As an FPGA Engineer at Nomedia, I have been responsible for designing and developing complex hardware solutions using VHDL and Verilog. My role includes:Hardware Description: Proficiently writing and optimizing hardware descriptions in both VHDL and Verilog to meet specific design requirements.Simulation and Testing: Creating and executing comprehensive test benches to verify the functionality and performance of the hardware designs.FPGA Implementation: Implementing designs on Spartan 3AN and Spartan 6 FPGAs, ensuring efficient resource utilization and optimal performance.IDE Proficiency: Expertly utilizing ISE and Modelsim as integrated development environments (IDEs) for design, simulation, and debugging tasks.Project Management: Collaborating with cross-functional teams to deliver high-quality FPGA solutions within project timelines.Troubleshooting and Debugging: Identifying and resolving issues in hardware designs to ensure reliable and robust operation.Throughout my tenure, I have consistently demonstrated a strong ability to solve complex problems, adapt to new challenges, and contribute to the success of various projects. My work has been instrumental in advancing the company's capabilities in FPGA-based solutions.

Founded and manage a YouTube channel focused on Electrical and Computer Engineering education, growing to 475 subscribers.Design and produce comprehensive educational content, including tutorials and demonstrations on VHDL, FPGA design, and image processing techniques.• Engage with a diverse online community, responding to viewer inquiries and feedback, which enhances the learning experience and channel visibility.Utilize multimedia tools and digital marketing strategies to increase viewership and subscriber engagement.Continuously research and stay updated with the latest technological advancements to provide current and valuable content to viewers.

Employed Deep Learning and the HPTI_V4 algorithm to develop a project for detecting diabetic retinopathyTrained models on a dataset and evaluated their performance for accurate diagnosis

In this self-initiated educational project, I developed a system to accurately measure the speed of vehicles on a highway using low-resolution (480p) video footage captured from a pedestrian overpass. The project involved the following key steps:Video Acquisition: Captured video footage of moving vehicles using a low-resolution camera mounted on a pedestrian bridge.Image Processing: Utilized MATLAB for processing the video frames. Applied image processing techniques to analyze pixel color changes over time.Speed Calculation: Implemented algorithms to calculate the speed of vehicles based on the rate of pixel color changes, achieving an accuracy of 87%.Technology Utilization: Leveraged MATLAB for all stages of the project, from initial data acquisition to final speed computation.Outcome: Successfully demonstrated the feasibility of using low-resolution video and image processing techniques to measure vehicle speed with significant accuracy.This project was conducted independently as part of my self-study in image processing through reading relevant books, showcasing my ability to apply theoretical knowledge to practical problems.