Object recognition presents considerable difficulties within the domain of computer vision. Field-Programmable Gate Arrays (FPGAs) offer a flexible hardware platform, having exceptional computing capabilities due to their adaptable topologies, enabling highly parallel, high-performance, and diverse operations that allow for customized reconfiguration of integrated circuits to enhance the effectiveness of object detection accelerators. However, there is a scarcity of assessments that offer a comprehensive analysis of FPGA-based object detection accelerators, and there is currently no comprehensive framework to enable object detection specifically tailored to the unique characteristics of FPGA technology. The You Only Look Once (YOLO) algorithm is an innovative method that combines speed and accuracy in object detection. This study implemented the YOLOv5 algorithm on a Xilinx® Zynq-7000 System on a Chip (SoC) to perform real-time object detection. Using the MS-COCO dataset, the proposed study showed an improvement in resource utilization with approximately 42 thousand (78%) look-up tables, 56 thousand (52%) flip-flops, 65 (46%) BRAMs, and 19 (9%) DSPs at a frequency of 250 MHz, improving the effectiveness compared to previous simulated results.

Optical Engineering (OE) publishes peer-reviewed papers reporting on research, development, and applications of optics, photonics, and imaging science and engineering.

Accurate and efficient learning outcome assessment is crucial for ensuring high-quality education, but traditional methods can be time-consuming, error-prone, and inconsistent. We developed a novel Excel Macro-enabled framework for automating the evaluation of Course Learning Outcomes (CLOs) and Program Learning Outcomes (PLOs) in higher education. The framework consists of two Excel Macro-enabled workbooks. The course section workbook guides instructors through the assessment process, automatically calculates CLO achievement levels, and generates reports for the coordinators and the Head of Department (HoD). The course-level workbook aggregates data from all course sections and calculates CLO and PLO achievement levels relative to the course. Proven successful in three FCIT (Faculty of Computer and Information Technology) programs at NBU (Northern Border University), the framework demonstrably reduces assessment time and errors, improves consistency, and facilitates data-driven program improvement, making it a valuable tool for enhancing program quality.