Paper ID #36738Capstone Project: CPU Design with MultiplexerProf. Yumin Zhang, Southeast Missouri State University Yumin Zhang is a professor in the Department of Engineering and Technology, Southeast Missouri State University. His research interests include semiconductor devices, electronic circuits, neural networks, and engineering education. ©American Society for Engineering Education, 2023 Capstone Project: CPU Design with Multiplexer Anthony F. Di Mauro, Michael C. Hawkins, Bradley K. Lindsey, Yumin Zhang Department of Engineering and
this reason, an amphibious water sampling rover was created by a capstoneproject team. This capstone project team was formed with five undergraduate engineeringtechnology students. This project was started in the Spring semester of 2022 and concluded inthe Fall semester of 2022. This project generated a rover, and the rover can navigate both on landand on water. And it can perform the water sample collection task. The rover can be controlledover a remote PC, and it can collect water temperature data, and the data can be sent remotelyover the internet. For wireless communication, a sub-GHz LoRa module is used. The rover canalso communicate over WiFi. A GUI (Graphical user interface) program was developed tocollect data from the rover and to
between the DoD, Microsoft, and Universities. Radana is currently an associate professor and a Chair of the CS Dept. at Saint Martin’s University.Mr. John L. Whiteman, Saint Martin’s University John L. Whiteman is a Senior Security Engineer for Lam Research in Oregon and a part-time adjunct cybersecurity instructor at Saint Martin’s University. John received a Master of Science in Computer Science from Georgia Tech University. John holds multiple security certifications, including CISSP and CCSP. ©American Society for Engineering Education, 2024 Integrating Cybersecurity in BSCS & BSIT Senior Design Capstone Projects: A Case Study John Whiteman
Electrical Engineering, Computer Engineering,Computer Science, Computational Data Science, and Software Engineering. This paperpresents the progress report of this scholarship program and its impact on the institution, itsComputer Science and Engineering Programs, and the community. Also, it presents the effect ofthe high-impact practices in this program in retention of computer science and engineeringstudents. High-impact practices reported include Capstone Courses, Collaborative Projects,First-Year Experiences, Internships, Undergraduate Research, and Writing Intensive Courses.IntroductionThe National Science Foundation (NSF) established the Scholarships in STEM (S-STEM)program in accordance with the American Competitiveness and Workforce
community college and technical college context. Communitycollege and technical college graduates typically start jobs with less training than bachelor’sdegree holders on average. The capstone experience can also be significantly different. BYOPrepresents the opportunity to add to the student’s portfolio of projects. Smaller class sizeshowever must be balanced against heavy teaching loads for faculty. Developing projectmentorship that enhances both the BYOP students and more advanced students experience maybe one approach. Another approach may be to partner with 4-year colleges and universities. Thevalue of the learning experience is considerable for the project mentors and the exposure toconnections with the 4-year program participants can be
Paper ID #41083Generative-AI Assisted Feedback Provisioning for Project-Based Learning inCS CoursesVenkata Alekhya Kusam, University of Michigan, Dearborn Venkata Alekhya Kusam is currently pursuing a Master’s degree in Computer and Information Science at the University of Michigan-Dearborn. She has always been fascinated by the transformative power of technology. Her research interests lie in generative AI, large language models, and natural language processing (NLP).Larnell Moore, University of Michigan, Dearborn Larnell Moore is an undergraduate student in his final year pursuing a Bachelor’s degree in Computer and
and WSN projects that ourundergraduate computer engineering students have done in their senior capstone course.IntroductionA smart home uses internet-connected devices to enable the remote monitoring and managementof appliances and systems. An efficient and smart home is a ubiquitous computing system thatcontrols any device in the house from anyplace. The field of smart home automation andsecurity is growing rapidly as many new ideas and possibilities are emerging from new advancesin technology. For example, through the voice recognition service, it is possible to control thedevices in the house by voice and remotely control the devices in the home using the individualsmartphone through the remote-control system. As the usage of smart home
utilizecompetencies developed in the first three years of the curriculum in the solution of a complexdesign problem.Educational excellence requires exposing students to the current edge of research. To ensure thatstudent projects are along the same trajectory that the industry is moving, educators mustcontinually introduce emerging techniques, practices, and applications into the curriculum. Thefields of Internet of Things (IoT) and Wireless Sensor Networks (WSN) are growing rapidly, andthere is increasing interest in providing undergraduate students with a foundation in these areas.This paper presents IoT and WSN projects that our undergraduate computer and electricalengineering students have done in their senior capstone course in wildfire
projects. Thisresearch also analyzes how adult learners interactively learn, reflect, and apply their AIknowledge to examples drawn from their workplace, while improving their understanding andreadiness to implement AI technologies effectively.Our three-day workshop centered around enriching and engaging learning about AI technologies,ethics, and leadership, featuring topics like supervised learning and bias, AI strategy, andgenerative AI. Apart from discussions, the workshops incorporated hands-on learning with digitaltools, robots, problem-solving scenarios, and a capstone project. Participants were 44 leadersfrom a large government organization. Their learning was measured through pre- andpost-questionnaires on AI leadership, knowledge checks
Activities for the 27,404 2017 Classroom and Outreach A Comparison of Network Simulation and Emulation 9,760 2016 Virtualization Tools A Taste of Python – Discrete and Fast Fourier Transforms 6,233 2015 Design of a Bluetooth-Enabled Wireless Pulse Oximeter 5,644 2019 Capstone Projects in a Computer Engineering Program Using 5,558 2016 Arduino A Real-time Attendance System Using Deep-learning Face 5,225 2020 Recognition STEM Outreach: Assessing Computational Thinking and 4,288 2017 Problem Solving A Methodology for Automated Facial
Application Administrator at a Mitsubishi Power Systems, where he built state-of-the-art Enterprise and Machine Learning Applications. Academic positions include Adjunct Professor at the University of Bridgeport, CT, and Assistant Professor – Computer Security where he is tenured at the School of Engineering Technology, Farmingdale State College - State University of New York. He has 6 years of higher education experience, and a total of 14 years. He has presented and published numerous conference papers, journal articles and contributed to a book chapter on Large-scale Evolutionary Optimization. He has excelled at going the extra mile, teaching not only his own classes but an additional Capstone projects, doing
].Survey Design and MethodologyThis research project was reviewed and determined to be exempt by our college’s InstitutionalReview Board (IRB). Our experimental setup consisted of two groups of students at a largeMidwestern R1 University, in an undergraduate, pre-capstone SE course. We utilized a quasi-experimental pretest-posttest hybrid between groups and within groups design for this study. Thecontrol and treatment groups consisted of successive cohorts of sophomores/juniors from CS andComputer Engineering, one section each. This SE course was a mandatory component of theiracademic progression towards earning their degree.The treatment group was taught using PI while the control group received instruction throughtraditional lectures. The