Alwin Jude

Electrical and Electronic Engineering graduate with a Distinction MSc in Robotics, AI and Autonomous Systems. Experienced in embedded systems, robotics, Python, machine learning and computer vision through academic and practical projects. Strong problem-solving skills with experience developing intelligent systems, analysing engineering data and programming autonomous solutions.

Seeking a graduate engineering role where I can apply my technical knowledge while continuing to develop professionally.

Intelligent Spoon Stabilisation System

Designed an Arduino-based stabilising spoon to reduce the effects of hands tremors experienced by people with Parkinson's disease.

• Integrated gyroscope sensors and developed angle measurements algorithms to detect unwanted movement in real time.
• Calibrated sensor readings to improve balancing accuracy and minimise measurement drift.
• Tested the prototype under different movement conditions, demonstrating improved spoon stability and reliable sensor performance.

Machine Learning Analysis of Video Streaming Quality using PCAP Network Data

Investigated whether machine learning could accurately predict video streaming quality from captured network traffic, reducing the need for manual network analysis.

• Developed a Python pipeline to process PCAP files, engineer network features and prepare datasets for model training.
• Evaluated multiple machine learning models, including LSTM networks, comparing prediction accuracy and generalisation performance.
• Improved prediction accuracy by approximately 12% through feature engineering, preprocessing and model optimisation.

Image Stitching Application

Developed an OpenCV application to automatically combine overlapping images into panoramic images, demonstrating computer vision techniques for image alignment.

• Implemented ORB and AKAZE feature detection algorithms to identify and match image key points.
• Applied feature matching and image transformation techniques to generate seamless panoramic outputs.
• Evaluated both algorithms for stitching accuracy and computational efficiency to determine the most effective approach.

Remote Controlled Vehicle

Designed and programmed an Arduino-based remote-controlled vehicle to demonstrate embedded systems integration and real-time motor control.

• Integrated motor drivers and sensors to achieve responsive and reliable vehicle movement.
• Developed control logic for real-time navigation and hardware communication.
• Debugged and calibrated the electrical and embedded system components, improving control accuracy and overall system reliability.

MSc in Robotics, AI and Autonomous Systems – City, St George's University of London (2024-09 – 2025-09)

BEng in Electrical and Electronic Engineering – City, St George's University of London (2020-09 – 2024-06)