Houriya BENZAD

– Built an automated quality control system using computer vision (YOLO) to detect defects on automotive components, improving inspection speed and consistency vs. manual checks. – Trained and optimized deep learning models on GPU, then deployed on Edge AI platforms (Raspberry Pi 5, Hailo-8L) to enable real-time inference in industrial environments. – Led end-to-end hardware/software integration (camera, edge device, embedded system), ensuring reliable real-time performance. – Developed an industrial HMI for operators (visualization, status monitoring, alerts), improving usability on the production line. – Collaborated with engineering and quality teams to align the solution with industrial constraints (latency, reliability, deployment).

– Designed and developed an embedded control system for automated water station management, enabling real-time supervision and monitoring. – Programmed ATmega328P for system control and monitoring tasks; implemented low-level control on PIC16F877A for hardware interfacing. – Developed real-time firmware to manage sensors, actuators, and communication interfaces, improving system reliability and responsiveness. – Validated system behavior through testing and debugging on real hardware.