This thesis presents work on a method for the reconstruction of the longitudinal temperature gradient in a power cable using Frequency Domain Reflectometry, with a particular focus on the reconstruction of small temperature perturbations, but applicable to all temperature profiles. The work is presented as a proof-of-concept and has attracted much attention from the industry.

Monitoring the spatial temperature profile of a power cable is vital for its operation, as temperature influences its current-carrying capacity, insulation ageing, and overall reliability. A method requiring only a single connection at one cable end avoids the cost and complexity of installing distributed sensors, making it practical for monitoring existing cable systems. Since transmission-line parameters vary with temperature, accurately understanding this dependency is essential for predicting the performance under different environmental and current-carrying circumstances.

A theoretical framework is constructed that allows for a mathematical model which converts Frequency Domain Reflectometry measurements, measured by a Vector Network Analyser with a single port connection, into a differential spatial characteristic impedance profile. By making use of the property that the characteristic impedance is slightly temperature-dependent, a complete reconstruction of a cable's temperature profile can be obtained.

MATLAB software has been developed to monitor the differential characteristic impedance profile in real time of any cable using Vector Network Analyser measurements with a single port connection.

This model has been validated through lab experiments, for which several experimental test beds have been established and used to test the models performance with multiple cable types, including a power cable.

The method has demonstrated to be capable of detecting differential characteristic impedance gradients along a power cable, providing good accuracy for experiments in a controlled environment, allowing for the reconstruction of any differential longitudinal temperature gradient in transmission lines.

The measurement technique has been extended to the capability of measuring the frequency dependency of most transmission line parameters that characterise a power cable, yielding a complete frequency-dependent profile of the parameters, operating under the assumption that the parameters are uniform over the cable.

Lab experiments have been established to validate the parameter characterisation measurement technique using the single port Vector Network Analyser connection. The temperature- and frequency dependency of each transmission line parameter has been measured through these experiments.

The spatial reconstruction of the differential longitudinal temperature gradient in power cables shows promising results for practical applications. The parameter characterisation method using the Vector Network Analyser shows considerable insight in how the parameters of a power cable depend on temperature and frequency. Future work would benefit from research on implementation of the longitudinal temperature profile reconstruction in non-controlled and noisy environments, getting this project one step closer to industrial implementation. ... Read more

The main goal of this project is to utilize a commercially available OpenBCI Ultracortex IV for the measurement of Electroencephalogram(EEG) signals. A pipeline consisting of preprocessing, classification and extraction is employed to transform the motor execution EEG signal into a singular Left or Right output. This output is then further displayed on an Interface that offers the option to either calibrate or play a simple game.

The Ultracortex and relevant software were used to determine the sensor layout, with the placement of the sensors focused on areas which exhibited high cortical activity during motor execution. Experiments were strategically designed to optimize our chance of successful readings and OpenVIBE was used in conjecture with preprocessing filters to save the raw and filtered data which was further sent to the Machine Learning group.

The collected data was analyzed through Spectrograms, Power Spectral Density(PSD) and Event-Related Desynchronization/Synchronization(ERDS) plots. The analysis aimed to confirm whether the desired activity occurred and whether the observed patterns resemble those documented in other research papers.

The data from the headset is live-streamed to the interface via Lab Streaming Layer(LSL) where it undergoes further filtering before being sent to the Machine learning group. This process was done through python libraries which then allowed for efficient and effective communication between the other groups. ... Read more