SERIGNE SALIOU MBACKE DIOP

Development of a real-time embedded vision system for long-range beacon tracking in port environments -Designed and deployed a full embedded vision pipeline (image acquisition → detection → angular estimation → communication) on Raspberry Pi Compute Module 5 -Conducted camera characterization and experimental validation to ensure compliance with system requirements: -analysis of sensor behavior (noise, jitter, photometric stability) -calibration of intrinsic parameters (pixel size, field of view, focal length) -evaluation of acquisition parameters (exposure, gain, HDR, frame rate) -definition and execution of rigorous experimental protocols (lab + outdoor) -validation of system performance against specifications (detection range, robustness) -Led the selection and integration of an industrial camera system, improving detection capability from 40 m to 70 m based on quantitative experimental analysis -Developed robust LED beacon detection algorithms combining: -geometric validation -connected components analysis -a contrario statistical filtering for false alarm reduction -Implemented real-time angular position estimation (φ/θ) using geometric modeling (pinhole camera model) -Achieved real-time performance (20 FPS) with reliable detection up to 70 meters in outdoor conditions -Designed and implemented TCP/IP communication architecture for integration with industrial maintenance software -Set up cross-compilation toolchain for efficient embedded deployment (ARM target) -Conducted extensive experimental validation, including performance benchmarking and comparison with industrial reference sensors -Ensured software reliability through unit testing (GoogleTest) and detailed technical documentation

Development and Optimization of CPU/GPU Algorithms for Autonomous Navigation Based on ROS 2 for a Mobile Robot: - Implementation of SLAM on NVIDIA GPU - Path Planning - Testing and Evaluation

Vehicle Detection and Distance Estimation Project Using a Stereoscopic Camera with HDR Processing: - HDR image processing with OpenCV and Matlab/Simulink and vehicle detection using YOLOv4 - Disparity map computation and depth estimation of detected vehicles with OpenCV

I recently completed a one-month internship at the Société Africaine de Raffinage (SAR) as a Systems Engineer. I worked on sensors and the digital control system. I had the opportunity to collaborate with experienced engineers and develop my skills in automation, gaining hands-on experience with PLCs for industrial process control and configuring sensors and actuators, mainly solenoid valves. This internship was an incredibly rewarding experience, and I look forward to applying what I have learned in my future projects.