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Design an intelligent micromachine that can self-power and sustain from environmental energy scavenging to achieve an autonomous device that can communicate at will with peers indefinitely. Explore sleep/wake hibernation strategies coupled with food scavenging off-grid traits to identify the tightest work to sleep efficiency schedule, incorporating adaptive reconfiguration to manage significant environmental impacts. Capture, store and manage background radiations and stray RF signals to feed on in a continued effort to make intelligent survival decisions and oversee management protocols. Ensure that every micro Watt of usable energy gets extracted from every part of the harvest and then forward-scheduled it for productive use. Finally, employ natures tricks and experience to introduce essential personality traits, pursuing maximising survival numbers and increasing dispersal target area sizes of large self-sufficient wireless sensor deployments. This research intends to provide a closely coupled software-hardware foundation that aids implementers in intelligently harnessing and using tiny amounts of ambient energy in a highly autonomous way. This platform then continues on to explore ways of maximising the efficient usage of the harvested energy using various hibernation/wake strategies and then making objective comparisons with proposed intelligent energy management protocols. Finally, the protocol extends to enable the device to manage its personal survival possibilities so the devices can use an evolutional personality-based approach to deal with the unknown environmental situations they will encounter. This work examines a machine that can self-power and sustain from environmental energy scavenging with the aim to live forever. Living forever implies a brain (microcontroller) that can manage energy and budget for continuous faculty. With these objectives, sleep/wake/hibernation and scavenging strategies are examined to efficiently schedule resources within a transient environment. Example harvesting includes induced and background radiation. Intelligent, biologically-inspired strategies are adopted in forward-scheduling strategies given temporal energy relative to the machine's function (the Walton).

The United Nation's 7th Sustainable Development Goal (SDG7) is to ensure access to affordable, reliable, sustainable and modern energy for all. A key challenge in achieving SDG7 is providing access to the estimated 0.9 billion people living in rural areas without access to electricity. In Nepal, factors including political unrest, challenging geography, and a weak economy, have limited electricity access. However, micro-hydropower has been used to provide electricity in rural areas. The technology is mostly manufactured locally, with the Nepali government supporting communities with a subsidy that funds approximately 50% of the total project cost. Manufacturing companies fulfil the roles of designer, manufacturer, and installer with the local community providing labour during the construction phase. The combination of locally manufactured equipment that is subsequently owned and operated by the community provides a unique range of challenges. This thesis explores the opportunity to improve the reliability of the technology and the operational sustainability of projects. To do so, a new design methodology is proposed that allows an existing technology, the Turgo turbine, to be adapted for local manufacture and use in Nepal. The proposed design methodology, known as 'Design for Localisation', frames the direction of the thesis. Firstly, an understanding of the local context is developed. A field-based methodology is developed and used at 24 micro-hydropower plants to consider factors affecting their operational sustainability. Findings from the site study are combined with a detailed evaluation of the project process, using available literature and interviews with stakeholders, resulting in an improved understanding of how strengths and weaknesses in the operational sustainability of plants develop. Secondly, design solutions for local manufacture are developed. A survey of manufacturing companies is used to identify the local availability of materials and processes. These findings indicate determine the method for manufacture of the turbine blade. Subsequently, computational fluid dynamics is used to optimise the performance of the runner, increasing efficiency from 69.0 to 82.5%. In collaboration with a local manufacturing company, a locally appropriate design is developed and manufactured. CAD, the internet, and additive manufacturing are used to transfer and physically replicate the digital design as a mould for casting. Thirdly, local testing and monitoring is used to evaluate the design. A hydrodynamic testing rig is developed at Kathmandu University. The locally manufactured Turgo turbine runner and an imported off-the-shelf Turgo turbine runner are tested under the same conditions, and the results compared. A site-based installation is used to understand the performance of the runner once integrated with ancillary sub-systems, and in environmental conditions. Finally, the efficacy of the Design for Localisation process and its further application is considered. A scaling method, allowing the Turgo turbine design to be adapted for any site with appropriate geography, is presented. An open-source approach is proposed to improve the availability of the design, enable subsequent improvement and further local adaptation to other contexts.

High-temperature (HT) power converters are increasingly important in extreme environments, such as electric vehicles, aviation, etc. Due to the limited temperature operation beyond 150 °C in Si-based devices, GaN-based power transistors are expected to be excellent candidates for power converters at high temperatures over 200 °C in electric vehicle applications. HT power converters with self-contained functionality (power, driver, microcontroller, sensors, etc.) without external heatsink or cooling systems are increasingly essential owing to reduced size and cost. The lateral AlGaN/GaN-based high electron mobility transistors (HEMTs) have been regarded as promising candidates in high frequency, high power density, and HT applications. GaN smart power integrated circuit (IC) provides an effective solution to achieve a systemon-chip scheme for HT power converters. This thesis uses normally-off GaN transistors with a recessed metal-insulated-semiconductor (MIS) gate, and it focuses on the development of GaN DC-DC converters with integrated gate drivers for HT power converters in extreme environments. To evaluate the recessed MIS gate for high-temperature GaN power converters, the impact of etch depth on the performance of mobility and RON is systematically studied at high temperatures. The mechanisms of carrier scattering are discussed at different etch depths, and full recess with dielectric engineering is proposed to improve the stability of GaN IC. On the lateral GaN smart power IC technology platform, this thesis focuses on three parts for HT power converters including 1) an integrated gate driver for a GaN boost converter; 2) integrated gate drivers with a half-bridge stage for a synchronous GaN buck converter; 3) an integrated technique of deadtime management for a synchronous GaN buck converter. Firstly, the GaN boost converter with the optimized gate driver exhibits a voltage conversion from 5 to 11 V at 100 kHz, and only 11% reduction of output voltage is observed at high temperatures up to 250 °C. Then, the synchronous GaN buck converter with an integrated half-bridge stage achieves a voltage downconversion with an input voltage of 25 V, and it shows good thermal stability and almost no reduction of output voltage at temperatures up to 250 °C, with a large gate swing of 10 V. Lastly, an integrated GaN buck converter with a no deadtime technique (NDT) exhibits a maximum efficiency of 80 % at high temperatures up to 250 °C , with an input voltage of 30 V at 100 kHz. At high temperatures, the optimized GaN NDT converter shows better performance than a synchronous GaN buck converter with a fixed deadtime technique (FDT) at high load currents, in terms of smaller voltage overshoots and oscillations of gate drivers and better converter efficiency as well. The proposed GaN NDT converter uses one control signal and provides a simple and effective method of deadtime management for high-temperature power converters.

In practice, sensorless drive systems of interior permanent magnet synchronous motors (IPMSMs) are usually working in highly utilized or continuous heavy duty conditions where various disturbance exist, such as fast changing uncertain loads, inverter losses, measurement noise, model inaccuracy, etc. These issues could reduce the performance and stability, which become the main vulnerability of sensorless drives. Direct calculation method or phase locked loop (PLL) is conventionally adopted to estimate the speed and position at the last stage of sensorless estimation which concerns the overall performance. Without compensation or gain-scheduling, these conventional methods are unable to accurately estimate the rotor position and speed for the sensorless drive systems. The linear extended state observer (LESO) can be introduced to replace the PLL for performance improvement. However, the fast changing uncertainties cannot be observed thoroughly and very high gains are more likely needed which may excite noise in the control channel. In the thesis, a nonlinear third-order extended state observer (NESO) is proposed for the further enhancement of the rotor position and speed estimation for sensorless IPMSM drives. The disturbance observation bandwidth of the proposed observer is expanded by deploying a gain optimization method. The measurement noise excited in the control loop is suppressed effectively. Comparative experimental results between the LESO and NESO verify the effectiveness and improvement of the proposed NESO against rapid speed variations, sudden load variations and down speed challenge. The sensorless operation of IPMSM with different saliency is tested to validate the capability of the NESO. However, the complexity of the parameter configuration and analysis may limit the application of such method. Therefore, a third order super-twisting extended state observer (STESO) is then proposed to reduce the difficulty of implementation without sacrificing the advantages of nonlinear properties. Utilizing the high-order extended state and super-twisting algorithm, fast convergence and disturbance estimation can be achieved in STESO. The dynamic performance and robustness of the STESO against sudden speed and torque variations is comparable to that of the NESO. Experimental results focus on the disturbance estimation ability and robustness of STESO. In comparison with LESO, the enhancement of STESO can be markedly noticed in the experimental test. Moreover, the extremely low speed operation is achieved with the aid of high frequency signal injection in the existing unified sensorless structure. A high frequency current controller and a high frequency extended EMF observer are proposed to estimate the excited extended EMF. The proposed STESO is used to retrieve the position and speed. The effectiveness of such scheme is verified by the experimental results.

Two-dimensional electron gases (2DEGs) at the ZnO/ZnMgO interface are promising for spintronics and quantum computing applications due to the combination of low spin-orbit coupling and high electron mobility. This thesis proposes that these 2DEGs could be used to achieve voltage-tuneable Josephson junctions (JJs). To achieve this, the 2DEG needs a high superconducting coherence length, which requires both a high carrier concentration and electron mobility. This thesis summarises methods to enhance ZnO 2DEG quality using pre-growth processing on ZnO substrates, and testing ZnO/ZnMgO heterostructure fabrication on alternative substrate materials. This is achieved through rapid thermal treatment of commercially available ZnO and by exploring growth on a- and c-plane sapphire. Rapid thermal annealing of ZnO substrates is shown to greatly improve substrate quality. Devices produced on thermally treated substrates demonstrate 2DEG behaviour with mobilities and carrier concentrations of 4.8 × 10⁴ cm²/Vs and 5.05 × 10¹² cm⁻² respectively. This translates to a long mean free path (1800 nm) and a long clean-limit coherence length (120 nm) at 2K, making the sample well suited for voltage-tuneable Josephson junctions. The high 2DEG mobility is attributed to a reduction in substrate defect density due to the thermal treatment. Later chapters show how thin MgO buffer layers can be used to achieve Zn-polar ZnO layers on c-plane sapphire. The Zn-polar layers are used to produce ZnMgO/ZnO heterostructures which could provide the basis of 2DEG formation on non-ZnO substrates. Practical means of controlling Zn-polarity on a-plane sapphire could not be found, so an alternative heterostructure is produced which utilises polarity discontinuity at the ZnO/ZnMgO interface. Finally a road-map for future research is presented. This includes suggestions for retrofitting working ZnO 2DEG devices to measure proximity superconductivity through the 2DEG. It is explained how this proximity superconductivity can be tuned through electrical gates, and how this component could be used to achieve tuneable superconducting qubits.

This thesis is about the growth and placement of dopants in silicon semiconductor devices and specifically acceptor dopants as device dimensions enter the nanoscale. Single-atom donor dopant devices have already been demonstrated in the laboratory. Using density functional theory (DFT) and the aluminium atom we now show how acceptor sites might be fabricated and characterize their electronic behaviour. The thesis opens with a review of the physical basis of statistical doping and the operation of the silicon CMOS transistor which is the most widespread microfabricated device by a wide margin. We show how downscaling requires ever-increasing accuracy in dopant placement and illustrate using some current process techniques. Next, we describe some prototype single-dopant devices and the chapter concludes with a description of a phosphorus nuclear spin qubit and its application. Chapter 2 outlines the theoretical basis of the DFT nanostructure models found in later chapters and chapter 3 presents some elementary calculations intended to validate the local DFT environment. Chapters 4, 5 and 6 are based on published papers produced during this work and listed on page 11. In chapter 4 we introduce patterned atomic layer epitaxy (PALE), an experimental fabrication technique for Si nanostructures. Chapters 5 and 6 describe how PALE could be applied to locate Al dopant atoms in an Si substrate. The final chapter offers some calculations indicating the electronic behaviour of this dopant when embedded in Si nanostructures of various kinds.

There is a proliferation of the number of operational data centres (DCs) across the globe to meet present and future demands for on-demand computational offerings. In spite of the many efforts to improve the utilisation and power efficiency of traditional DCs, results achieved remain sub-optimal. This is primarily because of the rigid utilisation boundaries of traditional server architecture. Disaggregation of server resource components and dynamic orchestration of such resources over a suitable network has been proposed to improve efficiency of next generation composable DCs. This thesis conducts a study on the best setup for such composable DC infrastructure to achieve optimal energy efficiency. First, we formulate a mixed integer linear programming (MILP) model to investigate the optimal scale and scope of resource disaggregation for energy efficient composable DCs. Concurrently, we also investigate the most suitable network for optimal energy efficiency. By placing CPU and memory intensive workloads energy efficiently in different composable DCs, we found that implementing logical disaggregation at rack-scale in composable DCs that adopt all-optical network enables optimal energy efficiency. Physical resource disaggregation of traditional DC servers at rack-scale leads to up to 8% and 20% savings in overall power consumption when CPU intensive and memory intensive workloads are provisioned respectively. We also found that adoption of micro-service architecture in conjunction with the logical disaggregation and rack-scale resource disaggregation can further improve efficiency in composable DCs. A combination of disaggregation and micro-services enabled optimal resources utilisation and energy efficiencies. Thus, relative to the traditional DC up to 23% reduction in the total power consumption is achieved by combining both approaches. Secondly, we describe two variants of a practical and scalable network for composable DC that leverages optical technologies and techniques. Additionally, we formulate a MILP model to evaluate the performance of the novel network in rack-scale composable DCs that implement different forms of disaggregation. The electrical-optical variant of the novel topology achieves similar performance as a reference network while utilising fewer transceivers per compute node. The targeted adoption of optical technologies by both variants of the proposed network achieves greater (4 - 5 times greater) utilisation of available network throughput than the reference network which implemented a generic design. Furthermore, we also formulate a MILP model and develop a comparable heuristic to study the benefits of adopting server disaggregation in the fog computing tier. We evaluate the energy efficient placement of interactive apps in a future fog 6G network in our study. Relative to the present practice of deploying traditional servers in the fog computing layer, adoption of disaggregated servers reduces total fog computing power consumption by up to 18% when a network with low delay penalty is considered. Finally, we recommend that logical disaggregation and rack-scale disaggregation should be implemented in composable DCs that desire energy efficiency. This is because of the advantages and flexibility that both approaches jointly offer as reported in this thesis. We also recommend the targeted use of optical network technologies and techniques. Relative to a general-purposed design, this provides a more efficient approach to mitigate network challenges of composable DCs. Furthermore, these recommendations should be extended to the fog computing tier and edge of future networks to enable greater energy efficiency of the cloud-of-things architecture.

This thesis presents novel methods for AC copper loss calculation and analysis, and investigates the influence of AC copper loss on electromagnetic and thermal performance, with particular emphasis on torque/power-speed characteristics of interior permanent magnet (IPM) machines for low voltage and high-speed operation. The influences of position and size of both solid and hollow rectangular conductors on the AC copper loss and thermal management capability are firstly investigated by the finite element method. It is shown that the AC copper loss accounts for a major part of the total loss in the IPM machine with solid rectangular conductors. There is a tradeoff between high maximum torque capability and low AC copper loss at high speed. Thus, it is essential to consider AC copper loss in the calculation of torque/power-speed characteristics. With the liquid coolant flows directly inside the hollow conductors, the hollow region should be carefully designed since it affects not only the AC copper loss but also the thermal management capability. The hollow shape with the best overall performance can be obtained by considering the variable coolant flow rate. An efficient and accurate hybrid analytical and finite element model has been proposed for calculating the AC copper loss, accounting for the influences of temperature, frequency, current angle, and saturation. Comparing with a pure finite element method, it is much faster to predict the AC copper loss under different frequencies, current levels, and temperatures. By utilizing the proposed method, together with the general lumped thermal parameter model, the torque/power-speed characteristics accounting for AC copper loss can be obtained within a desirable computing time. Furthermore, a novel lumped thermal parameter network is proposed based on heat transfer physics. It eliminates the difference in temperature rise between the distributed loss model and the concentrated loss model. Comparing with the existing models, it utilizes full loss and full resistance and maintains the same parallel flux divide ratio comparing with the distribution loss model. The thermal network element with internal heat generation is relatively isolated from other thermal network elements to prevent over-prediction. It has been shown that the proposed method is relatively more accurate than the existing models for the prediction of hot-spot temperature. All developed analytical and hybrid models, as well as the lumped thermal parameter network, have been validated by finite element analyses.

The overall aim of this PhD project was to contribute to the field of record linkage by proposing innovative solutions that address existing issues among blocking approaches used for record linkage. A thorough explanation of blocking and record linkage is provided so as to better introduce the reader to these concepts. An extensive literature study is also provided to detail many existing blocking approaches, their advantages and disadvantages in comparison to one another, and how existing blocking methodologies could be improved in general. In particular: (1) criticisms of commonly applied blocking evaluation metrics, (2) how to appropriately select a blocking approach for any new (i.e., never before evaluated) unlabelled dataset, (3) the requirement of labelled data or manual fine-tuning of parameters for many existing blocking methods for optimal performance, (4) and how many existing blocking methods may only be applied to structured datasets. In each technical contribution potential solutions to each of these issues are proposed along with an explanation of their benefits and underlying logic. Algorithms and figures are used where possible to more easily convey the key points of each proposed solution. Each proposed solution is empirically evaluated using a large selection of existing blocking approaches and datasets, accompanied by a thorough discussion of results so as to better demonstrate the performance of proposed techniques in comparison to existing approaches. The contributions of this thesis are summarised in the conclusion section, and an outline of how the work of this thesis could be further developed in future work is provided.

The subject of this thesis is the characterisation of a scientific Complementary Metal Oxide Semiconductor (CMOS) image sensor to be used on the JANUS camera on ESA's JUICE mission to Jupiter. The first part of this thesis investigates the initial characteristics of the device to better understand how changes in these characteristics manifest themselves over a range of tests. Initially, following total ionising dose and displacement damage, an increase in the dark current is observed. At temperatures above room temperature, it is theorised that the dark current is proportional to the exponent of the band gap of silicon. Following thermal annealing of these irradiated devices a slight recovery in the average dark current is noticed, which can be credited to the annealing of some radiation induced defects. The second part of this work investigates how image lag manifests in the image sensor, where a transitionary point to high level image lag is observed, referred to as the image lag 'knee-point'. The signal that this knee point occurs is studied with varying total ionising dose and transfer gate voltages, allowing the cause to be hypothesised and an optimum operating condition to be recommended. The image lag is also investigated on a pixel-by-pixel basis, which is a novel approach compared to the typical average level across the whole image sensor. Measurements with devices exposed to total non-ionising doses demonstrate the creation of a population of pixels that exhibit higher levels of image lag than average, an effect that has been attributed to displacement damage in the image sensor.

Remote sensing provides image and LiDAR data that can be useful for a number of tasks such as disaster mapping and surveying. Deep learning (DL) has been shown to provide good results in extracting knowledge from input data sources by the means of learning intermediate representation features. However, popular DL methods require large scaled datasets for training which are costly and time-consuming to obtain. This thesis investigates semantic knowledge extraction from remote sensing data using DL methods in regimes with limited labelled data. Firstly, semantic segmentation methods are compared and analysed on the task of aerial image segmentation. It is shown that pretraining on ImageNet improves the segmentation results despite the domain shift between ImageNet images and aerial images. A framework for mapping road networks in disaster struck areas is proposed. It uses pre and post disaster imagery and labels from OpenStreetMaps (OSM), forgoing the need for costly manually labelled data. Graph-based methods are used to update the pre-existing road maps from OSM. Experiments on a disaster dataset from Palu, Indonesia show the efficacy of the proposed method. A method for semantic feature extraction from aerial imagery is proposed which is shown to work well for multitemporal high resolution image registration. These feature are able to deal with temporal variations caused by seasonal changes. Methods for tree identification in LiDAR data have been proposed to overcome the need for manually labelled data. The first method works on high density point clouds and uses certain LiDAR data attributes for tree identification, achieving almost 90% accuracy. The second uses a voxel based 3D Convolutional Neural Network on low density LiDAR datasets and is able to identify most large trees. The third method is a scaled version of PointNet++ and achieves an F_score of 82.1 on the ISPRS benchmark, comparable to the state of the art methods but with increased efficiency. Finally, saliency methods used for explainability in image analysis are extended to work on 3D point clouds and voxel-based networks to help aid explainability in this area. It is shown that edge and corner features are deemed important by these networks for classification. These features are also demonstrated to be inherently sparse and pruned easily.

At the University of Nottingham, two research groups fabricate atom chips and hexagonal boron-nitride (h-BN) devices. These prototypes require characterisation to provide information about their functionality to the researchers. Electroluminescence and photoluminescence data is required of h-BN devices, atom chips and spintronic devices require measurement of their magnetic field strength. An instrument capable of performing this would be similar in design to a nitrogen-vacancy (NV−) magnetometer. The requirements of this instrument that differ from others are that it should accept larger samples, scan over a larger range, function in ambient conditions and be highly flexible with regards to the types of measurements it can perform. The aim of my studies was to construct such an instrument, capable of imaging the surface of samples whilst measuring reflectance, photoluminescent and electroluminescent emission and measuring the surface topography with an AFM probe, whilst probing the sample with microwaves and a DC magnetic field. The resultant instrument should also be suitable to convert into an NV− magnetometer. This thesis describes the equipment selected and the construction. Functionality is demonstrated by performing tests on samples of nanodiamonds and h-BN devices. The tests provide information about the electroluminescent and photoluminescent properties of these devices which has added to the understanding of how they work. A photoluminescence source was located on one h-BN device, coupled with the knowledge of how it was fabricated, this demonstrates that photoluminescence is also observed in samples of very high purity. Electroluminescence was detected and located on a different device, the instrument confirmed the origin of the emission, in a region of overlap between two thin flakes of h-BN. It also showed that heat generated by the tunnelling current caused deformation of the layers, resulting in a bulge on the surface. The instrument investigated thin flakes of h-BN, to use the information in conjunction with data taken with an imaging ellipsometer. This ellipsometer is unique in its ability to probe samples with 6.5 eV photons, it is used to measure h-BN at and above its band gap. A model is fitted to the data, which gives the refractive indices of h-BN. The results indicate a birefringence of ∆n=2.2 at 6 eV, making h-BN one of the most birefringent materials recorded. To summarise, the instrument has been proven to be functional and flexible. It is suitable to be used as an NV− magnetometer when an appropriate nanodiamond is located and it has provided information on the h-BN devices and aided in the discovery of new information about h-BN as a material.

This thesis is focused on sensorless brushless DC (BLDC) and brushless AC (BLAC) drives, particularly for high-speed application. For BLDC drives, zero-crossing detection (ZCD) of back EMF is the most popular solution for sensorless control. In order to further improve its performance and robustness in the high-speed range, a comprehensive investigation is carried out. Firstly, the oversampling technique is employed to detect the zero-crossing points (ZCPs) of back EMF, which can considerably reduce the sampling delay when the sampling ratio is insufficient. Secondly, two non-ideal factors that will deviate the ZCPs, i.e. asymmetric machine parameters and the resistance tolerance of back EMF measurement circuit, are studied. The corresponding commutation correction algorithms are developed to eliminate the commutation errors caused by these non-ideal factors. Thirdly, the ZCP may be undetectable due to a long freewheeling angle, which is a critical burden of the ZCD based sensorless method operating towards high-speed. Consequently, a PWM pattern resulting in a minimum freewheeling angle is identified and a theoretical technique is developed to predict the maximum torque and speed region for sensorless control. In the high-speed range, the PWM switching ratio is usually insufficient. It is found that conventional commutation patterns, i.e. regular-sampled commutation (RSC) and natural-sampled commutation (NSC), may lead to commutation delay, unstable sensorless control and abundant sideband harmonics. To suppress these adverse influences, a novel commutation pattern, i.e. carrier-synchronized commutation (CSC), is proposed, which can considerably improve the control performance when the switching ratio is insufficient. For high-speed BLAC drives, the main challenge is the design of a decoupling current controller and a rotor position observer. To tackle this challenge, a generic discrete-time BLAC model considering the fractional period sampling delay is developed. Based on this model, a new decoupling current controller and a new rotor position observer are designed directly in discrete-time domain, which can guarantee good control performance in the low sampling ratio condition.

In recent years, a large amount of multi-channel data has been collected due to advances in technology such as with computers and the Internet. However, obtaining and labelling data are still laborious and time-consuming. Yet another issue that adds to the difficulty is finding important channels and features from multi-channel data since having enough channels alone does not guarantee designing efficient algorithms due to scalability problems. In this thesis, a generative model and hierarchical learning models are introduced to deal with the aforementioned issues. First, the learning process of Variational Autoencoders is analysed. Taking into account the role of the mean and the standard deviation, which are used in the reparameterization trick, we propose a new generative model. The proposed model is modified from the original Autoencoder architecture which is used for dimensionality reduction. The model preserves the architecture of the Autoencoder by removing the reparameterization trick and becomes a generative model by extension of the mapping of the decoder from a discrete latent space to a continuous latent space. The model is compared with VAE and MMD on three benchmark datasets: MNIST, Fashion-MNIST and SVHN datasets. The experimental results show that the difference of the accuracy of the test set when training ANNs using synthetic data generated by the proposed model is less than 10% when training it using the original training set in MNIST and Fashion-MINST datasets. In addition, further experiments are carried out to investigate the impact of the number of the training set when training generative models. The results show that the accuracy of the test set decreases less than 10% when the number of the training set decreases in the NNIST and the Fashion-MNIST dataset. Second, two types of hierarchical learning models are proposed. Designing these models began with the idea of utilizing an innate hierarchy of targets. The first type of model, HAL, is proposed when targets are discrete. This model involves inserting the auxiliary block to output the auxiliary scores from the coarse classes. These scores are distributed based on the corresponding coarse classes. Although the model improves the accuracy of a test set, it has the disadvantage of requiring the coarse classes at the test phase. The second type of models are proposed when targets are continuous. C-FNNs and HADNNs are proposed to perform the regression task by utilizing the coarse classes. C-FNNs and HADNNs are evaluated on three benchmark indoor localization datasets, examples of multi-channel data. Results show that C-FNNs increase the floor accuracy by 30% at least and 60% at most in the three datasets. However, C-FNNs require more than three times the parameters than the baseline. HADNNs achieve better accuracy than C-FNNs and require 1.2 times the parameters than the baseline at most. Third, human motion data is analysed in order to show the importance of the relationship between sensor locations and motion types when identifying motion types. The data were gathered from patients and students in Inha University Hospital, Korea. Twenty-three subjects participated in the experiment and all had to perform nine motion types. Forty-eight total measurements were obtained from eight different body parts. The motion type detection algorithm is divided into five steps and is evaluated based on four metrics: recall, precision, accuracy and F-measure. The proposed detection algorithm has $0.8986$ average recall, 0.9071 average precision, 0.9739 average accuracy and 0.8977 average F-measure. The detection algorithm outperforms PCA, which is a popular method in feature extraction. This shows the importance of feature extraction based on the relationship between channels and targets in multi-channel data. Finally, the motion type detection process is proposed by integrating the proposed models. The process is divided into three: generation, labelling and classification. In generation, the proposed generative model is used to generate synthetic data. In labelling, SVM and PCA are used to label synthetic data. In classification, ResNet with C-FNNs and with HADNNs for a classification task are trained using the combination of the labelled synthetic data and the original training set, and the neural networks are used to detect motion types. The process is evaluated using InhaMotion and nine open source human motion datasets. The results show that training ANNs with synthetic data prevents overfitting, and the proposed generative model outperforms VAE, beta-VAE and MMD. In addition, the combination of ResNet and C-FNNs increase the accuracies of the test sets when coarse classes are available during the training phase. Since C-FNNs do not require coarse classes at the test phase, it is practical to use in daily life problems where hierarchy of targets should be considered.

Parity-time (PT) symmetry is a unique space-time reflection symmetry that describes the system invariance to the combined parity (P) and time-reversal (T) operators. The parity operator corresponds to the interchange or inversion of spatial coordinates and the time-reversal operator inverts the time. PT-symmetry in electronics and electromagnetic systems is achieved by combining loss and gain in balanced proportions where loss is a conventional positive resistor and gain corresponds to a negative resistor. This judicious combination of loss and gain proportions has led to intriguing system behavior at exceptional points resulting in exotic wave manipulation effects such as non-linear propagation, negative refraction, sub-wavelength focusing, unidirectional cloaking and invisible sensors. Although such devices have been proposed in theory and proof of concept has been presented at low frequencies, practical implementation at microwave frequency ranges requires precise control and stability of loss-gain components with reduced noise figure. For instance, realisation of a negative resistor at microwave frequencies through active devices, such as bipolar junction transistors, operational amplifiers and tunnel diodes, is not only complex but also prone to instability and high noise figure levels which can further restrict the functional design of PT-symmetric devices. In addition, studies have shown that noise can break PT-symmetry by disturbing the loss and gain balance resulting in performance degradation of such devices. In this thesis, non-invasive design of PT-symmetric circuits to manipulate the reflection and transmission behaviour at microwave frequency ranges has been shown by incorporating an appropriate inductive or capacitive impedance while sustaining PT-symmetry. The studies begin with the design and investigation of ideal PT-symmetric circuits, where the loss, gain and reactive elements are connected in either series or shunt configurations through a transmission line. The scattering parameters, exceptional points and eigenvalues for the aforementioned PT-symmetric circuit configurations have been shown. The research studies show that the PT-symmetric circuits in series or shunt configurations can behave as either, a cloaking structure or as a switching device, based on the choice of electrical line length. Studies in subsequent sections present the noise characterisation in terms of noise figure by incorporating realistic loss and gain circuit elements with equivalent thermal noise sources at the exceptional points of the PT-symmetric systems. It has been shown that noise in a two-port PT-symmetric systems distorts the location of its exceptional points, resulting in system’s performance degradation in terms of increased reflections and the input and output port and reduced transmission towards port-2. In particular to cloaking applications, it has been shown that increased noise figure levels effect the performance of a PT-symmetric cloak by increasing its radar cross-section. The simulations have estimated and compared the performance of the noisy and ideal PT-symmetric cloak at microwave frequency range. Finally, practical implementation and realization of gain element in PT-symmetric systems is proposed with simulated and measured results.

Ultrasound imaging is a low cost and non-invasive technique, with many biomedical and industrial applications. In many applications, operator dependency can significantly influence the quality of the acquired information. Transcranial ultrasound is one such application, and this Thesis will investigate both transducer design and image processing techniques inspired by the desire to improve the ability of an ultrasonic system to detect and size anomalies in the blood flow. Aperiodic sparse 2D ultrasonic array configurations, including random array, log spiral array, and sunflower spiral array, have been considered for their potential as a conformable transducer able to image within a focal range of 30-80 mm, at an operating frequency of 2 MHz. Optimisation of the imaging performance of potential array patterns has been undertaken based on their simulated far field directivity functions. Subsequently, two log spiral array patterns have been selected: one is the overall optimal design; the other is a compromise design to accommodate in-house manufacturing limitations. Both conventional 1-3 (C13) piezocomposite and piezoceramic fibre Composite Element Composite Array Transducer (CECAT) structures have been fabricated and characterised. The CECAT device provides a conformable piezocomposite material and demonstrated reduced mechanical cross-talk between neighbouring array elements and improved the operational bandwidth, while the mechanically stiff C13 devices perform better in terms of sensitivity. Moreover, the C13 device incorporating the overall optimal array pattern performs best in terms of the image background noise level, while for transducers based on the compromise design, the CECAT device offers better axial resolution when imaging multiple reflectors. Image processing algorithms, such as Hough transform and Morphological Opening, have been implemented to automatically detect and dimension particles located within a fluid-filled tube structure, in a variety of experimental scenarios. This includes bespoke phantoms using tissue mimicking material to simulate a basic transcranial ultrasound arrangement. The image processing algorithms were initially developed using data collected from a commercial 1D linear array transducer. Subsequent experiments using the fabricated CECAT log spiral 2D array transducer demonstrated that this algorithmic approach was able to detect the walls of the tube structure and stationary anomalies within the tube with a precision of ~0.1 mm.

Neuromorphic vision sensing (NVS) devices represent visual information as sequences of asynchronous discrete events (a.k.a., ’spikes’) in response to changes in scene reflectance. Unlike conventional active pixel sensing (APS), NVS allows for significantly higher event sampling rates at substantially increased energy efficiency and robustness to illumination changes. However, neuromorphic vision sensing comes with two key challenges: (i) the lack of large-scale annotated datasets to train advanced machine learning frameworks with; (ii) feature representation for NVS is far behind that of APS-based counterparts, resulting in lower accuracy for high-level computer vision tasks. In this thesis, we attempt to bridge these gaps by firstly proposing an NVS emulation framework, termed as PIX2NVS, that converts frames from APS videos to emulated neuromorphic spike events so that we can generate large annotated NVS data from existing video frame collections (e.g., UCF101, YouTube-8M, YFCC 100m, etc.) used in machine learning research. We evaluate PIX2NVS with three proposed distance metrics and test the emulated data on two recognition applications. Furthermore, given the sparse and asynchronous nature of NVS, we propose a compact graph representation for NVS, which allows for end-to-end learning with graph convolutional neural networks. We couple this with a novel end-to-end feature learning framework that accommodates both appearance-based and motionbased tasks. The core of our framework comprises a spatial feature learning module, which utilizes our proposed residual-graph CNN (RG-CNN), for end-to-end learning of appearance-based features directly from graphs. We extend this with our proposed Graph2Grid block and temporal feature learning module in order to efficiently model temporal dependencies over multiple graphs and allow for long temporal extent. We show that performance of this framework generalizes to object classification, action recognition, action similarity labeling and scene recognition, with state-of-the-art results. Importantly, our framework preserves the spatial and temporal coherence of spike events, while requiring less computation and memory. Finally, to address the absence of large real-world NVS datasets for complex recognition tasks, we introduce, evaluate and make available a 100k dataset of NVS recordings of the American Sign Language letters (ASL-DVS) acquired with an iniLabs DAVIS240c device under real-world conditions, as well as three neuromorphic human action dataset (UCF101-DVS, HMDB51-DVS and ASLAN-DVS) and one scene recognition dataset (YUPENN-DVS) recorded by the DAVIS240c capturing their screen playback reflectance.

The vibration power flow analysis (PFA) has been widely accepted to investigate the dynamic performance in linear dynamical systems; however, the vibration transmission behaviour and power flow characteristics of many nonlinear dynamical systems are still unclear. This study aims to investigate the vibrational energy and power transmission in coupled systems with different types of local nonlinearities. Different approaches including analytical, semi-analytical, and numerical methods are used to gain physical insights into the power flow mechanisms of nonlinear systems. The findings are expected to provide guidelines for exploiting nonlinear elements in mitigation of power transmission and suppression of vibration to achieve enhanced designs. The effects of different nonlinearities on dynamic response, kinetic energy, and power transmission in coupled nonlinear/linear oscillators through nonlinear/linear interface are investigated. It is shown that the hardening, softening, and double-well stiffness nonlinearities and the cubic damping nonlinearity mainly affect the power flow curves locally in the vicinity of the resonant frequencies. It is found that the coupled systems with double-well potential stiffness may exhibit chaotic and super-/sub-harmonic response. Multiple solutions and jump phenomenon of vibration power transmission are also observed. The dynamic characteristics and vibrational power transmission behaviour of coupled systems incorporating bilinear stiffness and bilinear damping elements are investigated. It is observed that the bilinear spring element may cause large super-/sub-harmonic components in the response, resulting in higher level of vibration transmission through the interface. It is shown that a combination of small bilinear stiffness ratio and a large bilinear damping ratio may be employed to provide good overall suppression of vibration transmission through the nonlinear interface. Study of tuned inerter damper coupled to a linear and nonlinear primary oscillator is presented. The optimal stiffness and damping ratios to achieve equal resonant peaks of displacement and kinetic energy of the primary oscillator are obtained. It is shown that an increase in the inertance of the absorber substantially reduces the response peaks of the primary system. Nonlinearly coupled oscillators with dual-force excitations with different fundamental frequencies are studied. For the cubic stiffness interface, the stiffness nonlinearity mainly affects the secondary resonant peaks. For the bilinear stiffness interface, a larger bilinear frequency ratio shifts the secondary resonances and critical frequency of the equilibrium point of power transmission to higher frequencies. These results and findings provide a deep understanding of power generation, transmission, and dissipation mechanisms in coupled systems with local nonlinearities. This study also presents some guidelines for the designs of nonlinear systems achieving better dynamic performance.

Silicon and Silicon Carbide-based power module are common in power electronic systems used in a wide range of applications, including renewable energy, industrial drives and transportation. Reliability of power electronics converters is very important in many applications. It is well known that reliability and ultimately the lifetime of power modules is affected by the running temperature during power cycles. Although accurate thermal models of power electronics assemblies are widely available, based e.g. on computational fluid dynamics (CFD) solvers, their computational complexity hinders the application in real-time temperature monitoring applications. In the thesis, geometry-based numerical thermal models and compact thermal models will be developed to address the fast thermal simulation in the electronic design process and real-time temperature monitoring, respectively. Accurate geometry-based mathematical models for dynamic thermal analyses can be established with the help of finite difference methods (FDM). However, the computational complexity result from the fine mesh and large dimension of ordinary differential equations (ODE) system matrix makes a drawback on the analysis in parametric studies. In this thesis, a novel multi-parameter order reduction technique is proposed, which can significantly improve the simulation efficiency without having a significant impact on the prediction accuracy. Based on the block Arnoldi method, this method is illustrated by referring to the multi-chip power module connected with air-force cooling system including plate-fin heatsink. In real-time temperature monitoring, more compact tools might be preferable, especially if operating and boundary conditions such as losses and cooling are now known accurately, as it’s often the case in practical applications. Compared with geometry-based model which is more suitable in the design of power modules, lumped parameter thermal compact model is simpler and can be applied in real-time temperature prediction during the power cycles of power modules. This thesis proposes a reduced order state space observer to minimize the error caused by air temperature and air flow rate. Additionally, a novel feedback mechanism for disturbance estimation is introduced to compensate the effect result from the error of input power loss, air flow and changes of other nonlinearities.

Novel scanning thermal microscopy probes have been presented, along with the design choices and fabrication challenges associated with realising them. The probe’s performance was characterised as a resistance thermometer (passive mode) and as a self-heated thermal conductance measurement device (active mode). Frequency domain measurements demonstrated that improved performance in both operating regimes was the result of changes to the cantilever which made measurements less sensitive to the thermal properties of the cantilever. The commercial KNT Scanning Thermal Microscope (SThM) probe has become ubiquitous in scanning thermal microscopy thanks to its commercial availability, high spatial resolution and relatively high reproducibility. However, no published studies have been performed with the aim of optimising this probe for a specific type of thermal metrology. The commercial probes are fabricated in the same cleanroom which is used for research fabrication, providing a unique opportunity to fabricate and test novel, optimised SThM probes based on the same technology. Improvement of the existing probes requires a proper understanding of the complex thermal network which governs their operation. The typical lumped model based upon the thermal-electrical analogy is useful, but contains no information on the temperature distribution within the device itself, data which is crucial for informing the design of new probes. This thesis presents a lightweight distributed model which is capable of computing, and comparing the temperature distributions of probes with arbitrary layout. The performance of novel probe designs may be assessed by simulating their response to contacting upon materials with various thermal conductivities. The model is tightly integrated with the design process to inform probe manufacture. Tests undertaken with this model indicated that the placement and layout of the sensor should be optimised, and that the sensor should be thermally isolated from the body of the cantilever. Realising such changes required various improvements to the electron beam lithography process used to pattern the sensors. In particular, an increase in the positional accuracy of feature placement between writing layers and an optimisation of the focus of the beam were required for proper lift-off of narrow, sub-micron features. A matrix of probe designs were fabricated and tested to investigate which designs could be realised with high yield, and of those, which would have the best performance for each application. Probes with material removed from their apices were found to give a signal which was less dependent on thermal loading through the air. All probe types were demonstrated to have greater sensitivity than the commercial probe to materials of varying thermal conductivity when used in the active mode. The source of the improvements was experimentally confirmed using a two-pole frequency response model, where it was demonstrated that their output was substantially insensitive to the influence of the temperature of the cantilever body.

Hairpin windings are gaining increasing popularity in recent years due to their ad-vantages in improving electrical machine performance while reducing manufacturing time and costs. Machines adopting hairpin windings can achieve higher torque density and power density while enabling them to be manufactured automatically on a large scale to meet the increasing demand from transport electrification. Their geometrical features introduce new challenges and opportunities for thermal management. In partic-ular, spray cooling is increasingly being used, since hairpin windings open up regular and accurately defined gaps in the end-windings compared to the traditional random wind-ings. This research proposes a reduced dimensionless correlation based on previous empirical models. A simple experimental setup is designed and manufactured to determine the pa-rameters in the correlation using two off-the-shelf hydraulic nozzles. The established correlations are then used to predict the nozzle’s cooling performance, and the results are validated experimentally using an existing stator with hairpin windings. Lumped parameter thermal network modelling is used to evaluate the spray cooling de-sign’s performance based on an existing machine, and the results are compared to those of a conventional water jacket cooling design, demonstrating spray cooling’s superior cooling ability when combined with hairpin windings. Additional experimental results of other types of nozzles and oil-jet cooling are also re-ported to provide design guidelines to machine designers who required to implement such cooling setups in their design.

In recent years, events of intermittent low- and high-drag have been observed in turbulent channel flows near transition. During the low-drag events, the wall shear stress, on average, temporarily reduces to about 40% below its time-averaged value and the velocity profile approaches Virk's maximum drag reduction (MDR) asymptote. There are still open questions regarding this phenomenon, for example the characteristics of Reynolds shear stress (RSS) during the events and whether they continue to exist at higher Reynolds numbers (i.e. in the so-called ``fully-turbulent" flow regime). Investigating intermittencies in turbulent flow close to transition requires knowledge of intermittencies during the laminar-turbulent transition process. However, there is currently no time-resolved experimental wall shear stress data available in the transitional flow regime for channel flow. To study the laminar-turbulent intermittency, instantaneous wall shear stress (using hot-film anemometry, HFA) is probed at the transitional Reynolds numbers. Higher order statistics show that with increasing Reynolds numbers (from the laminar flow regime), the laminar-turbulent intermittency firstly grows, then diminishes, and eventually disappears by Reτ ≈ 72, where Reτ =uτ h/ν and uτ, h and ν indicate the friction velocity, channel half-height and kinematic viscosity, respectively. Using multiple hot-film probes, information about the large-scale turbulent structures during transition is inferred. Additionally, a flow visualization has been conducted to observe the large-scale structures, which qualitatively supports the wall shear stress results. Beyond transition, simultaneous measurements of wall shear stress and velocity (using laser Doppler velocimetry, LDV) have been conducted to detect and characterize the low- and high-drag intermittencies for Reτ = 70 - 250. The fraction of time spent in these intermittent events is observed to be independent of Reynolds number when the criteria for minimum time duration is kept constant in ``inner" units. The previously observed spike in the ensemble-averaged wall shear stress before and after the low-drag events is found to be an artefact of the conditional sampling and ensemble-averaging process and is not a physical phenomenon. Conditionally-averaged streamwise velocity profiles get closer to Virk's MDR asymptote, near the wall, for all the Reynolds numbers studied. Simultaneous wall shear stress and RSS measurements are carried out for Reτ = 70 and 85. An increase in the conditionally-averaged RSS is observed during the low-drag events for all the wall-normal locations measured, but is particularly apparent for y+ ≈ 20 - 40, where y+ = y uτ/ ν and y is the wall-normal distance. When using HFA for long-time wall shear stress measurements, minimizing the non-thermal calibration drift is a significant challenge. A new method to minimize recalibration in thermal anemometry using a non-linear regression technique is proposed and investigated. This new method was utilized in the current work and finds potential applications in cases of correcting for non-thermal calibration drifts in long time measurements and also in scenarios where the direct calibration of hot-films is not possible or suffers significant uncertainties.

In this thesis, the use of a rectangular dielectric-lined waveguide for energy modulation of electron bunches is presented. The choice of waveguide allows for guided THz pulse propagation with phase velocity matched to the electron velocity. The effect of dispersion, in particular group velocity slippage, has been explored and the choice of a narrow bandwidth THz pulse discussed with regards to the increase in interaction length and minimised group velocity dispersion. A coupler was designed for maximising transmission into the accelerating mode of the waveguide. A non-conventional THz source design was required to generate the correct mode. Modelling of the interaction was performed with different methods and tools so as to investigate the required accuracy of simulations. The use of the Time-Domain (TD) and Particle-in-Cell (PIC) solver in CST Microwave Studio (CST-MWS) was compared with purpose-built simulations in Mathematica. It was established that for narrowband THz pulses the interaction as a function of time delay between THz and the bunch is well approximated by a sinusoidal energy modulation. PIC simulations were used to verify the THz bandwidth and centre frequency for which this approximation was valid. A full structure was designed, manufactured and analysed. THz time domain spectroscopy allowed for measurement of the dispersion relation to compare to the model. Dimensional analysis gave the dimensions of the apertures of the structure. The dimensional analysis showed that, due to a manufacturing error, the waveguide dimensions were larger than designed. Experimental work performed using the CLARA beam at Daresbury Laboratory demonstrated energy modulation of a long, chirped electron bunch. This has potential for use as a bunch diagnostic to assess the time-dependent properties. The THz source was of limited energy, showing that only small laser power is required for such a scheme. An energy spread increase of approximately 8 keV was verified, but full bunch acceleration was not observed.

High Temperature Superconductor (HTS) magnets are enabling components for many valuable applications including medical imaging and fundamental physics research. One major inconvenience of using superconducting components is the need to cool them to a very low temperature, and once at this low temperature, to power them. This requires the use of cryogenic systems and power supplies, which are often larger and more complex than the superconducting components. The aim of the work in this thesis is to develop better and more flexible power supplies for superconducting magnets, known as flux pumps. These devices power superconducting magnets wirelessly, isolating the power supply from the magnet both thermally and mechanically. This reduces the power requirement for keeping the magnet cold, cutting both power consumption and the size and weight of the cryogenic system. This thesis focusses specifically on developing improved HTS flux pumps of the transformer-rectifier type and seeks to demonstrate their feasibility and advantage over conventional technology. First, new simulation models are presented which help to understand transformer-rectifier flux pumps and how to optimise them. Based on analysis of these models, superconducting switches are the most important components of these devices. The subsequent section documents the development of an improved superconducting power switch, which shows a tenfold increase in performance compared to switches of this type previously reported on. These switches were used to build a transformer-rectifier flux pump which provides 100 mV to a load magnet, the highest output voltage for an HTS flux pump to date. Based on work from previous chapters, a design methodology for transformer-rectifier flux pumps is presented, giving examples of future designs and their trade-offs. Finally, the current state of flux pump technology is discussed, concluding that that flux pumps are a compelling technology and that future work should focus on demonstrating flux pumps in applications.

Optimising the material properties of CoFeB/MgO/CoFeB based magnetic tunnel junctions (MTJs) is a key step for enabling the further development of data storage and processing technologies based on spin electronics (spintronics). A promising area of current research is the use of thin films with antiferromagnetic (AF) ordering, which has led to a focus on AF materials for spintronic memories. The first order AF to ferromagnetic (FM) metamagnetic phase transition found in FeRh at technologically useful temperatures offers additional degrees of freedom for developing AF spintronics. The work presented in this thesis explores the characterisation and optimisation of thin layers for spintronic and AF spintronic memories. An in-depth study of the sputter deposition conditions required to fabricate MTJs with uniform layers and pinhole free MgO tunnel barriers was undertaken. Kelvin Probe Force Microscopy was used to determine the electrical properties of the barrier, demonstrating that a low sputtering power produced smooth barriers without pinholes, critical for fabricating reliable MTJs. The sputtering parameters necessary to fabricate Ta/CoFeB/MgO/CoFeB/Ta/Pt MTJ films with perpendicular magnetic anisotropy (PMA) were optimised through a study of annealing conditions on their structural and magnetic properties. Annealing films to 340C enhanced the PMA, which was attributed to the intermixing of Ta and CoFeB at the bottom layer Ta/CoFeB interface, whereas annealing to 360C was shown to degrade magnetic properties, attributed to the diffusion of oxygen from the MgO barrier. To electrically characterise films, a new time-saving fabrication process utilising PMMA resist was developed for point contact current-in-plane-tunnelling measurements. Initial results demonstrated the successful fabrication of point contacts onto the film surface with the required spatial resolution of 1.5 +/- 0.05 um, without causing structural damage. The nucleation of the AF to FM phase transition in FeRh thin films grown onto MgO(001) was investigated using polarised neutron reflectivity. The FM phase was found to initiate at the MgO/FeRh interface in the form of a 5 nm strained FeRh layer. The strained layer was observed to be ferromagnetically ordered at room temperature. These results demonstrated the impact of topography and strain on the magnetic properties of FeRh as thicknesses are reduced to the order of the exchange length, highlighting the challenges associated with material optimisation for AF spintronic memories.

Electrical impedance tomography (EIT) is an imaging technique for mapping the internal conductivity distribution of an object by taking voltage measurements from electrodes attached to the surface of the object, while an electrical current is injected to these boundary electrodes. EIT has been researched in many different application areas in the world as a simpler, cheaper and safer alternative to many other tomography techniques, while provider very useful and often unique functional information. The main aim of this PhD study is to extend the use of EIT by further improvements of our understanding of the EIT data and image analysis and its challenges. In each case the challenges are highlighted and some solutions are proposed. EIT has great applications in area of industrial processes. We highlight a challenges associated with the EIT to simultaneously reconstruct the permittivity and conductivity, in particular, when there is a low contrast in permittivity values of samples but in high contrast with background. A good case study to highlight this challenge is a water dominate oil and gas flow with important application in process industry. The thesis is proposing a dual-modality EIT with transmission mode travel time ultrasound tomography for such an application. Another potential area with the EIT is the use of EIT for artificial skin for robotics application. This is done using a soft material such as fabric and the skin can be developed by EIT with sensing the change in conductivity while pressure applied to the skin. We have identified two main issues with the application, one the need to extend the functionality of the skin to be dynamical. This will enable the EIT based skin to work as an interface allowing social interaction with the robot. Second is a well established issue in medical EIT, which also exists in robotics EIT and that is the movement of electrodes which corrupted the EIT image. We have developed a spatially correlated total variation imaging algorithm so that the robot skin using EIT could work as dynamical imaging sensor allowing for interactive skin. The interaction of the EIT based skin through pressure sensing can be done like a movie rather than individual images, which resembles the human skin interaction. The movement of electrodes and electromechnaicl interpretation of pressure via EIT image are both very difficult problems to model and interpreter. For these issue we implemented a convolutional neural network deep learning algorithm. The imaging results shows very good performance of both spatially correlated TV algorithm together with the deep learning approach. The working flow of this dissertation can be explained as the following sections. Firstly, the basic background of EIT, its applications and mathematical theories including the forward problem, inverse problem have been reviewed. Secondly, a complex impedance image reconstruction is developed. The complex EIT which is determining conductivity and permittivity distribution at the same time using the real and imaginary part of the voltage measurements are presented. A complexvalued forward model, Jacobian matrix, inverse solution and related simulation studies are developed as well, the results indicated there are still challenging in reconstructing both parameters simultaneously. And then, a novel EIT combined with ultrasound transmission mode tomography (UTT) dual-modality for threephase material image is developed. Identification of three phase oil/gas/water in water dominated situation should be possible via complex EIT, but practicality this is challenging. Therefore, the EIT/UTT dual modality imaging can be deployed for such application, where EIT is used to identify non-conductive phase which either oil or gas phase and hence UTT is used to identify air phase. Both simulation and experimental studies are implemented and a image fusion method is proposed for producing three-phase images. Finally, a conductive fabric based EIT dynamical sensing system integrated with deep learning for improving image quality is proposed, a movie like denoised experimental results are presented using a spatiotemporal total variation algorithm and a convolutional neural network training. The deep learning method helps overcoming the imaging artefact's due to electrode movement which is a main issue in fabric based EIT.

Bulk superconductors, in their capacity as trapped field magnets, offer a practical means of generating high magnetic fields in small volumes. This is desirable for applications in which portability is of primary concern. In particular, superconducting materials are seen as enablers leading towards light-weight, high power density electric motors to be used in future hybrid-electric passenger aircraft. One of the issues that needs to be addressed before this can become a reality, however, is the instability of trapped magnetic field in these materials, when exposed to external time-varying magnetic fields. In this work a comprehensive study of the effect of AC magnetic fields on the trapped magnetic field in bulk superconductors is presented. Two distinct geometries are studied; the crossed-field and the parallel configuration, in which the AC magnetic field is applied perpendicular or parallel to the direction of trapped magnetic field, respectively. An analytical empirical model is derived, with which the decay of trapped magnetic field in the crossed-field configuration can be predicted accurately, provided the value of the critical current density in the material is known. The model is found to be in excellent agreement with the observed experimental data, as well as with finite-element numerical simulations. In the parallel configuration the time dependence of trapped magnetic field is studied as a function of the AC magnetic field amplitude, its frequency and the operating temperature of the superconductor. Subsequently, the data are compared with their equivalent in the crossed-field configuration. It is found that, while the crossed-field configuration leads to a greater rate of decay of trapped field, in both configurations reducing the operating temperature proves an effective mitigation measure against it. Lastly, the limits of the well established Bean critical state model are studied within the scope of the Campbell penetration depth of magnetic field, which is, itself, a direct consequence of the reversible and elastic movement of flux vortices within the pinning potential. I derive a convenient way of measuring the Campbell penetration depth using a pick-up method, and present measurements of its value in a bulk superconductor at different applied magnetic fields.

The ever-increasing development of wearable sensors and self-powered microelectronic devices has become possible by the advent of new types of piezoelectric micropower generators. One important material that has enabled the possibility to seamlessly integrate such microgenerators into many emerging applications such as, smart textiles and fibre-reinforced polymer (FRP) composites, is Poly(vinylidene fluoride) (PVDF). A series of novel and efficient Piezo-Polymer based Energy Harvester (PPEH) concepts were first developed in this project [2]. For this aim, the textile braiding technology was employed as a new method for large-scale production of a number of piezoelectric PVDF braid microgenerators. The initial concept of microgenerators consists of a 0.4 mm solid core polyurethane (PUR)-enamelled copper wire (the inner electrode), which was over braided with twenty-four PVDF multifilament yarns (as the intermediate piezoelectric layer), and the whole structure was over braided again with sixteen strands of 0.1 mm PUR-enamelled copper wire (the outer electrode). The smart braid was then incorporated into a glass FRP composite panel as an embedded sensor/micropower harvester. The experimental results of a series of tensile tests, dynamic modal analysis, and three point-bend (3PB) cyclic loading tests showed that the tensile strength and strain-to-failure properties of the host composite were respectively improved by about seventy-four and sixty-two percent; the mechanical damping efficiency of the panel was also increased by about 125% via utilising a passive shunt damping circuit which simultaneously extracted and wasted the piezo generated power as heat over a 100 kW resistor. Furthermore, 3PB cyclic loading tests results (with an excitation amplitude of 1 mm between 1 Hz to 10 Hz) showed that the average power density of as-manufactured (unoptimized) smart composite prototypes is around 2.2 mW.cm−3. Further, in this work, two poling approaches were successfully implemented for improving the piezoelectric properties of as-received PVDF yarns. Verification on the degree of crystallinity and the β-phase content within the material was made by Differential Scanning Calorimetry (DSC) and Fourier Transform Infrared (FTIR) spectroscopy techniques. The results showed that an in-house made radial poling apparatus was capable of improving the crystallinity level (%) of PVDF yarns by as much as nearly twenty-seven percent. Yet, the characterisation results of the second poling approach – the axial poling – where a high electric field (with an applied potential of ≈55 kV) was induced along the axial direction of the yarns, also showed that the overall melting temperature of the samples was decreased by about 3.5°C, indicating that a permanent piezoelectric effect has been induced/achieved by this method. Finally, strong positive correlations were found between the experimental results obtained from the tensile test procedures, modal analysis, three-point bending cyclic loading test, and the poling experiments and their corresponding finite element (FE) model counterparts simulated in Abaqus®/CAE 2017, Creo Parametric 3.0, COMSOL Multiphysics 5.2a software. These promising results were in addition to the successful development of a full-scale simulation of the braiding process which was performed using the Ls-Dyna® FE-code. This research project has successfully demonstrated the feasibility to design, manufacture, and characterise a unique type of “all-fibrous” piezoelectric polymer microgenerator that, for the first time, has proved to be functional and chemically sustainable in the harsh resin epoxy-involved environment at various stages of composite manufacturing process (during vacuum infusion and curing process). This is while no insulating layer/shield between the microgenerator and the resin/reinforcement components has been used. This work therefore offers a promising method for industrial-scale productions of flexible piezoelectric sensors (for Structural Health Monitoring (SHM) applications), and micropower energy harvesters (for the development of future sustainable energy harvesting technologies) in applications such as structural power composites, multifunctional composites, and wearable electronic devices from low-frequency, large-amplitude human body motions.

In this thesis, two kinds of novel overgrowth approaches to achieving high-quality non-polar III-nitrides have been presented, novel multiple-colour structures and non-polar InGaN/GaN LEDs based on the patterned templates have been obtained. Detailed investigation of structure and optical performance have been performed for further validation of our high quality and multiple-function patterned templates.

Electrical Submersible Pump (ESP) is one of the most efficient and reliable devices to lift fluids to the surface. It is commonly used in the oil and gas industry, where it provides a low-cost solution for high volumes of lifting and the flexibility to cover a range of sizes, output flow capacities, production profiles and various well conditions. The ESP is operated in a very challenging environment and it is exposed to many factors that could lead to its downtime. One of the critical elements for the ESP failure is the mechanical seal. While a significant amount of research has been dedicated to the improvement of the seal design to enhance its performance and reduce the risk of failure, its failure remains among the major contributors towards the ESP failure, with significant consequences in terms of loss of production (downtime) and equipment replacement costs. The aim of the research presented in this thesis is to enhance the condition monitoring of the ESP to allow a more accurate detection of early conditions that could lead to the mechanical seal failure, such as excessive vibrations caused by a misalignment between the motor and the pump shafts. The proposed monitoring method is based on the Motor Current Signature Analysis (MCSA), i.e. the analysis of the stator current in the induction motor connected to the pump. It is known that vibrations produce characteristic features in the current signal, appearing as sidebands in its frequency spectrum; however, those sidebands are difficult to detect and measure because of their small amplitudes and the typical presence of large spectral leakage in the measured current spectra. The main objective of this research is to develop a novel signal processing method to compensate for the leakage error that affects the MCSA, in order to obtain more accurate measurements of the current features of interest to detect the shaft misalignment. An analytical model has been formulated to relate the shaft misalignment to vibrations, and the vibrations to specific sidebands in the motor current spectrum. The model has then been used to run numerical simulations, in order to validate the proposed method in a wide range of conditions, and with different motor power ratings. Finally, a small-scale experimental test rig has been designed to validate the proposed method with motor current measurements from a real motor. The obtained results confirm that the proposed method allows a significant decrease in the spectral leakage affecting sideband measurements in MCSA, with a consequent significant improvement in the estimation of the amplitudes of those sidebands. The method is therefore promising for the detection of vibrations that could lead to the mechanical seal failure in ESP, although further investigations on high-power motors are required in the future for a full validation.

Advancements in the field of scanning electron microscopy have been one of the major nano technology enablers. A scanning electron microscope (SEM) generates a magnified image of the sample by bombarding it with an electron beam and detecting the electrons that scatter off the surface along with the electrons that are generated in the sample. In conventional SEMs, the generated or secondary electrons are detected by the famous Everhart Thornley detector via positively biased input-grid. However, in doing so, it loses energy and angular information of the electrons. This information is crucial to interpret the SEM image of the sample under study. What is needed is a small and compact detector that can detect electrons and furnish energy information inside an SEM chamber. The detector designed to achieve these aims is able to detect low energy electrons at the same time able to take the geometrical constraints of the SEM into account. This study presents a design and implementation of a novel secondary electron detector based on the Bessel Box (BB) energy analyser for SEM Simulations were carried out for the design in SIMION 8.1 ray tracing software. An energy resolution of 0.4% has been numerically calculated and experimentally tested in an ultra-high-vacuum chamber. This was followed by the integration of the BB detector to a conventional scanning electron microscope. The device described would be appealing to the electron microscopy and spectroscopy at large. The detector has been successfully demonstrated for electron spectroscopy applications: Auger and secondary electron. It has also been demonstrated for secondary electron microscopy, all obtained by as-designed BB detector.

This thesis reports an investigation of the design, development, implementation and use of in-situ thermal sensing systems utilising Fibre Bragg Grating Sensing Technology (FBGST) for low voltage electric machine (LVEM) thermal condition monitoring applications. The thesis first investigated the key design and operational features of the in-situ FBG temperature sensor for thermal hot spot monitoring in stand-alone prototype random wound components. Vital sensing aspects such as the sensor characterisation, packaging material choice, in-situ calibration requirements, use of multiplexing for distributed sensing, the installation procedure and the thermal measurement sensitivity to machine vibration were investigated. The reported findings enable a much improved understanding of the performance implications of embedded FBG sensor design features and the attainable in-situ hot spot thermal monitoring performance in random wound coils. It is shown that reliable, improved fidelity information on the coil's thermal status can be obtained from application of wound coil embedded FBG thermal sensing systems. The thesis then reported the use and performance evaluation of the devised in-situ FBG temperature sensor in operational LVEMs. Different in-situ FBG thermal sensing configurations were designed and embedded into two standard LVEM topologies: an induction machine and a permanent-magnet synchronous machine. The in-situ system's on-line thermal monitoring performance was experimentally examined under different thermal conditions, ranging from typical healthy continuous and periodic running duty cycles, to a deteriorated cooling system and winding fault conditions. It was demonstrated that the presented scheme has the potential to provide competent on-line measurement of critical machine thermal hot spots that are largely beyond effective reach of conventional thermal monitoring solutions. In addition, the ability of the proposed system to enable fault diagnosis through identification of fault induced localised thermal signature is also reported. The results demonstrate the capability of unambiguous recognition of inter-turn faults, including a single shorted turn, and diagnosis of fault severity, location and fault critical-thermal operating conditions. Finally, the winding thermal and electrical characteristics at inter-turn fault onset were investigated, enabling advanced understanding of fault thermal signature manifestation in a wide range of operating conditions. The thesis also investigated the use of the FBGST multi-physical sensing feature for extracting simultaneous thermal and mechanical information of rotary components for condition monitoring purposes. It is shown that a single FBG embedded in a bearing or rotor structure can enable simultaneous understanding of component's thermal and mechanical operating conditions, and thus improved understanding of their health status.

Modern spintronics involves the development of clean low-dimensional electronic systems with the goal of coherent control of spin transport for quantum-based computation. Graphene is an ideal platform towards this goal, thanks to its potential to embed exceptionally high-quality electronic transport. In this thesis, we report the observation of efficient and tunable spin injection in high-quality and fully-encapsulated graphene, enabled by van der Waals heterostructures with one-dimensional (1D) contacts. The nanoscale-wide 1D contacts offer a sizeable and gate-tunable contact resistance, allowing spin injection both at room and at low temperature, with the latter exhibiting spin injection efficiency comparable with standard two-dimensional (2D) tunnel contacts. This architecture prevents significant doping from the contacts within the graphene channel, a problem inherent to standard 2D contacts, allowing us to routinely achieve high-quality channels with mobilities up to approx. 130,000 cm2V-1s-1, which remains constant within a range of technologically relevant carrier densities, and electron mean free paths comparable with the dimensions of the channel, which may indicate that the device is in a quasi-ballistic spin transport regime. Electrical control of cooling and heating is of considerable interest in the electronics industry. The study of thermoelectric effects in 2D heterostructures may deliver a technology to these important applications. Recently, thermoelectric effects in graphene have been studied when fabricated on SiO2, Boron Nitride (hBN) substrates and fully hBN encapsulated. However, when the graphene and hBN are aligned the Density of States (DoS) is modified; thermoelectrics in such structures has not been studied to date. In this thesis, we report the use of thermoelectric measurements as a complementary characterisation of 2D materials electronic band structure. Additionally, we show extra features around the secondary Dirac points of the thermopower, for an aligned device, which are different from that of the non-aligned device. The trends for the primary feature and these secondary features with respect to the device temperature has the potential to lead to novel technologies.

For decades both industry and academia have been interested in situation awareness, from individual situation awareness to system situation awareness of dynamic collaborative systems. Several theories and definitions exist for situation awareness and although considerable research has been conducted in this domain no definitive consensus has been reached. Therefore, the purpose of the research in this thesis is not develop new theories or definitions, but to explore how situation awareness presents itself in teams and systems in terms of team cognition. The methods used in this thesis include simulating team tasks using agent-based modelling, analysing team knowledge using concept maps and analysing team processes using entropy. In order to remove the risk of intrusion on the tasks being explored, the communications of team members are recorded and used as the primary data for the analyses conducted. Visually presenting knowledge of agents using concept maps made it easier to understand how the information was stored and transferred throughout the teams. An interesting result showed that it was not important for all agents to have the same information when key decisions were made and that when information is not shared the team performed better and with greater accuracy than when there was a focus on information sharing. Visually presenting team processes using entropy and process distribution allowed for patterns of behaviour to be identified. Results show that while individuals within teams feel confident with the amount of knowledge they have they will focus on working independent up until the point they can no longer achieve results on their own, at that point the team shifts to teamworking. The differences between teamwork and taskwork are related to the theories of shared and distributed situation awareness, concluding that shifts in team processes represent shifts in the two types of situation awareness.

Emissivity is the quantity representing the radiative properties of materials that must be prior measured precisely to undertake accurate measurements for radiation thermometry. This work presents the development and validation of three emissivity measurement instruments to undertake studies on emissivity behaviours for materials with complex surface conditions from 200 to 1150 °C. These instruments aim to offer accurate emissivity references for use in non-contact temperature measurements and materials science.

Metamaterials are artificial materials that can exhibit extraordinary electromagnetic properties that have never been observed in nature. By engineering elements of metamaterials, one can achieve a so-called left-handed or double-negative medium, enabling new physics and new potential applications. Applications can be found in a wide variety of fields, such as invisibility cloaking, wireless power and data transfer, magnetic resonance imaging and body area networking. In addition, metamaterials are known to support magnetoinductive (MI) waves. By their very nature, magnetoinductive waves with strong inter-element coupling offers low loss and wide bandwidth channels. Recently, one of the new directions of research is low-frequency communications in RF-challenging environments (i.e., underground/underwater or in vivo medical diagnosis and treatment applications) with the aid of MI waves, due to their advantages over other transverse electromagnetic (TEM) wave-based techniques in penetrating lossy medium. Underground/underwater, or human body and tissues, are an extreme challenge for the conventional wireless links using TEM waves. TEM waves interact strongly with the lossy background medium resulting in high ohmic losses, the need for large antenna size and often rapid attenuation which increases in severity with frequency. The use of arrays of resonating circuits forming MI waveguides therefore has been proposed in communications in lossy medium to achieve longer ranges as well as robust channels of transmission. Elsewhere, modelling of propagation of low-frequency waves in a lossy medium has been paid increasing attention to embedded biomedical systems and body area networking for health-related applications, due to the fact that the human body and organs are relatively conductive from about 0.05 S/m to about 1.5 S/m at microwave frequencies. In terms of signal propagation, the attenuation of a signal in a given medium is the most important parameter. The radiation of TEM waves in conductors was understood and described a long time ago by Maxwell’s equations. However, there is as yet no theoretical or numerical model to express the penetration of MI waves in conductive environments. The main aims of this research are (i) firstly, to investigate the impact of an intervening conductive medium on the magnetic coupling of two low frequency coils which is usually presented by a questionable assumption in most literature, (ii) secondly to examine the field distribution and the mutual coupling, and by that, the attenuation and phase delay between two low-frequency coils embedded in a homogeneous dissipative medium and (iii) finally, study a completed circuit model, that takes into account all the effects of the background medium on coils coupled in close proximity and thereby, derive a new dispersion equation for the MI waves in these media. Since the dispersion equation is known, the propagation of MI waves will be fully understood and some relevant applications can be proposed. Besides, with the novel equivalent circuit model being considered, this research has laid the foundation for the theory of wireless power transfer in non-conventional environments.

The reluctance of previous governments to make adequate provision for the long-term storage and disposal of nuclear waste has resulted in an imminent and significant decommissioning burden, as existing 'temporary' solutions are well beyond the end of their useful lives. It is preferable to use remote handling solutions for decommissioning, in order to ensure worker safety; however the susceptibility of electronic devices to radiation damage is often unclear. This document discusses factors affecting the survivability of electronics in radio-active environ- ments, and the design of GaAs circuits suitable for high-speed communications applications in such an environment. Techniques for performing accurate RF measurements and electromagnetic simulations are also discussed. Measurements of the designed devices are presented. Results of irradiation testing of a silicon rotary encoder and GaAs pHEMT transistors, on a related process to that used for the aforementioned circuits, are also presented, demonstrating Mrad hardness for the GaAs devices and krad hardness for the silicon; although the silicon and GaAs results are not directly comparable they are nevertheless of interest to illustrate the difference in radiation hardness between the two technologies, and the GaAs results suggest that the communications circuits designed on the process are likely to be highly radiation-tolerant. Neither the hardness of the encoders tested nor the GaAs process considered are believed to have been measured before. Analysis of a novel technique for improving the performance of certain common RF circuit elements at high frequencies, using stubs of an unusual form of lossy transmission line, is also described; an improvement in the matching of a load standard over frequency is demonstrated in simulation.

In this thesis, I used atomistic and micromagnetic model to study the dynamics behaviour of the domain wall and skyrmion in confined nanostructures driven by applied magnetic field, spin polarized current and voltage control magnetic anisotropy gradient. I also studied the thermal behaviour of the magnetic skyrmion in magnetic ultra-thin film. I study the domain structure and the magnetic switching in the Permalloy (Fe20Ni80) nanoscale magnetic junctions with different thicknesses by using micromagnetic simulations. My work shows that the nanoscale magnetic junction has the potential to be used as a building block for future spin-based data storage or logic computing technologies. The current-driven skyrmion motion in a narrow ferromagnetic nano-track with voltage-controlled magnetic anisotropy (VCMA) is studied numerically. The skyrmionium dynamics in a nano-track with voltage-controlled perpendicular magnetic anisotropy (VCMA) also has been reported. The results provide guidelines for practical realization of the skyrmion-based information channel, diode, and skyrmion-based electronic devices such as racetrack memory. The thermal-induced phase transition to a skyrmion state in IrCoPt has been demonstrated by the atomistic simulations parametrised from ab-initio calculations which include long-range exchange interactions. The simulation results give a clear vision of the thermal-induced behaviour in a chiral magnetic thin film.

This study undertook the development and testing of fibre optic coupled infrared thermometers (IRTs) that could substitute for thermocouples in harsh conditions that would affect contact temperature measurements deleteriously. The IRTs have been configured without photodiode cooling and signal chopping but achieved low minimum measurable temperatures, fast responses and good sensitivities. IRTs were configured with mid-wave infrared (MWIR) and short-wave infrared (SWIR) photodiodes, to measure over different temperature ranges. The thermometers had small footprints, therefore could be installed into constrained spaces and not cause interference with the process. The MWIR thermometers were substituted for thermocouples in high temperature conditions in end milling tool temperature measurements and reactive electrochemical conditions in Lithium-ion cell temperature measurements. The conditions into which the fibre optics were embedded would lead to inaccurate measurements from thermocouples, whereas the fibre optic and remotely positioned IRT offered immunity against these errors. Calibration drift is a major problem that afflicts thermocouple temperature measurements. There has been progress towards addressing this weakness with improved thermocouples. The SWIR thermometer used a zero drift operational amplifier to minimise offset voltage, drift and noise. The IRT was coupled to a sapphire fibre optic probe that had tin deposited onto the core to form an integral fixed point temperature calibration cell. This low drift IRT provided an increment towards creating a drift-free, self-calibrating IRT that would substitute for thermocouples with integral calibration capabilities. The feasibility of substituting thermocouples with embedded fibre optics coupled to IRTs has been demonstrated and potential improvements of these thermometers have been identified.

Rotating machines exist in a wide variety of industrial processes such as power generation, vehicle transportation, manufacturing and other industries. Often, they operate continuously for a long time and under a variety of harsh conditions. Thus, they are prone to failure in one or more of their components, causing a decrease in system efficiency and, ultimately, a complete breakdown. It is well known that when a machine component begins to deteriorate, its dynamic behaviour changes. Monitoring relevant parameters allows rapid identification of any changes that are taking place and possible failure modes. Rotational speed and vibration are key parameters in the condition monitoring of rotating machinery. These parameters usually contain abundant fault-related information about the machines. A literature review is conducted to examine all existing techniques for rotational speed and vibration measurements. Advantages and existing limitations of the reviewed techniques are discussed. Consequently, a technical strategy, incorporating electrostatic sensing and digital signal processing techniques is proposed. Mathematical modelling is established and used to determine the characteristics of electrostatic sensors. The results of the model are used to optimise the electrode and markers design. A novel electrostatic measurement system, including sensing electrodes, signal conditioning circuit and signal processing unit, has been designed and implemented to provide a solution to a robust online monitoring of rotational speed and vibration of rotating metallic shafts. Extensive evaluations of the prototype system were conducted on purpose-built laboratory scale test rigs. Experimental results from the rotational speed measurement suggest that the measurement error is within ±0.2% over the speed range from 40 rpm to 3000 rpm with a repeatability less than 0.7%. Results obtained from the vibration displacement measurement of an unbalanced metallic shafts with 0.5mm eccentricity have demonstrated that the measurement system has a relative error no greater than ±0.6% under all test conditions. The developed measurement system can potentially be incorporated into a condition monitoring system, integrated with fault detection and diagnosis algorithms, to assess the working condition of rotating machinery, detect incipient faults and allow repairs to be scheduled.

Josephson junctions (JJs) have become an integral component in superconducting electronics due to their non-linear response, sensitivity to magnetic fields and microwaves and the ability to generate high frequency electromagnetic radiation. This thesis investigates three phenomena of JJ arrays; the ratchet effect, flux flow resonances and Shapiro steps. A simulation model (which is similar to discrete Josephson transmission line) was used extensively to explore what consequence a) multiple array parameters such as the size of the superconducting loops separating the JJs, array spatial asymmetry, number of JJs in the array, structural fluctuations of the array due to imperfections of fabrication or intrinsic inhomogeneity of the Josephson critical current along the array; b) or junction parameters such as junction widths, junction critical currents; c) or finally thermal fluctuations due to the finite temperature of operation have on these three effects, as such this thesis is split into three parts. In the presence of an externally applied magnetic field, ratchet effects have been reproduced numerically in the current voltage characteristics (IVCs) of asymmetric JJ arrays. Firstly, the ratchet efficiency of several arrays was simulated and found that each array design has a unique pattern of efficiency which could be reversed and tuned by changing the applied magnetic field. Larger efficiencies could be achieved with larger numbers of JJs in the array. Larger arrays were able to withstand more amounts of noise than smaller arrays. Flux flow resonances have been reproduced numerically on the IVCs of the JJ arrays for multiple values of applied magnetic field. The derivatives (dI/dV) have been investigated in detail. The spatial asymmetry of the arrays has a large influence on the flux flow resonance produced by the JJ arrays, with each design producing a different pattern of resonances. The number of junctions in an array also has an effect on the resonances, the voltage location of smaller arrays show periodic oscillations with magnetic field whereas larger arrays do not, the resonance voltage and dI/dV locations increases to a maximum and levels out. Simulations also showed that the magnetic field around each insulating loop for larger arrays had distinct distribution around specific holes. Integer and fractional Shapiro steps were reproduced numerically on the IVCs of the JJ arrays. When analysing Shapiro steps, DC and AC magnetic field and / or AC current were applied. The voltage steps appeared in different preferences for steps with positive or negative voltages. It was found that larger steps were a result of the phase difference across the JJs being in phase with each other. An alternating magnetic field was applied to the arrays which produced similar steps caused by the induced current within the array. Combining both AC current and an alternating magnetic field resulted in pumping between the two effects leading to larger steps when the frequencies of both coincide. For the case of ratchet effects and flux flow resonances a detailed qualitive comparison was made with experimental data of JJ arrays made of yttrium barium copper oxide (YBCO).

Due to the advantage of abundant space and more consistent wind speed, interest in offshore wind energy has significantly increased. To reduce the cost related to offshore wind power integration, this thesis investigates the control, modelling and operation of offshore wind farms connected with diode rectifier HVDC (DR-HVDC) systems, where a diode rectifier is used offshore and a modular multilevel converter (MMC) is used onshore. Compared to MMC based HVDC systems, the main benefits of DR-HVDC are lower investment, lower space requirement, higher efficiency and improved robustness. However, as the diode rectifier is unable to control offshore frequency and voltage as the MMC counterpart does, permanent magnet synchronous generator based wind turbines (WTs) have to perform more control functions including the establishment of the offshore AC network. In order to ensure that each WT converter autonomously contributes to the regulation of the overall offshore voltage and frequency, a distributed phase locked loop-based control for WT converters connected with DR-HVDC is proposed. A small-signal state-space model of the DR-HVDC system is developed to justify the use of active power and voltage (P-V) control, and reactive power and frequency (Q-f) control. The WT level analysis is implemented and to reveal the coupling between WT active power and reactive power with such control scheme. An angle compensation control is further proposed to reduce the coupling during WT active power change. Small-signal analysis is also carried out to investigate the impact of the angle compensation control parameters, active power control parameters and reactive power control parameters on system stability. To ride-through onshore faults, an active MMC DC voltage control combined with a WT overvoltage limiting control is proposed. With this control scheme, active power re-balance between the offshore and onshore side is achieved faster and thus, the MMC submodule capacitor overvoltage is alleviated. During offshore AC faults, a current limiting method is proposed to ensure the safe operation of WTs and an effective offshore overcurrent protection solution is proposed for fault detection and isolation. In addition, the system response during permanent DC pole-to-pole faults is analysed. Finally, the operation of offshore wind farms connected with two parallel transmission links, i.e. a DR-HVDC and a HVAC link, is investigated. A hierarchical control structure which contains primary, secondary and tertiary controls, is proposed to ensure reliable operation and smooth transition between DR-HVDC mode, HVAC mode and parallel mode.

Over the last decade there has been a vast increase in the use of composite materials in many engineering aspects. This increase is due to their ability to provide increased mechanical strength while also yielding weight savings in structures. Not only can composites be manufactured to create complex geometry components, their layup can be designed to provide optimum strength and stiffness. Carbon Fibre Reinforced Polymer (CFRP) composite materials pose a challenge for ultrasonic Non Destructive Evaluation (NDE) inspections due to their anisotropic material properties and often complex morphology. This Thesis develops an integrated framework to allow for accurate Finite Element Analysis (FEA) simulations of CFRP components to be constructed. The developed framework can generate the CFRP model geometry from a range of different sources. This enables the ability to construct models based on the design specification of the component, through scripting or importing of component design information, as well as creating models based on their real structure, from images taken from micrographs or X-Ray CT data. This allows for ultrasonic NDE inspections to be simulated to develop a better understanding of inspection performance, as well as act as a tool to aid inspection design. A parametric study for the inspection of a flat CFRP component is presented to demonstrate the benefit of simulation to aid the understanding of the ultrasonic response and ability to optimise the inspection setup. Further work focuses on the simulation of inspections of tapered geometry components. This adds an additional level of complexity to the inspection and FEA simulation is shown to be an effective technique to optimise specific inspection parameters. Importantly,inspection amplitude maps are used as a tool, both to understand the limitations of an inspection configuration, and to develop new inspection approaches.

In broadband array processing applications, an extension of the eigenvalue decomposition (EVD) to parahermitian Laurent polynomial matrices - named the polynomial matrix EVD (PEVD) -” has proven to be a useful tool for the decomposition of spacetime covariance matrices and their associated cross-spectral density matrices. Existing PEVD methods typically operate in the time domain and utilise iterative frameworks established by the second-order sequential best rotation (SBR2) or sequential matrix diagonalisation (SMD) algorithms. However, motivated by recent discoveries that establish the existence of an analytic PEVD -€” which is rarely recovered by SBR2 or SMD -” alternative algorithms that better meet analyticity by operating in the discrete Fourier transform (DFT)-domain have received increasing attention. While offering promising results in applications including broadband MIMO and beamforming, the PEVD has seen limited deployment in hardware due to its high computational complexity. If the PEVD is to be fully utilised, overcoming this bottleneck is paramount. This thesis therefore seeks to reduce the computational cost of iterative PEVD algorithms -” with particular emphasis on SMD - through the development of several novel algorithmic improvements. While these are effective, the complexity of the optimised algorithms still grows rapidly with the spatial dimensions of the decomposition. Steps are therefore taken to convert the sequential form of SMD to a novel reduced dimensionality and partially parallelisable divide-and-conquer architecture. The resulting algorithms are shown to converge an order of magnitude faster than existing methods for large spatial dimensions, and are well-suited to application scenarios with many sensors. Further in this thesis, an investigation into DFT-based algorithms highlights their potential to offer compact, analytic solutions to the PEVD. Subsequently, two novel DFT-based algorithms improve upon an existing method by reducing decomposition error and eliminating a priori knowledge requirements. Finally, an innovative strategy is shown to be capable of extracting a minimum-order solution to the PEVD.

The existence of harmonics and oscillations represent major problems for reliable operation of power system components. Therefore, investigating their response requires finding an appropriate model which reflects their response including these variations. The mathematical derivation of the state space models and impedance models of some of voltage source converters in flexible ac transmission systems (VSC-FACTS) systems is presented using synchronous dq and dq-dynamic phasor approach. Two types of the VSC-FACTS devices are studied in this thesis; the static synchronous compensator (STATCOM) due to its popularity in the power system network and static synchronous series compensator (SSSC) due to its effective on damping system oscillations. The effect of mechanical section of the synchronous machine and turbine sections on the machine impedance is analysed. A generalised state space and impedance modelling is proposed by converting the synchronous dq models to dq-dynamic phasor models A development of harmonic stability criteria for the proposed modelling is presented. The proposed modelling is employed to present the harmonics effect on the STATCOM and SSSC response and to identify their unbalanced operation in frequency domain. The main features of the proposed modelling technique are compared comprehensively with the conventional modelling techniques for stability studies assessment. It shows the advantages of proposed method and the importance of including the harmonics in the stability studies. A comparison between different control modes of the SSSC is discussed in the frequency domain. The effectiveness of these control modes on damping system oscillations is investigated using the impedance concept. It presented the effectiveness of impedance control mode on damping system oscillations over the other control modes. A fast impedance measurement unit (IMU) is proposed to monitor the small signal stability. The proposed IMU can measure accurately the system impedance within a very short time without any filtering requirements. The effect of changing the STATCOM control gains on the impedance norm is investigated. Also, the effect of shunt and series virtual impedances on the infinite norm of the STATCOM impedance which can be used by network operators to retain the stability is discussed.

Pressure tube inspection within CANDU nuclear reactors is a critical maintenance operation to identify and track the growth of defects. Current inspection approaches utilising ultrasonic techniques are technically challenging due to transducer alignment caused by the tube dimensional changes. This Thesis focuses on enhancing ultrasonic techniques to improve the inspection accuracy by introducing signal processing algorithms and phased array technology. This work is motivated by the nuclear industry desire to reduce the time and cost consuming replica processes. The Synthetic Aperture Focusing Technique (SAFT) has been applied to industrial inspection data where the ultrasonic image performance is poorly-focused. The transducer focal point operates as a virtual source to transmit ultrasound with a corresponding beam angle. Subsequently, the refocused image demonstrates a distinct improvement in the measurement of defect width. Regarding to the defect depth measurement, this Thesis proposes a wavelet analysis method, which employs the Haar wavelet to decompose the original poorly-focused A-scan signal and reconstruct the defect information from selected frequency components within the transducer operational bandwidth. Compared to the original image characterisation, this method provides an improved estimate of defect depth within an acceptable error ±0.04 mm. A hybrid simulation platform for ultrasonic phased array transducer inspection has been developed and experimentally validated, which combines the benefits of finite element modelling and analytical extrapolation. This approach has been used to study a range of phased array imaging solutions based on both the Total Focusing Method and array SAFT processing. The phased array technique is predicted to improve the accuracy of characterising defects on the inner and outer surfaces of the pressure tube and a dual array system incorporating 32-element 5 and 10 MHz arrays is proposed as a potential future sensor head configuration. The results conclude there is significant potential to improve the quality of the inspection data.