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.

Epilepsy is a medical condition which is caused by excessive or synchronous neuronal activity of the brain cells. These activities can lead to attacks where the patient can lose conciseness or experiences random muscle cramps at seemingly any point in time. Using implantable on body sensors these seizure attacks could be detected and even prevented. These sensors would form a Medical Body Area Network (MBAN) which interconnects all of the sensors. This project looks at a proof of concept implementation of such an MBAN and focuses on a secure connection between an implant and a gateway device, which is a mobile phone. The implant and mobile phone will communicate with each other using Bluetooth Low Energy (BLE). This form of communication does not provide a secure pairing method for devices that lack in- and output capabilities, such as an implant. To set up a secure connection the data will be encrypted with an encryption key, which has to be shared between the implant and mobile phone. In order to do this in a secure way, an Out Of Band (OOB) channel will be used to pair the two devices. This thesis looks at three different OOB channels, Near Field Communication (NFC), ultrasound and galvanic coupling and compares them in therms of security, health safety, data rate, power consumption and feasibility.