reduces system security. Additional flexibility and new grid services providers need to be sought in order to successfully integrate these emerging technolo-gies while maintaining the reliability and security of the system. Although the significance of consumer participation is seen as the “the heart of the transition”, the flexibility from consumers, DER, and other energy vectors and sectors, is yet untapped. This thesis aims to study the potential and possible ways of exploiting flexibility from such distributed multi-energy sys-tems (DMES), so as to aid the integration of RES. In this context, a comprehen-sive, integrated techno-economic modeling framework is developed, in order to identify, quantify and optimize the flexibility from DMES and develop new rel-evant business cases. This includes, a high-resolution multi-energy demand model to understand the “building block” of the energy system analysis, a mul-ti-markets, multi-services co-optimization model for optimal DMES operation, and a business case assessment model and an investment model for planning DMES under uncertainty to support new business cases. The power of the abovementioned contributions is demonstrated through various realistic case studies.
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    Novel Defenses Against Data Poisoning in Adversarial Machine Learning
    Weerasinghe, Prameesha Sandamal Liyanage ( 2019)
    Machine learning models are increasingly being used for automated decision making in a wide range of domains such as security, finance, and communications. Machine learning algorithms are built upon the assumption that the training data and test data have the same underlying distribution. This assumption fails when (i) data naturally evolves, causing the test data distribution to diverge from the training data distribution, and (ii) malicious adversaries distort the training data (i.e., poisoning attacks), which is the focus of this thesis. Even though machine learning algorithms are used widely, there is a growing body of literature suggesting that their prediction performance degrades significantly in the presence of maliciously poisoned training data. The performance degradation can mainly be attributed to the fact that most machine learning algorithms are designed to withstand stochastic noise in data, but not malicious distortions. Through malicious distortions, adversaries aim to force the learner to learn a model that differs from the model it would have learned had the training data been pristine. With the models being compromised, any systems that rely on the models for automated decision making would be compromised as well. This thesis presents novel defences for machine learning algorithms to avert the effects of poisoning attacks. We investigate the impact of sophisticated poisoning attacks on machine learning algorithms such as Support Vector Machines (SVMs), one-class Support Vector Machines (OCSVMs) and regression models, and introduce new defences that can be incorporated into these models to achieve more secure decision making. Specifically, two novel approaches are presented to address the problem of learning under adversarial conditions as follows. The first approach is based on data projections, which compress the data, and we examine the effect of the projections on adversarial perturbations. By projecting the training data to lower-dimensional spaces in selective directions, we aim to minimize the impact of adversarial feature perturbations on the training model. The second approach uses Local Intrinsic Dimensionality (LID), a metric that characterizes the dimension of the local subspace in which data samples lie, to distinguish data samples that may have been perturbed (feature perturbation or label flips). This knowledge is then incorporated into existing learning algorithms in the form of sample weights to reduce the impact of poisoned samples. In summary, this thesis makes a major contribution to research on adversarial machine learning by (i) investigating the effects of sophisticated attacks on existing machine learning models and (ii) developing novel defences that increase the attack resistance of existing models. All presented work is supported by theoretical analysis, empirical results, and is based on publications.
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    Channel modeling and transceiver design for molecular communication systems
    Cao, Trang Ngoc ( 2019)
    Molecular communications (MC) is a new communication paradigm that uses molecules to transmit information. Since MC has similar mechanisms to signaling between cells in nature and can overcome limitations of conventional communications, e.g., at a tiny scale, MC is envisioned in many applications such as targeted drug delivery, health monitoring, toxic environment monitoring, etc. To realize these applications, the principle design of MC systems, comprised of transceivers and channels, first needs to be investigated by using communication theory. In this thesis, we consider four different scenarios of MC and investigate channel models and transceiver designs of these systems. In particular, first, we model the channel between mobile transceivers in MC systems with absorbing receivers. We apply the derived stochastic channel model to the optimal release designs in drug delivery and MC systems. Second, we optimize the detection interval in a system with an absorbing receiver and external interference, e.g., a transmitter of another communication link. Third, we design fractionally-spaced equalization and sequence estimation combined with impulse response shortening to eliminate inter-symbol interference in MC systems. Fourth, we propose a chemical-reaction based detection mechanism when signaling molecules cannot be detected directly by the receiver. To design this detection mechanism, we develop an algorithm to analyze the channel where the reaction occurs. In this thesis, several analytical expressions are derived to analyze and design systems. Interesting insights into system performance are obtained from numerical results. Moreover, the transceiver designs proposed in the four scenarios can result in significant improvement in system performance, e.g., bit error rate or the amount of released molecules.