Academic and course-based projects involving deep learning, embedded systems, and robotics.
Developed an architecture capable of accurately classifying abnormal brain activity in EEG data using the HMS — Harmful Brain Activity Classification Dataset from Harvard Medical School.
Developed a model capable of accurately predicting an individual's identity based on their voice. Trained using a comprehensive range of fundamental audio features.
Used an FPGA board with a PS2 port to receive keyboard input, converting scan codes to ASCII codes to control servo motors for Braille cell output.
Built a sensor-based system to detect open/closed states of doors and windows. Data is transmitted wirelessly to a central server, with a dedicated mobile app for real-time monitoring and unauthorized access alerts.
Developed a robust mechanical framework with essential electronic elements for stability and control. Movement is precisely controlled using pulse-driven stepper motors without feedback loops.
Developed a wirelessly controlled robotic arm system using Arduino with PID control for precise motion across 6 degrees of freedom.