Ali Ahmad

PROFESSIONAL PROFILE Motivated electrical engineer with a strong technical foundation and a passion for innovation. Eager to apply problemsolving skills and technical expertise to challenging projects in electrical and related fields EDUCATION Bachelor of Electrical Engineering School of Electrical Engineering and Computer Science (SEECS) , Islamabad , 3.61 (2026) INTERNSHIP EXPERIENCE ESDAC LAB 20-Jun-2024 - 30-Aug-2024 Deployed Ubuntu Linux on an FPGA-based SoC (DE-series board) and configured the system for embedded operation. Developed user-level applications in an embedded Linux environment for peripheral interaction and system testing. Implemented custom Linux drivers for hardware peripherals, including input devices, switches, and basic graphics control. Interfaced and acquired data from FPGA-based ADC modules and an ADXL345 accelerometer. Implemented and tested basic image processing routines on the embedded platform under system-level constraints Fatima Fertilizer Company Limited 18-Jun-2025 - 25-Jul-2025 Interpreted P&ID and PFD diagrams for process understanding and instrumentation mapping. Worked with industrial instrumentation analyzers for flow, level, temperature, pressure, conductivity, and pH measurement. Configured field devices using HART communicators for parameter setup and diagnostics. Gained exposure to Yokogawa DCS architectures, including system performance and redundancy concepts. ChipXprt 24-Jun-2024 - 30-Aug-2024 Learned Verilog, Vivado and chip design process. Worked on implementing a JTAG port for flashing code onto a RV32IMAC RISC-V core. Learned Zepyhr RTOS and implemented it on a STM32 microcontroller. FINAL YEAR PROJECT Deep Learning–Based Drone Detection and Tracking Designed and deployed a real-time drone detection and tracking pipeline for dynamic outdoor environments using live video streams. Implemented and optimized YOLO-based object detection models under strict real-time constraints, benchmarking multiple architectures to balance accuracy, latency, and embedded GPU efficiency. Integrated a ByteTrack-based multi-object tracking pipeline to ensure consistent target identities and stable tracking across consecutive frames. Deployed, profiled, and optimized the full perception stack on NVIDIA Jetson platforms, transitioning from Jetson Nano to Jetson Orin Nano to significantly improve inference throughput and scalability. Evaluated end-to-end system performance using frame rate, inference latency, and tracking stability metrics to guide model and system-level design trade-offs. Integrated perception outputs with downstream control logic to enable closed-loop visual tracking based on image-plane error, moving beyond standalone detection. Contributed to a modular, extensible system architecture with emphasis on end-to-end latency, robustness, and deployment stability. TECHNICAL EXPERTISE

Motivated electrical engineer with a strong technical foundation and a passion for innovation. Eager to apply problemsolving skills and technical expertise to challenging projects in electrical and related fields