Atrial Fibrillation or AF is the most common heart rhythm anomaly affecting millions of people. This work explores the possibilities of reinterpreting speech processing techniques for use in atrial fibrillation detection. An existing method of modelling single heartbeat, single lead ECG signals by means of an ARMA model's amplitude response as a time domain signal is implemented. The parameters of the models are then used for AF detection by means of detecting P wave absence. For this detection, the distribution of the P wave associated parameters is compared to a GMM model of normal sinus rhythm beats obtained from a large number of recordings from different sources.

In this thesis, the implementation of a passive, chipless, frequency coded Radio-Frequency Identification (RFID) tag for bedload transport studies is proposed. The proposed tag will be deployed in the semi-arid Río Colorado river, Bolivia with the aim to develop quantitative sediment transport models that relate transport to grain size. The designed tag is an open-loop resonator with a fragment-loading structure, that has an op- timised configuration based on a Multiobjective Evolutionary Algorithm based on Decomposition combined with Enhanced Genetic Operators (MOEA/D-GO). The designed RFID tag can ideally reach a size of 4 by 4 millimetres with a maximum calculated reading range of 1.3 meters, and operates in the ultra wide band from 3 to 7 gigahertz. Numerous simulations on the tags were run to verify their properties. The tags proved to have a good directivity, quality factor and radio cross section on its resonant frequency. The tags could reach resonance frequencies as low as 2.9 gigahertz and quality factors as high as 130. The proof of concept on a Printed Circuit Board with an FR-4 substrate results in a tag of 6.4 by 3.4 millimetres. Unfortunately, these properties could not yet be verified by measuremen