Electrical and Electronic Engineering - Doctoral Theses
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Item AI-enabled chipless RFID sensing system for reliable IoT applications(University College Cork, 2024) Rather, Nadeem; O'Flynn, Brendan; Buckley, John; Tedesco, Salvatore; Simorangkir, Roy B.V.B.; Science Foundation IrelandThe Internet of Things (IoT) is growing rapidly, driving the need for innovative and sustainable solutions for wireless identification and environmental monitoring. Passive Radio Frequency Identification technology (RFID) has been a key wireless communication technology enabling IoT. Recent advances have paved the way for battery-less, chipless RFID (CRFID), which eliminates the need for an integrated circuit (IC) component on the tag. This PhD thesis presents a new design strategy for developing concentric rings-based polarization-insensitive CRFID sensing tags. The proposed tag design approach of exponential spacing results in an 88.2% higher tag data encoding capacity than conventional designs which incorporate uniform spacing of the resonant rings. This is coupled with the idea of using the innermost ring for capacitive sensing. The concept of using RCS nulls for data encoding is implemented to enable convenient and accurate sensing by the innermost ring. This is made possible by adding an extra ring at the tag’s outermost edge. To enable robust detection of these tags, Artificial Intelligence (AI) is integrated on the reader side, employing both machine learning (ML) and deep learning (DL) techniques for decoding RCS EM signatures. In this research, ML and DL regression modelling techniques are applied to a dataset of measured RCS data derived from large-scale automated measurements of custom-designed, 4-bit CRFID sensor tags. The robotic measurement system is implemented using the first-of-its-kind automated data acquisition method using an industry-standard robot. The results show that all the ML/DL models were able to generalize well, that is, the ability of a model to perform accurately on new, previously unseen data. However, the 1D-CNN DL models outperformed the conventional ML models in the detection of ID and sensing values. In another contribution, a 3-bit depolarizing CRFID tag is developed and enabled for surface and shape robust detection using AI. For the first time reported, the system was trained on a dataset of 12,600 EM signatures, capturing varying surface permittivity, tilt angles, read ranges, and tag bend scenarios. The AI models using 1D-CNN are trained and validated, resulting in a low RMSE of 0.040 (0.66%) for tag ID detection. On the same dataset, for the first time, DL models were evaluated with Bidirectional Long Short-Term Memory (Bi-LSTM) and attention mechanism, further reducing the RMSE to 0.029 (0.48%). The outcomes of this thesis contribute significantly towards state-of-the-art of AI-enabled CRFID systems for robust and reliable real-world IoT applications.Item Modelling and growth of boron containing alloys of III-Nitrides for their application in the ultraviolet range(University College Cork, 2023) O'Connor, Thomas; Parbrook, Peter James; Schulz, Stefan; Science Foundation IrelandIII-Nitride semiconductor materials such as AlN, GaN, InN and their alloys, can emit light from the infrared to the deep ultraviolet region. Strong polarisation fields and significant differences in the optimum growth conditions for these binary compounds, make it difficult to understand these materials. BN is a relatively new material in this group which has the potential to improve strain engineering, increase the flexibility of bandgap engineering along with reducing the overall polarisation charge when alloyed with other III-Nitride materials. In this work, the Schrödinger equation was solved for a single quantum well system consisting of BxGa1-xN/ AlyGa1-yN and to understand the impact BN had on the wavefunction overlap along with the emission energy. Incorporation of wz-BN with GaN thin layers appears to prevent plastic relaxation of these layers with respect to their substrate and at higher temperatures resulting in phase separation of the material. A mechanism for recognising this clustering of boron atoms in the material is proposed using X-ray diffraction is presented. B(Al)GaN/AlGaN multiple quantum wells (MQWs) were grown and excited by photoluminescence (PL), and emission wavelengths between 328-349 nm were obtained. The incorporation of the lowest amounts of wurtzite-BN appears to result in a redshift and an improvement in the PL emission intensity of the material. However, this benefit comes at the cost of nanovoids/ nanopits forming in the material under the growth conditions used. Nanomasking effects dominate for the smallest levels of BN incorporation, with higher degrees of disorder, propagating into the barrier regions, being observed as the B/III ratio increased, as well as a reduction in the PL intensity. To our knowledge, this is the first report of both a ternary and quaternary B(Al)GaN/AlGaN MQW stacks grown by MOCVD on a c-plane AlN/sapphire templates for UV emission.Item Electromagnetic tracking methods and magnetic modelling for distortion compensation(University College Cork, 2023) Cavaliere, Marco; Cantillon-Murphy, Padraig; Hayes, John G.; Science Foundation Ireland; European Research CouncilThe thesis presents a comprehensive study of electromagnetic tracking (EMT), focusing on developing methods and techniques to reduce and compensate for distortions in the magnetic field through improved modelling and real-time correction methods. To this end, the research-oriented Anser EMT system is employed. Anser is the Latin name of the greylag goose, which uses the geomagnetic field for navigation. In this work, a general method for modelling magnetic fields is developed to significantly improve the Anser EMT magnetic model by correcting systematic errors and including magnetic shielding. Moreover, real-time compensation techniques for dynamic distortion are proposed using external reference sensors. Further improvements are demonstrated for dynamic tracking by optimising the EMT model and algorithm. Finally, the effectiveness of the Anser EMT system for developing novel EMT applications is demonstrated by introducing alternative tracking techniques based on the magnetic scalar potential formulation, particularly suited for tracking the elongated sensor coils used in medical applications, and on the magnetic vector potential formulation, for tracking large-area PCB coils. Overall, this work provides the theoretical and experimental basis for a new approach to distortion rejection in EMT systems with significant potential for future clinical benefit in the years to come.Item Electrochemical sensor interface(University College Cork, 2023) Murphy, Aidan; O'Connell, Ivan; O'Riordan, AlanRecent advances in nanotechnology have led to the development of electrochemical sensors that utilize electrodes with a width of one micron or less. This increased sensitivity has opened up a range of new electrochemical sensing applications, allowing the technology to move beyond the confines of the laboratory and into real-world settings. However, the interfacial electronics used for these sensors are often too bulky for portable use and can be prohibitively expensive due to the low current measurement capability required from the instrument. To address this challenge, this thesis presents cost-effective measures for creating portable interfaces specifically designed for ultra-micro and nano-scale electrochemical sensors. A portable data acquisition system has been developed to interface to nano and ultra-micro scale electrochemical sensors at the point of use through voltammetry. It can perform a range of voltammetric tests, including cyclic voltammetry, square wave voltammetry and generator collector voltammetry. The data acquisition system interfaces to a smartphone, operates from a rechargeable battery and is of suitable form factor to ensure that it’s fully portable. By utilising commercially available components, this system has been developed to lower the barrier for entry for the development of emerging portable electrochemical sensing technologies at micro and nano scale. A second data acquisition system has been developed to interface to nano and ultra micro electrochemical immunosensors through Electrochemical Impedance Spectroscopy. Results from the device were benchmarked against laboratory equipment to ensure it is of suitable sensitivity to be fit for purpose. The printed circuit board has been designed such that it can be integrated into a handheld device suitable for operatives such as veterinarians. This system will aid in the detection of biological agents such as viruses and antibodies at the point of sample, particularly in electrochemical sensors designed for agricultural applications.Item Planning studies for distribution grids with high penetration of distributed energy resources: the challenge of fairness in future electricity networks(University College Cork, 2023-05-02) Cuenca, Juan Jose; Hayes, Barry; Leahy, Paul; Massey, Beth; Department of Jobs, Enterprise and Innovation; Government of HuilaThe inclusion of distributed energy resources and electrification of heat and transport is creating additional supply and demand stresses that distribution grids were not originally designed for. The flows of energy and revenue are changing in magnitude and direction, making these grids more dynamic over time. In this changing landscape, the traditional approach for planning in distribution networks of "oversize, fit and forget" is not enough. A review of the literature in grid planning shows that the current focus is on transmission-network-inspired methods that are not realistically scalable for the distribution network case. Accordingly, this thesis presents a collection of novel technical and economic methodologies to transform the planning paradigm into an active one, including sharing economy concepts. First, this work presents a technology-agnostic impartial method to assign customers with export capacity for distributed generation. Subsequently, a new method to determine location, size, and prioritisation of flexibility resources at the distribution level is formulated using information on forecasted constraints and grid topology, this includes obtaining a distribution network expansion plan. Next, this thesis performs a technical/economic analysis of future distribution grids. Through co-simulation of electricity distribution networks and decentralised electricity trading platforms, an advanced methodology is developed for the assignment of electricity use of network charges. Ultimately, to paint a broader picture, this manuscript explores the socio-economic implications of the energy transition through the long-term simulation of access to distributed generation for small-scale participants at a national level. These new propositions are validated using two standard IEEE test networks, two real distribution feeders in the west coast of Ireland, and ultimately the entire interconnected distribution and transmission networks from Ireland. Technologies studied include small-scale rooftop solar PV, wind turbines, battery energy storage systems, voltage regulators and infrastructure upgrades. This work presents novel tools for planners to address the new challenges of modern and future distribution networks.