Implemented a U-Net-based model for semantic segmentation in self-driving cars, using Python and TensorFlow to classify road objects and enhance autonomous navigation. Processed real-time video data to segment and detect key features such as lanes, vehicles, and pedestrians, improving object recognition and path planning in self-driving systems.

◦Executed a predictive model using Arduino to estimate the State of Charge (SOC), State of Health (SOH), and predict thermal runaway events for mobile robots.◦ Integrated IoT-enabled battery monitoring and Battery Management Systems (BMS), improving system reliability and efficiency by 10% while enhancing battery health monitoring and failure prediction.

◦Implemented sensor-driven rotor balancing procedures to optimize machine performance, integrating real-time data analysis for improved operational efficiency in line with Industry 4.0 principles.◦ Collaborated with senior engineers to present insights from data analysis, identifying key areas for automation and enhancing process efficiency, earning commendation for innovative contributions to machine optimization.