Embedded | Firmware | Electrical | Robotics
Hello, I'm Manoj Kumar Selvaraj – a passionate Embedded Systems Developer and Robotics Engineer based in Maryland, USA. With an M.Eng. in Robotics from the University of Maryland and a B.E. in E.C.E from PSG Institute of Technology, I work across the full embedded stack - from hardware design, sensor fusion, and board-level bring-up to firmware development, RTOS integration, edge AI optimization, and FPGA acceleration. I enjoy designing robust hardware–software systems, validating high-reliability platforms, and building intelligent wearable and edge devices that operate with precision in real-time.
Masters in Robotics
B.E. in Electronics and Communication Engineering
Lead Embedded & Firmware Development Intern
Senior Software Engineer
Project Trainee
Technologies: Sensor Fusion, OpenCV, Real-Time Control
Timeline: Feb 2025 - May 2025
An embedded control system was developed to drive an autonomous mobile robot capable of navigating a structured arena, detecting color-coded blocks, and completing a full pick-and-place mission.
Technologies: Wearable IMU, TFLite, TinyML
Timeline: Oct 2025 - Dec 2025
A real-time, on-device gait prediction system that processes multi-axis IMU signals and runs fully quantized neural networks on ultra-low-power microcontrollers.
Technologies: Mixed Signal Design, Analog Circuits, Cadence
Timeline: Oct 2025 - Dec 2025
Designed a 5.2kb SRAM-based AI accelerator with 80-bit parallel throughput, implementing custom control logic and a fixed-point data pipeline. Verified system performance in Cadence, achieving 90.9% classification accuracy on MNIST.
Technologies: CNNs, TFLite Micro, Embedded AI
Timeline: Sep 2025 - Oct 2025
An embedded deep-learning pipeline for real-time speech command recognition, featuring quantized CNN deployment on a Cortex-M4F microcontroller.
Technologies: ROS2, OpenCV, YOLO
Timeline: Apr 2024 - May 2024
Integrated an autonomous system enabling path following, real-time stop sign detection, and dynamic obstacle detection using computer vision and machine learning algorithms.
Technologies: BLE, Android App, Firebase
Timeline: Jan 2021 - Jun 2021
Led a team of four to develop an indoor Bluetooth-based localization system to detect empty parking spots and navigate users to available spaces within a building.
Technologies: Embedded C, MQTT, Sensors
Timeline: Jan 2019 - Feb 2019
A microcontroller-driven smart energy meter that performs on-device signal acquisition, power computation, and cloud reporting through a WiFi-enabled firmware stack.
Technologies: Q-Learning, DQN, Python
Timeline: Feb 2024 - May 2024
Implemented Q-Learning and Deep Q-Learning for dynamic obstacle avoidance using ray-sensor perception and reinforcement learning.
Technologies: RRT, RRT-Connect
Timeline: Jan 2024 - Apr 2024
A comparative study of RRT, RRT-Connect, and Improved RRT-Connect for robotic path planning, evaluating execution time, path quality, and overall efficiency.
Convened a two-day workshop on "Embedded systems - PIC Microcontroller" at PSG iTech, engaging 100+ students.
Published a paper titled "Intelligent Parking for ADAS" at NC2SD 2021.
Won Best Project Award for "Smart Energy Meter".
Feel free to reach out.