Designed, developed, and demonstrated a low-cost, proof-of-concept Attitude Determination and Control System (ADCS) for a CubeSat. The project also featured the innovative integration of an embedded Artificial Intelligence (AI) payload for autonomous, real-time onboard image classification, showcasing the feasibility of Edge AI for future small satellite missions.

• Engineered a COTS-based ADCS using a Raspberry Pi 5, IMU (GY-87), and DC motors as reaction wheels.
• Developed control software in Python, implementing a simplified Extended Kalman Filter (EKF) for attitude estimation and a PD controller for stabilization.
• Integrated an AI payload using a TensorFlow Lite (EfficientNetB0) model for on-device land-use classification from a live camera feed.
• Successfully built and tested a 1U CubeSat mock-up, validating the core ADCS functionality and AI-driven image analysis pipeline.

Developed a Patient Digital Card (PDC) system to centralize patients' medical data, providing them with easy access to their information.

Performed maintenance on hardware and software systems, including installation of new software and computers.

Developed a facial recognition-based access control system that manages multiple doors and users. The system allows specific access for individuals to designated doors, logs entry attempts with timestamps and images, and provides a searchable log of access attempts.

• Used python and flutter

Created a mobile marketplace application connecting buyers and sellers, featuring user authentication and real-time data management.

• Used native Android/Java, FireBase and OpenStreetMap

Designed and built a small rover equipped with a camera and sensors, controlled via Wi-Fi. The rover was developed using Raspberry Pi and enabled remote monitoring.

• Used Raspberry Pi, WebRTC and flutter