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(2023)ThesisAs the first family of error correction codes that is theoretically proved to achieve channel capacity under successive cancellation (SC) decoder when code length tends to infinity, the polar coding scheme is regarded as a milestone in the information and coding theory. However, at finite code lengths, the SC decoder does not provide a satisfactory error correction performance compared to other codes such as low-density parity check (LDPC) codes. To overcome this weakness, various polar code constructions and decoding algorithms have been proposed. In this thesis, we first study all the significant developments in the field of polar coding covering 1) major signal-to-noise ratio (SNR)-dependent code constructions as well as universal reliability sequence, and 2) decoding algorithms including SC, SC list (SCL), cyclic redundancy check-aided SCL (CA-SCL), belief propagation (BP), and soft cancellation (SCAN) decoders. This study analyzes the performance and computational complexity of the available approaches to improve polar codes. Then, we turn our focus on Polarization-adjusted convolutional (PAC) codes which were recently proposed. These promising codes can further enhance the performance with (near) maximum likelihood (ML) decoders such as sequential decoders and sphere decoders. Convolutional precoding enables PAC codes to reduce the number of minimum-weight codewords of polar codes. Since this convolutional precoding cannot be employed with sphere decoding directly due to the direction of this decoding scheme as the lower rectangular shape of polar transform demands. We propose a selective reverse convolutional precoding scheme to reduce the error coefficient while avoiding the reduction in the minimum distance due to concatenation. The numerical results show that the proposed scheme can reduce the code’s error coefficient significantly resulting in improving the block error rate of polar codes under sphere decoding by up to 0.5 dB. Moreover, the hardware design of a decoding algorithm is considered. The memory requirement for the intermediate information in decoding algorithms takes a large silicon area, in particular belief propagation (BP) decoding. As an alternative to uniform quantization, we suggest employing a non-uniform quantization scheme that reduces the decoder’s memory requirement and improves its performance. To evaluate, we design a field programmable gate array (FPGA)-based hardware architecture for the BP decoder. A lookup table-based architecture is designed for the non-uniform quantization scheme to preserve the throughput. The design is verified on a development board. The numerical results reveal the expected performance improvement while reducing the memory requirement.
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(2022)ThesisEye movement detection, separating the eye positions into distinct oculomotor events such as saccade and fixation, has been associated with cognitive load classification, referring to the process of estimating the mental effort involved with a certain task. However, there exist three questions remaining to be answered for wearable applications: (i) will algorithms originally developed for fixation and saccade detection from gaze positions give similar accuracy from pupil center positions, particularly when the head is not fixed?; (ii) how much improvement to the performance of cognitive load classification can be achieved by separating fixation and saccade?; and (iii) will the fixation- and saccade-related measure be affected by differing cognitive load processes from diverse task designs? Regarding the first research question, three representative saccade detection algorithms are applied to both pupil center positions and gaze positions collected with and without head movement, and their performance is evaluated against a stimulus-based ground truth under different measures. Results from a novel dataset recorded using wearable infrared cameras indicate that saccade/fixation detection using pupil center positions generally pro- vides better performance than using gaze positions with an 8.6% improvement in Cohen’s Kappa. Regarding the second and third research questions, statistical tests of several pupil-related measures extracted from all samples, fixation-only samples and saccade-only samples are evaluated for varied cognitive load levels, which indicate that pupil-related measures from fixation-only samples can be used as a substitute for those from all samples in distinguish- ing different levels of cognitive loads. From the statistical test results of several fixation- and saccade-related measures across two task types, the possibility for such measures to distinguish varied cognitive load levels, together with their trends among varied cognitive load levels are different under varied cognitive load processes. Furthermore, for the cognitive load classification systems trained with and without fixation- and saccade-related features, accuracy can be improved by 14.0%-23.4% for a random forest classifier across two different task types by including fixation and saccade-related features. In general, this thesis contributes to fixation and saccade based cognitive load classification research by demonstrating that pupil center positions can be used as an alternative to gaze positions for fixation and saccade detection in a wearable context, and moreover, fixation and saccade separation can improve the cognitive load classification performance.
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(2023)ThesisThe stochastic nature of microgrid (MG) elements, makes the optimal energy scheduling a nonlinear and complex problem that system dynamics and operational constraints need to be accurately modelled to represent the full characteristics of the system. To optimise the energy scheduling, the optimality of the mathematical model-based optimisation methods is only guaranteed by continuous model observation and validations and any modifications to the network architecture and system dynamics require network re-modelling with updated operational constraints. Therefore, the speed and efficiency of the decision-making process is impacted by the ongoing interventions from experts. This research proposes a data-driven and model-free energy management system (EMS) optimisation technique based on reinforcement learning (RL). In the proposed method, the operational cost minimisation problem is formulated as a Markov Decision Process in which the EMS agent finds an optimal policy that maximises the total achieved rewards without prior knowledge of the system model and through rewards and punishments. The performance of the proposed model is benchmarked with the model-based mixed integer nonlinear programming method and the results indicate that the RL approach provides near optimal solutions under various case studies with more than 93% accuracy compared to the mathematical benchmark. A two-stage decision-making framework is proposed next, where both “forecasting” and “optimisation” stages work in parallel to provide an autonomous and intelligent solution to the MG optimal energy scheduling problem. The long short-term memory (LSTM) network is used to provide an accurate projection of future stochastic state variables used in the RL decision-making process which in return simplifies the complexity of the RL state space and improves the learning efficiency and the convergence time. The simulations indicate that the LSTM-based forecasting effectively predicts the uncertain network parameters with mean absolute error ranging between 2.34 and 3.06 for renewable generation and load predictions and 10.14 for volatile grid transaction prices. By implementing the proposed framework, the self-driving, real-time and data-driven EMS optimisation technique can reduce the computational burden on the central EMS and the dependency on explicit model and the need for domain expertise, whilst minimising the operational costs of the network through an autonomous and intelligent solution.
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(2023)ThesisWith the increase of environment awareness and sustainable energy demand, green and renewable energy derived from inexhaustible natural sources, such as solar and wind, has attracted much interest. Wind energy, a key participant in the green energy market, is embraced by many countries as a replacement of traditional energy. Wind power generation, for its robust system structure and economic efficiency achieved by mass production, has a lower cost in the ever-growing market than most energy production relying on other technical routes. Building wind power facilities also have certain benefits of land shielding and ecology protection, because they are mostly three-dimensional constructions that would have little impact on the local environment. Since wind turbines need to be installed in areas with unabated wind flow for maximising energy production, they are often placed on the land with high annual average wind speed and long valid wind time, such as plateaus, mountains, and coasts. However, that also means the turbines would operate in a harsher environment for a considerable length of time; thus, it is crucial to maintain their operation safety, and the study of turbine components through numerical simulation is essential to achieve a balance between cost and safety. In the past decades, the direct-drive wind turbine has been one of the most widely used wind turbines. However, in recent years, the problem of parts aging has surfaced for the earlier batch of direct-drive wind turbines – the bearing has been detected as one of the many vulnerable parts. In direct-drive wind turbine, the temperature in the shaft plays a significant role and should be considered as a design parameter. The operation of the rotating shaft, fixed axle, and bearing is governed by the presence of the lubrication and the proper alignment of the assembly, Nevertheless, as the bearing of the wind turbines fatigues due to long-term stress and strain, the friction begins to increase, and the temperature of the bearing during its operation tends to increase. The rotating shaft with a relatively high temperature would heat the lubricating fluid and result in the change of physical properties of the lubricant. This causes a positive feedback circle in which the friction between the shaft and bearing will keep increasing, and eventually the bearing breakdowns and resulting in the failure of the direct-drive wind turbine system. Hence, it is imperative that the temperature in the shaft is maintained as low as possible so as to prevent further degradation of the bearing. This thesis aims to study the heat increase in a bearing of a wind turbine by focusing primarily on the heat increase in the bearing structure. The consideration of fins to increase the surface area for heat removal is investigated in this thesis. Fin structure is a typical structure that could be attached to the original structure as minor protrusions to enhance the heat transfer from the rotating shaft to the environment. Understanding of the mechanism of the fin enhanced heat transfer in rotation structure will be obtained, and feasibility of using a fin structure to reduce the bearing temperature of the wind turbine as recorded by the maximum temperature being experienced in the assembly will be attained for design considerations. Moreover, optimisation study of fin spacing and height in the rotating motion is also carried out. Additional information on the pressure on the surface of the fins, as well as the shear stress and maximum deformation caused by the rotation of the fins are analysed.
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(2023)ThesisThis thesis represents a body of work regarding the design, implementation and test of a quantum entropy source in a commercially available semiconductor process. The techniques proposed have been designed to be process-agnostic, meaning that the work presented is highly transferable. The analysis of literature provides the motivation for selecting a quantum mechanism as a noise generator to be utilised in an entropy source. Quantum tunnelling provides a non-deterministic rectifying process which mimics the statistics of a shot noise source which is a white Gaussian noise process that cannot be predicted by any analytical form. The dominance of Fowler Nordheim tunnelling at a high bias regime is assumed which allows for predictive small signal models and design equations to be formed. Numerical data and device measurements are presented by utilising a metal oxide semiconductor (MOS) capacitor structure, realised by grounding the drain and source of a MOS transistor. The thin oxide layer between the gate-bulk and the gate-drain, and gate-source presents a potential barrier that provides ideal tunnelling conditions. Devices of varying sizes across multiple chips are fabricated and tested to characterise inter and intra-wafer variation. Additional effort is spent creating temperature dependence and defect models. A methodology to utilise quantum tunnelling as a tool to extract series parasitic resistances is proposed and demonstrated, where by an analytical augmented model is able directly estimate the resistance value through either an analytical solution or parameter estimation techniques. This is verified with scattering parameter measurements in a practical experiment where the two methodologies produce almost identical results. Entropy measurements of the collected data are tested against the NIST SP800-90B test suite and show promising pre-conditioning performance. Entropy rates of up to 0.716 bits per bit are demonstrated when operating in extended lifespan (bias current of 3.5mA) and up to 0.91 bits per bit at a maximum possible bias (4mA) for a device surface area of 52.2mm^2. The effects of sample rate, bit depth and ADC linearities on the total entropy are discussed. It is concluded that additional research and implementation regarding sensing and measurement structures can greatly improve both the signal to noise ratio and bandwidth of the entropy source which in turn would result in a higher entropy rate.
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(2024)ThesisUnmanned aerial vehicle (UAV) techniques have developed rapidly within the past few decades. Using UAVs provides benefits in numerous applications such as site surveying, communication systems, parcel delivery, target tracking, etc. The high manoeuvrability of the drone and its ability to replace a certain amount of labour cost are the reasons why it can be widely chosen. There will be more applications of UAVs if they can have longer flight time, which is a very challenging hurdle because of the energy constraint of the onboard battery. One promising solution is to equip UAVs with some lightweight solar panels to maximize flight time. Therefore, more research is needed for solar-powered UAVs (SUAVs) in different environments. Firstly, this thesis introduces a privacy-aware navigation technique for a UAV as a fundamental of energy-aware navigation. A dynamic programming-based algorithm is developed to minimize the total privacy violation risk. This chapter serves as a preliminary work that inspired the author to focus on the navigation of SUAV. Secondly, based on the inspiration of privacy-aware navigation, an energy-aware path planning algorithm for an SUAV in an urban environment is developed. Compared with the existing minimum distance path planning algorithms, the proposed method takes the cost during flight and the solar energy gain from the sunshine into account, which is suitable for an SUAV. Finally, this thesis proposes a complex path planning strategy for an SUAV in a dynamic urban environment. Different from a static urban environment, some unknown moving obstacles make the problem difficult to solve. A hybrid mode path planning algorithm is proposed to adapt to this partially unknown environment. The algorithm is structured by a three-step progressive method, starting with an online energy-aware path planning algorithm, followed by a path following strategy and ending with the reactive obstacle avoidance method. The hybrid approach can handle the unknown uncertainties while maximizing the residual energy on the SUAVs. The proposed methods are presented and compared with some existing works. Simulation results are presented to prove the effectiveness of the proposed methods.