Electrical and Electronic Engineering - Theses
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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.
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ItemFlexibility and Grid Services from Distributed Multi-energy System( 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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ItemNovel Defenses Against Data Poisoning in Adversarial Machine Learning( 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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ItemChannel modeling and transceiver design for molecular communication systems( 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.
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ItemLinear Cyber-Physical System Security - Detection and Correction of Adversarial Attacks( 2019)Malicious attacks on Cyber-Physical Systems (CPS) may cause significant damage to the targeted system. In this thesis, we address the problem of attack detection as well as attack correction for multi-input multi-output discrete-time linear time-invariant dynamical systems when the attacker can inject signals (additively) to sensors and actuator signals. We consider the cases of sensor only attacks, actuator only attacks and consider the case when both sensors and actuators are attacked. We also consider the case when we have prior knowledge about a specific subset of sensors and/or actuators that is not accessible by the attacker. For each attack scenario (sensor and/or actuator attacks with or without prior knowledge), we present attack detection and correction methods. In this thesis, we present novel methods for solving the problems of attack detection and correction using the notion of ‘security index’ for various attack scenarios. The security index is a system parameter which characterises the system’s vulnerability against different types of attacks. In addition, it provides a quantitative measurement for measurement redundancy. In this thesis, we first present the security index as a representationfree system parameter. To achieve this, we use a behavioural approach as our starting point. To solve the detection and correction problems, we use a variety of system representations, but mostly Input/Latent/Output image representations.
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ItemA probabilistic approach for Wi-Fi based indoor localization( 2019)The Global Navigation Satellite System (GNSS) has been widely used to provide location information in outdoor environments, but it fails to provide reliable positioning indoors. WiFi based localization systems have attracted considerable attention because of the extensive deployment of Wireless Local Area Network (WLAN) infrastructure and the ubiquity of WiFi enabled mobile devices, offering a potentially low-cost way to track a mobile user in an indoor environment. The mainstream WiFi fingerprint based systems deployed in a practical large-scale wireless environment still encounter critical challenges in terms of intensive survey cost and large variations in a dynamic environment. Crowdsourcing, by its nature, uses heterogeneous devices in the process of surveying the site. While this reduces the time needed during the surveying phase, account needs to be taken of the variation in sensor performance. This variation results in a diversity in received signal strength (RSS) values and varying sensitivities to different access points (APs). In a complex and noisy indoor environment, for example a university building, a large number of APs can be sensed during both the survey and positioning phase, leading to a high-dimensional classification problem. In addition, because of multipath (fading channel) variation, signals from APs may not be sensed in every scan, thus resulting in a missing data problem. This PhD dissertation aims to mitigate these challenges and develop a practical room-level localization system at low deployment cost in a public wireless environment focusing on system architecture and methods. First, room-level localization is defined in terms of cell-based localization. By segmenting the floor plan into cells, training data collection is carried out by fusing RSS measurements taken within each cell by all contributed devices. A multivariate linear regression model is applied to calibrate the RSS measurements collected from different devices involved in the crowdsourced training phase. The conventional method of dealing with missing data is to set a low RSS value which will distort the RSS distribution and cause biased estimation. The Expectation-Maximization (EM) imputation method is used instead to estimate missing RSS values in the incomplete RSS measurements. Different features of the RSS spatial correlation for both fixed single location and across-cell measurements are studied. It is demonstrated that the RSS independence assumption is not valid in this context. We follow by using a high-dimensional probabilistic fingerprint for each cell, based on a multivariate Gaussian mixture model (MVGMM) to take account of spatial correlation of the signal strengths from multiple APs. The benefits of using information provided by invisible APs in differentiating between cells has been investigated, by incorporating a geometric distribution to provide a probability of existence of an AP that has not been seen in training. Finally, we design two frameworks based on hidden Markov model (HMM) and route grammar for mobile user tracking. The proposed system is able to achieve reliable and accurate localization performance. Field test results achieve a reliable 97% localization room level accuracy of multiple mobile users in a real university campus WiFi network. In addition, it has been demonstrated that an existing radio map can be adapted to localize a device new to the environment with an average matching accuracy of 94% in a multiple-surveyor-multiple-client system where client devices have not participated in the training phase.
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ItemOutput constrained extremum seeking: theory and application to UAV communication chains( 2019)Typically, the mobile ad-hoc network (MANET) refers to networks that do not rely on a pre-existing infrastructure such as wired routers to provide communication support. Ideally, a MANET is self-configuring, and nodes in the network can be dynamically added, removed, and change their locations as necessary. The goal of this thesis is to develop a distributed controller to restore a short-term communication service in a disaster-stricken area, through deploying a team of UAV-mounted communication relays. The deployed relays acting as mobile routers provide communication service for people in the disaster-stricken area. To serve more people, the deployed MANET is preferred to scatter in a highly populated region. In other words, we set the sparsely populated region as the constrained area where the deployed MANET are not preferred to enter. Since the environmental conditions such as humidity and obstacles within the signal path can affect, for instance, the path loss coefficient and the signal decay rate while modelling the signal distribution of the relay node. Without an accurate signal distribution model, deploying MANET to fixed locations using a signal-model-based approach can easily render the result suboptimal. In this regard, we proposed a novel extremum seeking control scheme, a model-free online optimisation strategy, to optimise the MANET communication quality, and meanwhile subject to the area constraint. Under reasonable assumptions and parameter tuning, the derived controller is shown to provide semi-global practical asymptotic stability guarantees for a class of multi-input multi-output dynamic plant. The developed method extends the known class of algorithms by explicitly incorporating constraints to meet the requirements of the UAV-based system described above. Numerical simulations of signal chaining using MANET with an area constraint are given to validate the proposed strategy.
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ItemHandover Analysis and Coverage Modelling in Ultra-Dense Heterogeneous Networks( 2019)Ultra-Dense Networks (UDNs) are one of the most important trends towards next generation cellular systems. It is expected that small cell densification will offload traffic from traditional macro base stations, and thus significantly boost network capacity. Despite its promising capacity gains, UDNs can lead to frequent handovers (HOs), which in turn can cause significant network overheads and a decline in user experience. With the aim of modelling handovers in the context of ultra-dense heterogeneous networks, we first propose a low-complexity analytical framework for multi-target small cell handovers. Our proposed HO framework accurately models important context-aware parameters of user velocity, small cell density and the effect of received power filtering and HO failure. To avoid load imbalance, we derive a simple HO threshold condition that leverages multiple cell load conditions while also guaranteeing the expected throughput of small cell users. Furthermore, we also propose a novel approach to model coverage regions of overlapping small cells. Based on this model, we derive the cumulative distribution function of the sojourn time in small cells using boundary length and chord length distributions of small cell coverage regions. Our model is comprehensive enough to capture both inter-tier and intra-tier HOs in small cell networks. The derived analytical results provide guidance for optimizing handover parameters based on user velocity and small cell density to reduce network overhead and improve user experience. Finally, a downlink coverage analysis of an unmanned aerial vehicle (UAV) assisted network with clustered UEs is presented. In this model, Nakagami fading is used to capture line-of-sight channels for air-to-ground communication. Simulations show that line-of-sight channels can be well approximated with minimal computation power.