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This project focuses on developing three technical courses for lower-division electrical engineering education to bridge the gap between Career and Technical Education (CTE) programs in high schools, engineering programs at community colleges, and lower-division electrical engineering courses at four-year universities. The primary goal of the project is to create a seamless academic transition by providing electrical engineering students with the necessary foundational knowledge in analog and digital systems, as well as hands-on experience with laboratory measurement tools. The courses utilize industry-relevant technologies such as LabView, MATLAB, PLC programming, and ready-to-use microcontroller boards to facilitate experiential learning at lower division courses. Early exposure to these tools and systems equips students with practical skills that not only prepare them for further academic pursuits but also align them with workforce demands in industries that increasingly rely on automation, data acquisition, and real-time system controls. The success of this project is attributed to its emphasis on design and project-based learning, which fosters critical thinking and problem-solving skills essential for real-world applications. By integrating design principles early in students' educational experiences, they are better prepared to tackle complex engineering problems as they progress through their academic careers. The use of project-based learning allows students to apply theoretical knowledge to tangible, real-world projects, improving their engagement and deepening their understanding of electrical engineering concepts. Practical tools like MATLAB and microcontroller boards in entry-level courses not only motivates students to pursue engineering but also increases retention rates in STEM fields, a key metric for academic success. This project is also advocating for early exposure to hands-on technical skills as a way to better prepare students for the workforce. By focusing on skill development in both CTE programs and early college courses, students are equipped with a stronger foundation for electrical engineering careers and are more likely to succeed in upper-division coursework and beyond. The seamless integration of high school, community college, and university programs ensures that students acquire both the theoretical and practical skills necessary to be successful in an increasingly technology-driven economy. Moreover, the project's use of industry-standard tools, coupled with its focus on bridging academic gaps, provides a sustainable model for developing a skilled and versatile workforce, addressing the growing need for engineers proficient in both design and system implementation. 

Anomaly analysis is an important component of any surveillance system. In recent years, it has drawn the attention of the computer vision and machine learning communities. In this article, our overarching goal is thus to provide a coherent and systematic review of state-of-the-art techniques and a comprehensive review of the research works in anomaly analysis. We will provide a broad vision of computational models, datasets, metrics, extensive experiments, and what anomaly analysis can do in images and videos. Intensively covering nearly 200 publications, we review (i) anomaly related surveys, (ii) taxonomy for anomaly problems, (iii) the computational models, (iv) the benchmark datasets for studying abnormalities in images and videos, and (v) the performance of state-of-the-art methods in this research problem. In addition, we provide insightful discussions and pave the way for future work. 

In recent years, the need to exploit digitized document data has been increasing. In this paper, we address the problem of parsing digitized Vietnamese paper documents. The digitized Vietnamese documents are mainly in the form of scanned images with diverse layouts and special characters introducing many challenges. To this end, we first collect the UIT-DODV dataset, a novel Vietnamese document image dataset that includes scientific papers in Vietnamese derived from different scientific conferences. We compile both images that were converted from PDF and scanned by a smartphone in addition a physical scanner that poses many new challenges. Additionally, we further leverage the state-of-the-art object detector along with the fused loss function to efficiently parse the Vietnamese paper documents. Extensive experiments conducted on the UIT-DODV dataset provide a comprehensive evaluation and insightful analysis.