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SATHVIKA G NAIK

Electronics and Communication Engineer

Karwar, Karnataka, India

@sathvika_naik

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About

Electronics and Communication Engineering student with project experience in UAV-based disaster management, wireless EV charging, embedded systems, and IoT. Interested in applying programming, hardware integration, and problem-solving skills in a challenging technical role while continuing to learn across diverse domains.

Experience

  • Intern
    Karunadu Technologies Private Limited
    2026 – 2026

    Interned focusing on embedded and IoT applications. Designed and implemented projects involving microcontrollers, sensors, and communication modules. Worked on real-time data processing, system debugging, and hardware integration for project developments.

Education

  • Bachelor of Engineering
    Shri Madhwa Vadiraja Institute of Technology and Management
    Electronics and Communication Engineering (ECE) · 2022 – 2026

Skills

3 to 6 months 3 to 6 months A few months of practice with it.
IoT Sensors Embedded Systems Microcontrollers
<3 months <3 months Just getting started - under three months. This is also what shows when a level has not been set.
C Hindi Python Verilog Kannada English Embedded C

Tools / apps / platforms

<3 months <3 months Just getting started - under three months. This is also what shows when a level has not been set.
Arduino Arduino IDE The MathWorks MATLAB

Languages

Not stated Not stated No level given for this one.
Hindi Speak English Speak Kannada Speak

Projects

  • Prototype of Wireless EV charging Station
    Arduino, resonant inductive coupling

    Built a contactless power transfer system using resonant inductive coupling with embedded monitoring firmware. Developed Arduino-based firmware for real-time monitoring of power metrics, charging efficiency, and system health, applying electromagnetic theory and embedded control logic.

  • UAV based disaster management
    ResNet-50, CNN, RIS

    Designed a decentralized multi-UAV swarm with 3-phase navigation achieving 100% collision-free mission success. Implemented ResNet-50 CNN for disaster classification with 94% accuracy and under 2-second inference across 5 disaster types. Integrated RIS technology to improve communication range and reduce power consumption.

Courses & certifications

  • VLSI Front End Design Verification using System Verilog · MAHE, Manipal
  • Learn C Programming · CodeChef
  • MIT App Inventor · SMVITM
  • MATLAB Onramp · MathWorks

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