• Collaborated within a team of 5 to design and construct a slalom kayak race monitoring system
• Programmed in Python on the Raspberry Pi 4B to determine whether the kayaker passes through the gates accurately using Computer Vision
• Utilised the ESP32 and Inertial Measurement Unit (IMU) sensor to detect gate hits with precision
• Implemented an Infrared (IR) Beam Break system to ensure accurate start/stop timing during races
• Developed a sustainable power solution integrating solar panels and rechargeable batteries for continuous operation
• Presented a working prototype at a Trade Show Event, showcasing the system's capabilities and features
• Developed portfolio website in HTML, CSS and JS as well as utilising webflow tools
• Designed multiple versions and considered many different layouts to optimise ease of use while offering maximum functionality
• Made careful design choices that would suit all platforms and devices such as big buttons so users can easily navigate when using mobile devices
• Implemented different animation styles for a visually appealing user interface including light/dark modes
• Currently hosting using Microsoft Azure Cloud Platform along with a custom domain name
• Spent two weeks on site working on creating a graphical user interface for a driver board
• Developed using C# and WinForms to build an open-source printing software with visual feedback making it user-friendly
• Tested drop-watching and image printing using printer hardware which was successful
• Available to download and install via GitHub or a Guided Windows Installer
• Used C++ to develop an application using QT for the Graphical User Interface and VTK for the 3D rendering of the model and enabling the option to view the model in a Virtual Reality environment
• Generated portable installer using CMake which is compatible with Windows and Mac
• Used GitHub for version control and for merging group code
• Utilised GitHub pages for hosting and automating Doxygen documentation.
• The purpose of the project was to create a speed gun capable of measuring the travel speed of electric scooters.
• Designed Filter and Amplifier for signal received from the Doppler Radar
• Processed Signal using STM32 with Fast Fourier Transform to determine the speed from the frequency measured
• STM32 transmitted UART signal to Xilinx CPLD which controls the two 7 segment displays that displays the speed
• Designed and Constructed a Computer Vision Guided Vehicle
• Programmed using Arduino, ESP32 and Raspberry Pi Microcontrollers
• Coded using C++ with OpenCV on the Raspberry Pi for real-time processing of road-signs and line-following using Pi-Camera
• Raspberry Pi also used for Node RED to output real-time statistics of the vehicle and its surroundings- ESP32 Microcontoller used for PWM Motor Control- Arduino Microcontroller used for Line-Following using IR Sensors