DCU-Final-Year-Projects-Booklet-2025

33 3. UsingMMwave to Prevent Jaywalking Accidents Jaywalking is amajor cause of pedestrian accidents, often due to poor visibility, driver inattention, or unpredictable movement. This project leverages millimeter-wave (mmWave) sensors to detect and prevent jaywalking accidents by providing real-time alerts. The mmWave technology operates in the 30–300 GHz range, enabling accurate pedestrian tracking regardless of lighting or weather conditions. The system uses radar-based sensing to detect motion, analyse pedestrian behaviour, and identify jaywalkers. Machine learning algorithms process Doppler shifts, range, and angle-of-arrival (AoA) data to differentiate between normal and risky movement patterns. When jaywalking is detected, alerts can be triggered through audible warnings, LED signals, or vehicle communication via V2X technology. Student Programme Electronic and Computer Engineering (Year 5) Project Area Embedded Systems, Internet of Things, Sensor Data, Sensor Technology, Software Development Project Technology C/C++ Student Name(s) Sina Tavakoli Email sina.tavakolifarimani2@mail.dcu.ie Supervisor Prof DerekMolloy 4. Levelised Cost of Energy Assessment of Static vs Dynamic Solar PV Systems This project investigates the performance of static and dynamic solar panel orientations. It compares three different configurations, one static and two dynamic, to determine which is the most energy-efficient. Student Programme Mechanical and Sustainability Engineering (Year 4) Project Area 3-DModelling, Arduino, Automotive Technology, Control Systems, Energy Conservation, Mechanical Design andManufacture, Life Cycle Assessment Project Technology C/C++, Excel/VB, Solidworks Student Name(s) AdamO’Connor Email adamoconnor190@gmail.com Supervisor Dr GregMcNamara 5. Real-Time Pitch Correction in Audio Using Python This project develops a real-time pitch correction application in Python, using time-domain techniques for accurate pitch detection and shifting. The system features a CustomTkinter GUI and processes live audio with the sounddevice library. Pitch detection is based on the cumulative mean normalised difference function (CMNDF), combined with autocorrelation (ACF) and the difference function (DF) for improved accuracy. Pitch shifting employs resampling and circular buffering, ensuringminimal distortion while preserving the audio length. Supporting both live and file-based processing, the application provides real-time waveform visualisation. Designed for musicians and audio engineers, it offers an intuitive and efficient solution for real-time autotuning. Student Programme Electronic and Computer Engineering (Year 4) Project Area Digital Signal Processing, Multimedia, Software Development Project Technology Python Student Name(s) Raj Yendamuri Email raj.yendamuri2@mail.dcu.ie Supervisor Dr Martin Collier