Rajdeep Barik
CONTACT
CONTACT
PERSONAL DETAILS
Languages Known: English, Bengali, Hindi
Hobbies
-
Lyricist & Composer -
Football
Movies-
Travel -
Guitar
Books
Event Management
Gaming
Video Editing (DaVinci)
Photography
Short Film Making
Cycling
Rajdeep Barik
Profile
Enthusiastic and forward-thinking engineering student with a passion for innovation and a strong problem-solving mentality. I dedicated to harnessing my academic knowledge and practical skills to contribute meaningfully to the field I will work.
Skills
CODING
- C|Python|HTML|CSS|JavaScript|SQL|Excel
TOOLS
- AutoCAD|Canava|MATLAB|DaVinci Resolve Studio|Git
CERTIFICATIONS2021-2025
Education
- 2018-2019
10th GRADE 85% - 2020-2021
12th GRADE 84% - 2021-2025
B.Tech - Electronics & Communication Engineering St. Thomas’ College of Engineering & Technology Maulana Abdul Kalam Azad University GPA 6.45 (Up to 7th Sem) 1 Active Backlog
Projects & Research
Smart Environmental Monitoring System: Group project consisting of both Hardware and Software (ARDUINO IDE) for creating a Smart Environmental Monitoring system (GRS 1.0) to early detection of smoke and temperature changes for protective action and quick access to live images for instant assessment. (Paper Accepted by IIC ~ Under Review).
WeatherView Website: Projectconsisting of Software (HTML, CSS, JavaScript) for creating a WeatherView Website.
Home Security : The Face Security System is a cutting-edge, Python-based solution designed to provide an additional layer of security and authentication. This innovative system utilizes facial recognition technology to identify and verify individuals, ensuring that only authorized personnel can access secure areas or sensitive information.
Autonomous Driving with Navigation using Google Maps : The Autonomous Driving with Navigation using Google Maps project combines computer vision with Google Maps API for self-driving capabilities. Built using Raspberry Pi and various sensors, the system features object detection, lane tracking, and real-time navigation. I designed the complete hardware setup and user interface. The project implements deep learning models for traffic sign recognition and obstacle avoidance, demonstrating practical autonomous navigation solutions. (Research paper to be published by IEEE)
