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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ItemThermal Multispectral Imaging and Spectroscopy with Optical Metasurfaces and Deep Learning( 2022-12)Spectral imaging captures information in one or more selective bands across the electromagnetic spectrum, permitting the objects in the world to be identified by their absorption or reflection characteristics. Advancements in spectrally selective imaging have primarily been in colour imaging in the visible domain; however, infrared detectors have also enjoyed technological advances that position them ideally for thermal spectral imaging. Advanced spectrally selective imaging systems in longwave infrared (LWIR) thermal wavelengths of 8-14 microns can produce unique thermal fingerprints of objects by recording the heat radiation emitted from objects, thereby creating additional knowledge of the world otherwise difficult to acquire with colour cameras. Therefore, advanced spectral imaging finds important applications in precision agriculture (e.g., early detection of plant diseases), non-invasive medical diagnosis (e.g., vein and dental analysis, skin screening), mining (e.g., non-destructive testing), environmental monitoring (e.g., greenhouse gas detection) and recycling (e.g., plastic classification). However, existing LWIR multi- and hyperspectral imaging systems are expensive and bulky (with cryogenic cooling) and demand time and resources to process several images. Further, LWIR spectral imaging is hindered by the lack of materials responding to thermal wavelengths to design wavelength filters and the low resolution of thermal sensors to design a multi-band filter mosaic compared to their counterpart in the visible wavelengths. Recently, miniaturized infrared spectrometers were reported in the thermal wavelengths. However, they work only with a single isolated object using an active blackbody in the background and fail to detect multiple objects in real scenes. They collect an average emission from multiple objects using single or multiple detectors, which cannot be further resolved due to missing spatial information. There has been an ever-increasing demand for miniaturized and CMOS-compatible LWIR sensors performing imaging spectroscopy to realize their full potential with increased on-chip integration and new compact applications. In this thesis, I design and demonstrate lightweight and high-performance computational infrared imaging technology to enable joint spatial and spectral data acquisition in LWIR wavelengths. I propose and discuss promising solutions for handheld, mass-producible and affordable LWIR multi- and hyperspectral sensing systems using existing monochrome thermal sensors with a focus on plasmonic filters, sensor engineering and artificial intelligence. The first part of this thesis is focused on designing narrowband filter technology towards LWIR multi- and hyperspectral imagers. I begin by presenting optical metasurfaces and designing nano-optical filters with hexagonal lattices of hole/disk geometries to create surface plasmonic resonances in the LWIR regime. I perform comprehensive detector studies and detailed analyses of nano optical filters to accurately tailor the spectral responsivities of the LWIR plasmonic filters for imaging applications. I propose CMOS standard infrared plasmonic filters offering horizontal scalability, narrow spectral width, micron size thickness, and high transmission features. In the second part of this thesis, I explore time-resolved and spatially-resolved multispectral imaging systems for acquiring spatial image information in selective spectral bands. I substantiate the findings from the plasmonic filter simulations by experimentally realizing the novel LWIR plasmonic filters. Their instrumentation is explored by stacking into thermal image sensors through a filter wheel, and by integrating the filter mosaic into the camera to make a compact single-sensor imaging system. I experimentally demonstrate their time- or spatial-multiplexing performance in real-time and recover high-resolution multispectral images with deep imaging. In the third part of this thesis, I develop a deep learning-based LWIR imaging spectroscopy system prototype for acquiring more spectral information with selective spatial images in real time. This is a computational LWIR spectral imaging system acquired by the joint design of a snapshot multispectral imager at the hardware front, and a novel deep learning-based algorithmic spectroscopy concept for rapid spectral reconstruction at the software front. Snapshot images are acquired in selective spectral bands using LWIR plasmonic filters stacked to multiple detectors, which are further processed with deep neural network architecture to rapidly predict the spectra. The power of our deep learning-based imaging spectrometer is experimentally demonstrated by identifying four minerals: amethyst, calcite, pyrite, and quartz. The proposed technique is a simple and approximate 'uncooled LWIR thermal hyperspectral imaging system', which can be used to identify multiple objects by retrieving the spectral fingerprint in a real scene without recording a large number of images and without needing an active blackbody source. I thus demonstrate next-generation thermal sensing systems by merging nanoplasmonic sensors and artificial intelligence. Our results will form the basis for a snapshot, lightweight, compact, and low-cost hyperspectral LWIR imagers enabling diverse applications in chemical detection, precision agriculture, disease diagnosis, environmental sensing and industry vision.
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ItemPrinted Radio Transceivers( 2022-10)The application of additive manufacturing technologies in the field of radiofrequency electronics is explored. Low-cost manufacturing technology is applied at the component level to fabricate P-N junctions and combined with other components to form a simple printed radio transceiver. An analysis is performed for large-scale antenna arrays - amenable to low-resolution additive manufacturing technologies - with a view to further improve the capabilities of printed radiofrequency systems. A low-resolution PCB printer with a minimum feature size in the order of 200 micrometres and thermal curing temperature up to 515 kelvin is demonstrated capable of fabricating P-N junctions suitable for application as a varactor at 100MHz to 2GHz. P-N junctions were formed by printing N type inks with inorganic ionic compounds (zinc oxide and diindium trioxide) and P-type inks with conjugated polymers (PEDOT:PSS and pentacene) as the active ingredients. For two P-N junction chemistries (the first combining PEDOT:PSS with diindium trioxide, the second combining PEDOT:PSS with zinc oxide), device impedance characteristics showed promise for application in a 50 ohm system operating at UHF frequencies. These inks were characterised with a source meter to demonstrate a current-to-voltage characteristic according to the Shockley equation and a network analyser to quantify the depletion region capacitance and equivalent series resistance as a function of frequency and bias voltage. A simple radio transceiver was demonstrated by printing a P-N junction as the terminating impedance to a printed antenna. Fabrication was completed in less than four hours and did not require temperatures exceeding 500 kelvin. As a transmitter, load modulation was applied to the P-N junction (a varactor) to phase modulate an incident radiofrequency signal before reradiating. Over-the-air measurements quantifying the power received from the transmitter as a function of distance, carrier frequency, signal bandwidth and BER for a 2FSK signal; demonstrated reasonable agreement with a Friis-equation-based path loss model. As a receiver, an AM signal received by the antenna experienced self-mixing due to the nonlinear current versus voltage characteristic of the P-N junction. The mathematics describing the harmonic content of the demodulated signal showed reasonable agreement to SPICE simulation data and empirical measurements. To overcome inverse quartic losses that commonly afflict such backscatter-based systems, antenna array techniques were demonstrated as a viable strategy. The reflected signal from an infinite size, infinitely dense antenna array was shown to converge to an optical mirror (i.e. inverse square losses) under certain circumstances. Further reductions in radiative power loss are obtained under the condition of (retrodirective) phase conjugate matching. Under ideal conditions, a planar array of isotropic antennas can return up to 25% of the energy from an isotropic source to a collocated receiver. Accounting for incomplete coverage area, improperly specified apertures, cable loss and antenna efficiency, the model was compared against FEM simulation data and empirical measurements for a linear array, showing reasonable agreement. Thus, low-resolution additive manufacturing was demonstrated as a viable technology for fabricating radiofrequency systems. Low-cost printers in conjunction with semiconducting and conducting inks may be used to fabricate simple radio transceivers, which may be combined in retrodirective arrays for improved performance characteristics.
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ItemInformation Theory and Machine Learning: A Coding Approach( 2022-11)This thesis investigates the principles of using information theory to analyze and design machine learning algorithms. Despite recent successes, deep (machine) learning algorithms are still heuristic, vulnerable, and black-box. For example, it is still not clear why and how deep learning works so well, and it is observed that neural networks are very vulnerable to adversarial attacks. On the other hand, information theory is a well-established scientific study with a strong foundation in mathematical tools and theorems. Both machine learning and information theory are data orientated, and their inextricable connections motivate this thesis. Focusing on data compression and representation, we first present a novel, lightweight supervised dictionary learning framework for text classification. Our two-stage algorithm emphasizes the conceptual meaning of dictionary elements in addition to classification performance. A novel metric, information plane area rank (IPAR), is defined to quantify the information-theoretic performance. The classification accuracy of our algorithm is promising following extensive experiments conducted on six benchmark text datasets, where its classification performance is compared to multiple other state-of-the-art algorithms. The resulting dictionary elements (atoms) with conceptual meanings are displayed to provide insights into the decision processes of the learning system. Our algorithm achieves competitive results on certain datasets and with up to ten times fewer parameters. Motivated by the similarity between communication systems and adversarial learning, we secondly investigate a coding-theoretic approach to increase adversarial robustness. Specifically, we develop two novel defense methods (eECOC and NNEC) based on error-correcting code. The first method uses efficient error-correcting output codes (ECOCs), which encode the labels in a structured way to increase adversarial robustness. The second method is an encoding structure that increases the adversarial robustness of neural networks by encoding the latent features. Codes based on Fibonacci lattices and variational autoencoders are used in the encoding process. Both methods are validated on three benchmark datasets, MNIST, FashionMNIST, and CIFAR-10. An ablation study is conducted to compare the effectiveness of different encoding components. Several distance metrics and t-SNE visualization are used to give further insights into how these coding-theoretic methods increase adversarial robustness. Our work indicates the effectiveness of using information theory to analyze and design machine learning algorithms. The strong foundation of information theory provides opportunities for future research in data compression and adversarial robustness areas.
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ItemA Blockchain-based Solution for Sharing IoT Devices( 2022)The Internet of Things (IoT) includes billions of sensors and actuators (which we refer to as IoT devices) that harvest data from the physical world and send it via the internet to IoT applications to provide smart IoT solutions. These IoT devices are often owned by different organizations or individuals who deploy them and utilize their data for their own purposes. Procuring, deploying, and maintaining IoT devices for exclusive use of an individual IoT application is often inefficient, and involves significant cost and effort that often outweigh the benefits. On the other hand, sharing IoT devices that are procured, deployed, and maintained by different entities (IoT device providers or simply providers) is efficient, cost-effective and enables rapid development and adoption of IoT applications. Currently, most IoT applications themselves procure, deploy, and maintain the sensors they need to collect the IoT data they require as there is limited support for sharing IoT devices and their costs. Therefore, there is a need for developing an IoT device sharing solution that allow IoT applications to 1) discover already deployed IoT devices, 2) use discovered IoT device data (IoT data) for their own purposes, and 3) share-cost of IoT device deployment via a “pay-as-you-go” model similar to cloud computing. To address the aforementioned problems, in this thesis we propose, develop, implement, evaluate, and validate a solution namely IoT Devices Sharing (IoTDS). IoTDS enables scalable and cost-efficient discovery and use of IoT devices by IoT applications. IoTDS incorporates services for IoT device registration, IoT device query, IoT device payment and IoT device integration. To support these services, we propose 1) a novel IoTDS ontology, an extension of Semantic Sensor Network (SSN) ontology to describe IoT devices and their data to enable IoT device registration and query services. The IoTDS ontology also provides for describing the payment and integration information that is used by IoT device payment and integration service; 2) a special-purpose blockchain namely IoTDS Blockchain that has been developed specifically to support the needs of the IoTDS services i.e., supporting decentralised and scalable query, integration and payments services for IoT devices and applications. Specifically, IoTDS Blockchain incorporates a distributed semantic triple store and functions to register IoT devices, and specialised transactions for supporting IoT device payments (we propose a new cryptocurrency namely SensorCoin); 3) a novel IoT marketplace (IoTDS marketplace) that offers an interface and a protocol (IoTDS protocol) to support the interactions between IoT devices, IoT applications, IoTDS Blockchain, and IoTDS services. IoTDS solution 1) facilitates IoT devices deployed across the globe by different providers to be queried by any IoT application; we term this global, 2) enables via the IoTDS Blockchain a non-ownership model; we term this IoT-owned i.e., no individual/organisation owns it or controls it, 3) able to handle the vast and ever-increasing number of IoT devices and IoT applications; we term this scalable, and 4) able to support and integrate heterogenous of IoT devices and their data; we term this interoperable. In this thesis, we provided implementation details of IoTDS services that includes IoTDS ontology, marketplace, and blockchain. the IoTDS ontology has been modelled using Protegee and Owl and implemented using RDF. The IoTDS Blockchain and corresponding functions are implemented using NodeJS and Web Socket. The IoTDS marketplace and corresponding IoTDS protocol has been implemented using NodeJS and MQTT. We conducted large-scale experimental evaluation of IoTDS solution by deploying it on Nectar cloud (20 instances) using both real and simulated (5,000,000) IoT devices and IoT applications (5000) to assess and validate the scalability and performance of IoTDS. We also developed and validated a mathematical model that can be used to estimate the performance of the IoTDS with the increasing number of IoT devices. Experimental outcomes show that the proposed IoTDS solution performs great (linear scalability) in supporting global discovery, use, and cost-share of large numbers of IoT devices and applications. The main contributions of this thesis are 1) an IoTDS solution for sharing IoT devices, 2) a survey of techniques for supporting IoT device sharing; 3) a special purpose Blockchain to support sharing of IoT devices, 4) a novel Marketplace to support registration, querying, payment, and integration of IoT devices, 5) a novel protocol for autonomic control of integrating IoT devices and fetching their data, and 6) an implementation and experimental evaluation of the IoTDS solution.
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ItemAdvanced Control of Wind Energy Conversion Systems for Grid Frequency Support( 2022)Renewable energy sources have been increasing rapidly recently. These generation units which are converter based technologies are replacing the conventional generator systems in the power grid. As a result fewer generators are participating in frequency regulation services and the frequency deviation from its nominal value has increased lately. An example of these converter based technologies are wind turbines which normally operate in maximum power point tracking meaning that they generate as much energy that they can possibly harvest from the wind. The problem of frequency deviation has increased the attention of many researchers to tackle the problem and investigate the possibility of wind turbines to participate in frequency regulation services. Frequency regulation is done by controlling the appropriate active power supplied to the transmission lines. There are various responses to an event that happens in the grid. While the inertial response and primary frequency response are the first two controls of the system to bring the frequency back to its operating point, secondary frequency regulation aims to eliminate steady state error of the frequency from its nominal value. Secondary frequency regulation is managed by the market operator. This means that the generation units have to track a power command signal generated by the market operator. This problem could be modeled as a tracking problem, since the wind generation unit has to track a power command signal sent by the market operator and reject the disturbances such as wind variation or fluctuations of the terminal voltage of the converter based generators. The problem is inherently difficult due to the time varying power commands as references and the stochastic disturbances such as wind variations. Technologies such as Light Detection And Ranging (LIDAR) has absorbed the attention of many researchers. This technology provides preview information for the coming wind disturbances up to several seconds ahead. Methods to use this information for better tracking the power command signal or better performance in maximum power point tracking has been the topic of many research articles so far. In this thesis, we will investigate the capability of a classical control methodology to provide wind turbines with the capability to participate in frequency regulation services. This control methodology is known as exact output regulation. It considers a time invariant plant model and has the capability to track a known reference signal and reject disturbance signals. The wind information could be modeled using an exo-system and produce the disturbance signal and the market operator will produce the reference signals to be tracked. This thesis will have two different scenarios considering the problem of secondary frequency regulation. In the first scenario the wind turbine is modeled by a high fidelity aero-elastic simulator known as Fatigue, Aerodynamics, Structures, and Turbulence (FAST) in conjunction with a simple generator. In this section the control is basically on the wind turbine and LIDAR wind preview information is also used to obtain the disturbance signal. In the second scenario we will investigate adding a Doubly Fed Induction Generator (DFIG) instead of a simple generator and design the control for both the generator and wind turbine. We have investigated two different types of output regulation which are well suited for each problem. To have a realistic results we have employed FAST 5 MW reference for the turbine model. The DFIG is implemented in MATLAB Simulink and to simulate stochastic wind signals we have used Turbsim which is able to generate different practical classes of wind signals. Then the two different problems has been addressed and compared against the performance of baseline controllers. The baseline controllers are the most widely used methods for wind turbine active power control. Different control objectives are defined in each chapter for the purpose of comparison between the proposed controller and the baseline controllers. These include the root mean squared error between the generated power and the power command signals, fatigue loads and actuator usage. Results show that the proposed output regulation methods in both scenarios are able to track the command signals better than the baseline controllers. In terms of the fatigue loads, in the first scenario the controller is able to reduce the fatigue loads in most of the considered fatigue cases such as blade and tower bending moments as well as the low speed shaft torque, however in the second scenario the fatigue loads for tower and blade bending moments were similar to baseline controller and only the low speed shaft torque was improved. The input command signals was smoother in both of the scenarios when using the proposed output regulation control techniques.