DCU-Final-Year-Projects-Booklet-2025

59 81. Continuous Blood Pressure Estimation fromPPG Signals UsingNeural Networks This project focuses on developing a non-invasive and continuous blood pressure estimation system using photoplethysmography (PPG) signals and neural networks. It builds upon Eoin Brophy’s federated learning framework for BP estimation, evaluating its methodology while integratingmodern deep learning techniques to improve accuracy and generalisation. The project involves real-world validation through collaboration with St. James’s Hospital, where patient data will be collected to test the model’s performance in a clinical setting. By enhancing existing techniques and validating themon real-world data, this work contributes to the development of accurate, scalable, and practical blood pressure monitoring solutions for wearable healthcare and remote patient monitoring. Student Programme Electronic and Computer Engineering (Year 4) Project Area Artificial Intelligence, Biomedical Engineering, Data Analytics, Digital Signal Processing, Sensor Data, Sensor Technology, Wearable_Technology Project Technology Python, Machine Learning Student Name(s) Lucas Mandolesi Email lucas.mandolesi2@mail.dcu.ie Supervisor Dr Shirley Coyle 82. Circuitry and AI for Design of a Breath Sensor This project explores the design of noise-free circuitry to integrate a printed breath sensor for non- invasive disease detection. The system focuses on sensing volatile organic compounds (VOCs), such as acetone, to diagnose conditions like diabetes. Advanced circuitry, including aWheatstone bridge and an Arduino Unomicrocontroller, ensures precise data acquisition. Deep learning andmachine learning techniques, such as Multi-Linear Regression (MLR), are employed to predict electrode properties like conductivity based on electrode images. Inkjet-printed sensors are utilised for cost-effective and scalable development. The integration of Python programming enables efficient signal processing, creating a reliable platform for portable, real-time diagnostics. Student Programme Biomedical Engineering (Year 5) Project Area Arduino, Biomedical Engineering, Circuit Modeling, Sensor Data Project Technology Python, Machine Learning, PSpice Student Name(s) Anurag Kannujiya Email Anurag.kannujiya2@mail.dcu.ie Supervisor Prof Dermot Brabazon 83. Enhancing Residential Energy Efficiency Through Solar Thermal Heating System Integratedwith Latent Thermal Storage This project evaluates the energy and economic performance of residential heating in Ireland, by integrating solar thermal heating systems with latent thermal storage. It will explore how building envelope improvements, such as enhanced insulation, can further reduce space heating loads when using solar thermal systems. The project analyses the potential for integrating these systems into residential heating, considering factors such as storage capacity, cost-efficiency, and potential energy savings. A residential simulation model was developed using DesignBuilder software, equipped with an integrated solar thermal and latent thermal storage system. This model conducts energy efficiency analysis and assesses the system’s ability tomeet heating demands in various scenarios. Student Programme Mechanical andManufacturing Engineering (Year 5) Project Area Energy Conservation, Renewable Energy Technology, Simulation Project Technology Excel/VB, DesignBuilder/ EnergyPlus Student Name(s) Kate Black Email kate.black4@mail.dcu.ie Supervisor Dr Mohammad Saffari