TAWFIQUE UR RAHAMAN
EMBEDDED SYSTEMSPROFILE SUMMARY
Detail-oriented Electronics Communication Engineering undergraduate with a focus on embedded systems and practical tech applications. Experienced in building functional IoT solutions, including an AI-powered smart home automation system, smart waste management, and accident detection using supervised learning. Dedicated to creating scalable technology with a strong emphasis on documentation and team collaboration.
SKILLS
TYPICAL APPLICATIONS (PROJECTS)
> VISTA — AI Powered Smart Home Automation
[OPEN PROJECT]LANG: PYTHON, C++(ARDUINO) // HW: ARDUINO // PROTOCOL: JSON SERIAL, SSE
Vista is an intelligent home-automation platform that integrates Arduino-based sensor networks with a Python machine-learning engine to deliver adaptive, privacy-preserving automation. The system processes real-time environmental data (temperature, humidity, motion, fire detection) using a custom CAN-style JSON serial protocol and makes autonomous control decisions for fans, lights, and safety systems based on a calculated Discomfort Index, event triggers, and ML-driven voice intent detection. A local Flask web dashboard provides live monitoring, manual overrides, and system diagnostics. The voice system supports wake-word detection, rule-based NLP, and online learning to continually improve user interaction. The entire platform runs offline, ensuring low latency, data privacy, and full edge autonomy. Vista showcases end-to-end engineering: embedded hardware design, serial communication, ML workflow, real-time control, system architecture, and UI/UX for IoT dashboards.
> Accident Detection Using AI (YOLOv8)
[OPEN PROJECT]Python, YOLOv8, Deep Learning
This project implements a real-time accident detection system using YOLOv8, trained to recognize collisions, falls, and hazardous events from video streams with high accuracy and minimal latency. The pipeline includes dataset preparation, class balancing, augmentation, and custom training for improved detection across varying lighting and traffic conditions. The system processes live CCTV or dash-cam footage, performs frame-level inference, and triggers automated alerts through a Python backend whenever an accident is detected. The solution is optimized for edge deployment, supports ONNX export, and maintains stable performance on low-resource hardware. This project demonstrates strong applied skills in deep learning, computer vision, dataset engineering, model optimization, and real-time video analytics.
> IoT Based Waste Management System
[OPEN PROJECT]C++ (ARDUINO) // HW: ARDUINO, ESP8266
An IoT-based waste management system that uses ultrasonic sensing and wireless alerts to monitor garbage levels in real time. The system detects bin fill-status, sends notifications for timely collection, and helps optimize municipal waste routes. It demonstrates practical skills in embedded systems, sensor integration, and IoT communication.