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Hello, I'm

Giresh
Branav

Embedded Systems Engineer

Engineering student passionate about building smart hardware solutions with ESP32, STM32, and IoT technologies.

Giresh Branav
Embedded C
IoT & Hardware
Python

About Me

A little more about who I am

Who I Am

I'm a B.E. Electronics & Communication Engineering student at KPR Institute of Engineering and Technology, Coimbatore. I'm passionate about embedded systems, IoT, and building things that bridge the gap between hardware and software.

Education

B.E. - ECE SRM Valliammai Engineering College Jan 2022 - Present
HSC Akshaya Academy Hr Sec School 2021 - 2022
SSC Akshaya Academy Hr Sec School 2019 - 2020
Giresh casual
Always exploring

Tech Stack

Embedded C Python MATLAB Arduino ESP32 STM32 Raspberry Pi FreeRTOS KiCad EasyEDA MQTT BLE Git Linux

Experience

Embedded Systems Intern Pantech eLearning Pvt Ltd Aug 2024 - Sep 2024

Developed embedded solutions using Arduino and ESP32. Worked on sensor integrations and IoT communication protocols.

Giresh at the seaside
Chasing horizons

Achievements

  • Smart India Hackathon 2024 - Finalist (Top 5 nationally)
  • IEEE Paper Published - Smart Helmet for Accident Detection
  • NPTEL Certified - IoT, Embedded Systems Design
  • IIT Bombay Workshop - Arduino & Embedded Systems

Building the future, one microcontroller at a time.

Projects

Things I've built

ESP32 CH32V003 Embedded C/C++ Custom PCB

No Net – Offline Embedded Monitoring System

A decentralized, internet-free monitoring system designed for hostile and remote environments. Features fault-tolerant SD-based "black box" logging to preserve data during power loss, low-power sleep/wake strategies for battery-powered deployment, and a custom-designed PCB with field-reliable enclosure. Delivers 80–90% of commercial system functionality at under 10% of the cost.

Giresh at college
In my element
ESP32-S3 ESP32-CAM mmWave Radar TF Lite Micro

Resource-Efficient Face Verification for Edge Devices

An edge-based face verification system using mmWave radar-triggered vision. A 24 GHz mmWave sensor detects human presence, ESP32-CAM captures and preprocesses face images, and ESP32-S3 runs a knowledge-distilled MobileFaceNet model with INT8 quantization for on-device inference. Achieved 4.07x model compression, ~345 ms end-to-end latency, and 99.35% accuracy on LFW — fully cloud-independent with zero privacy compromise.

By The Numbers

0 Projects
0 Certifications
0 IEEE Paper
0 Internship

Let's Connect

Feel free to reach out

GitHub

@Giresh05

LinkedIn

giresh-branav

Email

s.s.gireshbranav@gmail.com

Phone

+91 8428426215