Magnetic-aided navigation using unmanned aerial vehicles (UAVs) is a promising method in case traditional navigation methods fail, but aeromagnetic platform noise from electric motors, electronics, and actuators can mask subtle geological signals. Traditional compensation methods, such as the Tolles-Lawson (TL) model, assume linear relationships between platform orientation and platform noise, failing to capture complex, time-varying disturbances from dynamic onboard systems. Existing machine learning approaches typically require noise-free reference measurements or known anomaly maps, resources often unavailable in practical surveying scenarios.

This thesis develops data-driven, reference-free methods for compensating platform noise in aeromagnetic measurements. The research addresses two key questions: whether deep learning methods can effectively predict and compensate platform noise without ground-truth references, and which drone subsystems contribute most significantly to platform noise.

A validation apporoach was implemented using flight data where crustal anomalies are naturally attenuated, enabling reference-free performance assessment. Comprehensive data from a fixed-wing UAV equipped with scalar and vector magnetometers, logging 273 platform-related input signals during figure-of-merit manoeuvres over an 800~m $\times$ 800~m survey area was used.

The compensation approach employed hierarchical modelling: Extended Tolles-Lawson (ETL) compensation incorporating drone inputs projected onto the magnetic field direction, followed by multilayer perceptron (MLP) neural networks trained to predict residuals. SHAP (SHapley Additive exPlanations) analysis provided model-agnostic feature importance assessment to identify the most influential platform inputs.

Results demonstrate substantial improvements over traditional methods. ETL compensation achieved improvement ratios averaging 7.31 for scalar magnetometers and 18.76 for vector magnetometers, compared to 4.90 and 10.19 respectively for standard TL compensation. The combined ETLNN approach (ETL + neural network) further enhanced performance to average improvement ratios of 8.87 on total field magnetometer and 22.22 on vector magnetometer data, a significant improvement over traditional TL methods.

SHAP analysis revealed that engine-related parameters (battery current, throttle commands), inertial measurement data (accelerations, gyroscopic rates, vibration), and attitude information (roll, pitch, yaw) are the primary contributors to platform noise. Features projected onto the magnetic field direction consistently outperformed raw inputs, validating the physical basis for this transformation, while derivative features contributed minimally whilst increasing overfitting.

The primary limitation is the inability to validate performance on data containing actual magnetic anomalies, as the high-altitude validation approach deliberately suppressed geological signals. Future work should prioritise validation using artificial magnetic sources or reference magnetometer configurations to assess preservation of genuine geological signals.

Organic electrochemical transistors (OECTs) are a promising technology in the field of bioelectronics. They bridge electronics and biology through their ability to operate in aqueous environments. Their ability to transduce biological signals into electronic ones makes them a preferred choice for bioelectronic applications, such as biosensing and neural interfaces. While p-type materials like PEDOT:PSS have become the benchmark for the OECT performance, the development of stable, high-mobility n-type channel materials remains a major challenge. These materials are often limited by poor air stability, low electron mobility, and restricted ionic transport. In this work, a newly developed semiconducting n-type polymer with cleavable side chains is investigated. The polymer, YYBC, undergoes a post-deposition thermal treatment, which leads to a porous, more hydrophilic polymer, YYAC. This cleaved version aims to improve the performance of an n-type OECT.
The research followed three main objectives. OECT devices were first fabricated in a cleanroom environment, then a measurement setup for steady-state and transient characterization was developed, and lastly, the new n-type polymer was evaluated. For device fabrication, standard microfabrication methods were employed. For the measurement setup, programmable source-measure units, a function generator, and an oscilloscope were controlled through MATLAB for data acquisition and analysis. Lastly, the performance of the n-type polymer was assessed using electrochemical and electrical characterization. Parameters such as volumetric capacitance, transconductance, and time response were evaluated.
Results of this work show that the post-deposition side-chain removal significantly improves the polymer’s electrochemical behaviour. The findings show improved performance metrics, indicating that the new n-type polymer can be a promising material for future OECT applications.

Microwave Induced Plasma (MIP) is an advanced decapsulation tool developed by Jiaco Instruments. The goal of this thesis is to optimise the current MIP system. There are two directions: 1. Improve the etching rate. 2. Find selectivity between different materials (Si, SiO2, SiN). The experiments are divided into two parts in total, improved cavity and CF4-based MIP machine etching selectivity experiment. The cavity experimental results demonstrate that high input power is the route to optimising MIP efficiency. Etch experiments demonstrate that the CF4/O2 gas recipe is effective in achieving etch selectivity and that temperature is a key factor influencing these etch selectivities.

In the context of designing a real-time brain-computer interface for playing a game using the OpenBCI Ultracortex "Mark IV" headset, this paper focuses on the work of the decoding subgroup. The primary responsibility is to analyse EEG data retrieved from the OpenBCI headset and classify the intention of the user. Our objective is to achieve a high-accuracy classification of the EEG signals. The paper is structured into three main sections: preprocessing, feature extraction, and classification. Multiple methods for preprocessing and classification of motor execution EEG signals will be analysed, striving to contribute to the real-time implementation of the project. The results of our work provides valuable insights for future research and development in this field.

As silicon carbide(SiC) gets more and more attention from the semiconductor industry due to its robust mechanical and chemical properties, reliable and standardized processing technologies such as reactive ion etching(RIE) for SiC are in great demand. This is because of the difficulty and challenge of fabricating micro devices on the SiC substrate. Although the high hardness and chemical inertness make SiC a good candidate for applications such as sensors in harsh environments, they also impede the development of SiC-based devices when considering processing. This thesis aims to develop a standardized inductively coupled plasma(ICP) reactive ion etching(RIE) process for 4H-SiC substrate etching. The developed process is expected to be applied in the fabrication of micro-electro-mechanical systems(MEMS). The specifications are a high etch rate, micro-masking-free surface, and high selectivity. First, a literature review was conducted to comprehensively study the characteristics of the SiC material and the mechanisms of the ICP RIE process. Second, a baseline recipe was developed guided by the theory studied in the literature works. Third, initial tests were conducted, and the preliminary optimizations with a focus on etch rate and micro masking suppression were performed. Fourth, the design of experiments(DOE) based on the preliminarily optimized recipe was conducted to study the effect of process parameters on etch rate, etch profile, and selectivity. Last, the optimized recipes with a focus on etch rate, etch profile, and selectivity were summed and listed. The achieved maximum etch rate was 1.26 µm/min. The maximum selectivity of the hard mask material to SiC was 153 when the nickel hard mask was used. Amicro-masking-free surface of SiC was achieved.