• 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
• 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.
• 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