Ranjith Dasari

Motivated and innovation-driven engineering student with a strong foundation in embedded systems, AI, and electric vehicle technologies. Passionate about building real-world solutions that enhance safety, performance, and intelligence in modern mobility. Seeking challenging roles where I can apply my skills in microcontroller programming, AI model development, and smart system design to contribute meaningfully to next-generation tech. Eager to grow in a fast-paced environment that values creativity, leadership, and impact.

Motorcycle Racing, Go-karting, Cooking, Adventure Travel, DIY Projects

Smart Bike Helmet System (2024–25) Raspberry Pi Zero W-based system detecting accidents via MPU6050 sensor. Sends GPS location to emergency contacts via GSM module. Helmet includes Bluetooth-based speaker system with PAM amplifier and power management. Integrated with mobile app via WiFi for speed tracking and road limit alerts., Smart Bike Safety App (Android) Android Studio app with real-time speedometer, GPS-based overspeed alert system. Integrates Google Maps API to fetch dynamic road speed limits. Emergency SMS/call trigger upon crash detection using Raspberry Pi’s MPU6050 feedback. Custom animated UI, trip history, emergency contact list, and WiFi messaging to Pi., LLM-Enabled Smart Helmet Assistant (In Progress, 2025) Designing a custom Local Language Model (LLM) trained for voice command processing and task execution. Acts as a Jarvis-style AI assistant integrated with the smart helmet for real-time alerts, navigation, and rider interaction. LLM model optimized for low-latency inference on embedded devices using quantization and distillation techniques., AI Model for Fault Detection in Electric Vehicles (2025) Trained a deep learning model to detect faults in EV subsystems using real-time sensor data (voltage, current, vibration). Achieved accurate classification between hardware faults (motor, battery) and control signal issues. Implemented on microcontroller + cloud hybrid system for edge detection and remote diagnostics., Miniature Autonomous EV with YOLOv5 Object Detection Raspberry Pi 4 with optimized YOLOv5 model for real-time obstacle and sign detection. Achieved 85.6% mAP@0.5 with a custom dataset of 3000+ images. Implemented INT8 quantization and pruning to reduce model latency to 115ms. Power-efficient model (4.8W) suitable for low-power embedded EVs., Solar Tracker Using RTC and ESP32 Automatic solar tracking system from 6 AM to 6 PM. Servo motor adjusted panel position based on RTC module. Boosted solar efficiency using time-driven angle movement without LDRs., Dynamic Overspeed Indicator System (Bike Integration) Real-time overspeed alerts using LED blinking pattern synced with road speed limits. Raspberry Pi Pico WH controls RGB LED via Bluetooth from Pi Zero. Speed thresholds trigger blink patterns (1/sec, 2/sec, 3/sec) for rider awareness., Fire-Aware Pathfinding Algorithm (AI Problem Solving) Developed algorithm to compute safe paths in a 2D grid simulating fire spread. Used BFS/DFS strategies with delay calculation for AI-based escape route planning., Microcontroller Binary Difference System 8051-based assembly program to take two 3-bit binary inputs via switches. Displays difference using LEDs with port-level control and interrupts.

• Research and Development Head – EV Club, DSCE
• Spearheaded microcontroller development in all electric vehicle projects.
• Attended national/international EV conferences & hands-on workshops.
• Designed innovative EV sub-systems and led cross-functional teams.

I hereby declare that the above information is true to the best of my knowledge. Ranjith Kumar Dasari