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This thesis addresses radiation pattern synthesis problems for small linear periodic phased arrays (with array elements less then 10). Due to the small array size conventional pattern synthesis techniques fail to produce the required results. In the case of practical small arrays, mutual coupling and element pattern asymmetric effect degrade the array radiation performance. The main performance metrics considered in this thesis include side lobe level (SLL), gain, halfpower beamwidth (HPBW) and mainbeam scan direction. The conventional pattern synthesis approaches result in sub optimal gain, SLL and HPBW due to the limited number of elements and the mutual coupling involved. In case of difference pattern synthesis these factors resulted in lower difference pattern slope, degraded SLL and difference peak asymmetry. The sum and difference patterns are used in monopulse arrays and a simplified feed that could produce both patterns with acceptable radiation properties is of interest and has been examined (chapter 5). A conventional technique is applied to small arrays to synthesise a sector beam and there is limited control over the radiation pattern. It is shown that the mutual coupling has significant effect on the array radiation pattern and mitigation is necessary for optimum performance (chapter 6). Furthermore, wideband phased arrays may have a natural limitation of the HPBW in low gain applications and minimisation of the variation becomes important. Also the SLL variations for wideband antenna arrays in the presence of mutual coupling considerably degrade the radiation pattern. The mutual coupling degrades significantly the radiation pattern performance in case of small scanning wideband arrays (chapter 7). It is the primary goal of this thesis to develop an optimisation scheme thatis applied in the above scenarios (chapters 3 & 4). The only degree of freedom assumed is the array excitation. Optimised amplitude and phase for each element in the array are determined by the proposed scheme, concurrently. The deterministic optimisation techniques reported in the literature for the pattern synthesis may involve complicated problem modelling. The heuristic opti-misation techniques generally are computationally expensive. The proposedIntelligent z-space Boundary Condition-Particle Swarm Optimiser (IzBC-PSO)is based on a heuristic algorithm. This scheme can be applied to a wider rangeof problems without significant modifications and requires fewer computationscompared to the competing techniques.In order to verify the performance of IzBC-PSO antenna array measure-ments were performed in the receiving mode only using the online and offlinedigital beamforming setups described in chapter 8. The measurement resultsshow that the proposed scheme may be successfully applied with both onlineand offline digital beamformers for a practical small array (chapter 8).

Healthy vegetation are essential not just for environmental sustainability but also for the development of sustainable and liveable cities. It is undeniable that human activities are altering the vegetation landscape, with harmful implications for the climate. As a result, autonomous detection, health evaluation, and continual monitoring of the plants are required to ensure environmental sustainability. This thesis presents research on autonomous vegetation management using recent advances in deep learning. Currently, most towns do not have a system in place for detection and continual vegetation monitoring. On the one hand, a lack of public knowledge and political will could be a factor; on the other hand, no efficient and cost-effective technique of monitoring vegetation health has been established. Individual plants health condition data is essential since urban trees often develop as stand-alone objects. Manual annotation of these individual trees is a time-consuming, expensive, and inefficient operation that is normally done in person. As a result, skilled manual annotation cannot cover broad areas, and the data they create is out of date. However, autonomous vegetation management poses a number of challenges due to its multidisciplinary nature. It includes automated detection, health assessment, and monitoring of vegetation and trees by integrating techniques from computer vision, machine learning, and remote sensing. Other challenges include a lack of analysis-ready data and imaging diversity, as well as dealing with their dependence on weather variability. With a core focus on automation of vegetation management using deep learning and transfer learning, this thesis contributes novel techniques for Multi-view vegetation detection, robust calculation of vegetation index, and real- time vegetation health assessment using deep convolutional neural networks (CNNs) and deep learning frameworks. The thesis focuses on four general aspects: a) training CNN with possibly inaccurate labels and noisy image dataset; b) deriving semantic vegetation segmentation from the ordinal information contained in the image; c) retrieving semantic vegetation indexes from street-level imagery; and d) developing a vegetation health assessment and monitoring system. Firstly, it is essential to detect and segment the vegetation, and then calculate the pixel value of the semantic vegetation index. However, because the images in multi- sensory data are not identical, all image datasets must be registered before being fed into the model training. The dataset used for vegetation detection and segmentation was acquired from multi-sensors. The whole dataset was multi-temporal based; therefore, it was registered using deep affine features through a convolutional neural network. Secondly, after preparing the dataset, vegetation was segmented by using Deep CNN, a fully convolutional network, and U-net. Although the vegetation index interprets the health of a particular area’s vegetation when assessing small and large vegetation (trees, shrubs, grass, etc.), the health of large plants, such as trees, is determined by steam. In contrast, small plants’ leaves are evaluated to decide whether they are healthy or unhealthy. Therefore, initially, small plant health was assessed through their leaves by training a deep neural network and integrating that trained model into an internet of things (IoT) device such as AWS DeepLens. Another deep CNN was trained to assess the health of large plants and trees like Eucalyptus. This one could also tell which trees were healthy and which ones were unhealthy, as well as their geo-location. Thus, we may ultimately analyse the vegetation’s health in terms of the vegetation index throughout time on the basis of a semantic-based vegetation index and compute the index in a time-series fashion. This thesis shows that computer vision, deep learning and remote sensing approaches can be used to process street-level imagery in different places and cities, to help manage urban forests in new ways, such as biomass-surveillance and remote vegetation monitoring.

Oxygen reduction reaction (ORR) is crucial to the development of proton exchange membrane fuel cells (PEMFCs) and lithium-air batteries. These energy conversion and storage devices are promising due to their high energy density, high conversion efficiency and low environmental impact. In PEMFC, the proton exchange membrane has to be humidified which limits the operating temperature to below 100 oC. In Li-air batteries, the open cell structure leads to evaporation of solvent/electrolyte and results in drying of cell. Due to their low vapour pressure and good thermal stability, ionic liquids (ILs) have recently been used to address the issue of humidification in PEMFCs and evaporation in Li-air batteries. However, systematic accounts of the ORR across a range of ILs based electrolyte remain largely unavailable. This thesis aims to develop novel IL-based electrolytes and to understand the kinetics and mechanism of ORR process in these electrolytes. To this end, a range of novel protic ionic liquids (PILs) are synthesised and the kinetics and mechanism of ORR in these PILs at various electrode materials (Pt, Au and GC) are systematically studied using cyclic voltammetry, chronoamperometry and digital simulation. The PILs show excellent thermal stability and thermodynamic properties for ORR and can be used in non-humidified fuel cells. In addition, an efficient and non-precious catalyst (Fe-N/C) is fabricated that catalyses four electron reduction of oxygen in a PIL (diethylmethylammonium trifluoromethanesulfonate). This catalyst is characterised using X-ray photoelectron spectroscopy, X-ray diffraction, and electron microscopy. Notably, the fabricated Fe-N/C catalyst shows significantly better stability and tolerance to methanol than the benchmark Pt/C catalysts in the PIL. Furthermore, a range of aprotic mixed electrolytes based on aprotic ionic liquids (AILs) and dimethyl sulfoxide (DMSO) are developed to address the issues related to electrochemical stability, evaporation of electrolyte and solubility of discharge products in Li-O2 batteries. The mixed electrolytes exhibited higher current density and excellent reversibility of ORR-oxygen evolution reaction (OER) processes compared to pure DMSO or AILs. The influence of AIL concentration, Li+ ion, and AIL structures on ORR in mixed electrolytes is investigated to understand solubility and the rechargeability of the reduction products hence improving battery efficiency.