AC 2007-2711: TEACHING HARDWARE DESIGN OF FIXED-POINT DIGITALSIGNAL PROCESSING SYSTEMSDavid Anderson, Georgia Institute of TechnologyTyson Hall, Southern Adventist University Page 12.1360.1© American Society for Engineering Education, 2007 Session: 2711 Teaching Hardware Design of Fixed-Point Digital Signal Processing Systems David V. Anderson1 and Tyson S. Hall2 1 Georgia Institute of Technology, Atlanta, GA 30332–0250, dva@ece.gatech.edu2 Southern Adventist University, Collegedale, TN 37315–0370, tyson@southern.edu
Claudio da Rocha Brito, Melany M. Ciampi, Hilda dos S. Alves COPEC – Council of Researches in Education and SciencesAbstractThe real challenge for all the Engineering Schools lately is to form the professional to act in thenew work market. Nevertheless many Institutions have been searching hard for the best way todo so. Some of them have promoted new kind of curriculum more flexible and more adequate tothe new student. One question remains: How to prepare the engineer for professional life? Forsome it is the internship that will provide the student the taste of what is to be an engineer. InCivil Engineer, the best way is also the internship at the building site if the choice of the studentis to make constructions. For Civil
to work in Benin, religious/spiritual motivations, or other motivations? 9 Thinking back on when you applied for this program, how would you characterize your primary objective(s) for becoming involved? (e.g., you wanted to determine your own interest in pursuing a research career, you wanted to determine your own interest in a future career in development, you wanted to expand your life experience to include living in a developing country, or other objectives . . .). 10 Do you believe that your objective(s) was(were) met? (Mote that this does NOT necessarily mean that you obtained the experience you anticipated. For example, if your objective was to determine your own interest in
El n er o e Mo ctri w ssi to c Po mi rs Design s Dynamics an Statistics Tr Transportation Research Vibrations & Resonance Materials
graphs for fin efficiencies, transient temperature distribution charts for heattransfer in slabs, cylinders, or spheres (Heisler Charts), and radiation shape (view) factor charts.In the early 1970’s calculators replaced slide rules as the basic computational tool for solvingengineering problems. A few years later programmable calculators were available. Modulescontaining basic solutions to heat transfer problems were developed for these calculators.Authors included sections in their textbooks to introduce students to numerical techniques forsolving heat transfer problems.The computer application software for solving engineering problems has also changed. Prior tothe introduction of personal computers (PCs) in the early 1980’s, complex computer
combinedspectrum. The power spectrum increases in accuracy as the number of data points per FFT Proceedings of the 2003 ASEE Gulf Southwestern Annual Conference The University of Texas at Arlington Copyright©2003, American Society for Engineering EducationFigure 1. Example of a Power Spectral Distribution Plotincreases. The software used to process the data, provided with the TSI IFA 300 Anemometer,had a maximum block size of 256K.The PSD was also used to determine the correct choice of a low-pass filter. From looking at theplot, choosing a filter between 10 kHz and 50 kHz would satisfactorily capture the requiredsignal information for a velocity of 15 m/s. The software program
. The window is a 5 mm thick anti-reflective (AR)coated piece of fused-silica 50 mm in diameter with a flatness of 1/10 λ. Light reflects off the die,passes through the beam-splitter a second time, and then enters the objective and imaging lensbefore coming to the CCD camera, which is used to record the data. The objective and imaginglenses act as a compound microscope. The interferometric image created by the optical setupprovides a known distance of 316.4 nm between two consecutive fringes. The interferometric datais used to determine the crack length, ‘s’ of a beam. CCD Camera Vacuum
AC 2009-815: USE OF THE KNOWLEDGE AND SKILL BUILDER (KSB)FORMAT IN A SENIOR MECHANICAL ENGINEERING LABORATORYCharles Forsberg, Hofstra University Charles H. Forsberg is an Associate Professor of Engineering at Hofstra University, where he teaches courses in computer programming and the thermal/fluids area of mechanical engineering. He received a B. S. in Mechanical Engineering from the Polytechnic Institute of Brooklyn (now Polytechnic Institute of NYU), and an M. S. in Mechanical Engineering and Ph. D. from Columbia University. He is a Licensed Professional Engineer in New York State. Page
EngineeringEducation: A Modern Approach,” Proceedings of the American Society of Engineering Educators Conference, St.Louis, Missouri, June 2000.3 Dyke, S.J., Truman, K.Z., and Gould, P.L. (2000), “Current Directions in Earthquake Engineering Education: TheUniversity Consortium on Instructional Shake Tables,” Proceedings of the American Society of EngineeringEducators Conference, St. Louis, Missouri, June 2000.4 Williams, A., PE Exam Preparation: Civil & Structural Engineering Seismic Design of Buildings and Bridges.Kaplan AEC Education, Chicago, IL, 2007.5 Hiner, S. T., Seismic Design Review Workbook: for Californian Civil P.E. Examination. Can be obtained fromwww.seismicreview.com.6 ASCE, ASCE 7 Standard: Minimum Design Loads for Buildings and Other
). Talking about leaving: Why undergraduates leave the sciences. Boulder, CO: Westview Press.10. Tobias, S. (1990). They're Not Dumb, They're Different: Stalking the Second Tier : Tucson, AZ:Research Corporation11. Bean, J.P. (2005). Nine themes of college student retention. In A. Seidman. (Ed.),. (2005). College student retention. Formula for student success (pp. 215-244). American Council on Education CT:Praeger.12. Pascarella, E., & Terrenzini, P. (1991). How college affects students: Findings and insights from twenty years of research. San Francisco: Jossey-Bass.13. Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition. (2nd Ed.) Chicago: University of Chicago Press.14
confidence using theseinventories, although efforts should be made to improve the reliability. Page 14.1260.5 Table 1 Statistics for Concept Inventories Class No. Mean Score Standard Reliability Std Error Students Deviation Coefficient, of Meas. Score % alpha Thermo S 06 116 21.0 65 4.2 0.69 2.3 F 05 110 19.4 61 3.9 0.66 2.3 Fluids S 06 114 14.9 50 4.1 0.69 2.3 F 05
the course being taught? (level of blend, delivery mode, teaching style, …) • And most importantly, Why? Why use the technology or the tool? Why teach the course? Why select this specific objective? … Trying to answer the “why?” of everything is often the most effective, albeit challenging, tool in course design.At the heart of the constructivist instructional design is the instructor, the “i" in our formula. Ourresearch showed us that s/he is the most critical, integral part of effective instruction, just as thestudent is the most critical, integral part of constructivist instruction. The instructors mustconsider their teaching style29 and teaching methods, and as they do, map them against theirstudents’ learning styles.Create a design
substrate-enzyme reaction can be expressed as S + E ←⎯→ k1 ES (1) k2 ES ⎯⎯→ k3 P+E (2) S = Substrate E = Enzyme ES = Enzyme-Substrate Complex P = ProductThe Michaelis-Menten approach was used to derive the rate equation. For this approach, it isassumed that the product-releasing step is much slower than the reversible reaction. Thereversible reaction involves the formation of an enzyme-substrate complex, which is based on avery weak interaction
students’ design performance. Although there arenumerous existing studies that investigate how metacognition impacts performance, similarstudies may focus on the investigation about how each of the components of the self-appraisaland self-management from various groups of engineering students relate to design performance.A standard method of assessing students’ design performance needs to be formulated to increasethe validity of the data.References1. Chan, L. K. S., and Moore, P. J. 2006. Development of attributional beliefs and strategic knowledge in years 5 to 9: A longitudinal analysis. Educational Psychology 26(2): 161-185.2. Graves, D. H. 1983. Writing: Teachers and children at work. Portsmouth, NH: Heinemann Educational Books.3
. Page 14.1111.2Here we should briefly note that there are several different definitions of multidisciplinaryresearch [4], [5], [6]. The terms multidisciplinary and interdisciplinary are often usedinterchangeably, but Borrego & Newswander [3] have provided an excellent discussion of theseterms in the context of engineering education research. They define multidisciplinarycollaborations as those where “collaborators come together to work on a problem, each bringinghis or her own expertise and unique contribution. There is limited exchange of information inthis approach … collaborators leave the project without having learned much about the otherdiscipline(s)” (p.124). On the other hand interdisciplinary collaborations are defined as
Engineering Students’ Communication, Teamwork, and Leadership Skills, vol. 57, no. 3. Springer Netherlands, 2016.[5] B. A. Burt, D. D. Carpenter, C. J. Finelli, and T. S. Harding, “Outcomes of engaging engineering undergraduates in co-curricular experiences.”[6] L. C. Strauss and P. T. Terenzini, “The Effects of Students’ In- and Out-of-Class Experiences on their Analytical and Group Skills: A Study of Engineering Education,” Res. High. Educ., vol. 48, no. 8, pp. 967–992, Dec. 2007.[7] A. L. Miller, L. M. Rocconi, and A. D. Dumford, “Focus on the finish line: does high- impact practice participation influence career plans and early job attainment?,” High. Educ., vol. 75, no. 3, pp. 489–506, 2018.[8] S
79 16 M Private R2 INTRO 140 17 F Private M1 INTRO 123* Carnegie classifications: R1 = Doctoral Universities: Highest Research Activity; R2 = Doctoral Universities: Higher Research Activity; M1 = Master's Colleges and Universities: Larger Programs; M3 = Master's Colleges and Universities: Smaller Programs; B-A/S = Baccalaureate Colleges: Arts & Sciences Focus; and B-DIV = Baccalaureate Colleges: Diverse Fields** Course disciplines: CBME = Chemical/Biomedical Engineering; CIVIL = Civil and Environmental Engineering; DESIGN = Design; EECS = Electrical Engineering/Computer
Paper ID #19425Professional Licensure: The Core of the Civil Engineering Body of Knowl-edgeDr. Matthew Swenty P.E., Virginia Military Institute Matthew (Matt) Swenty obtained his Bachelors and Masters degrees in Civil Engineering from Missouri S&T, worked as a bridge designer at the Missouri Department of Transportation, then returned to school to obtain his Ph.D. in Civil Engineering at Virginia Tech. He worked at the Turner-Fairbank Highway Research Center in McClean, Virginia on accelerated bridge and concrete bridge research before coming to the Virginia Military Institute (VMI). He teaches engineering mechanics
larger mixed methods study that will inform policies for women faculty in engineering. Acknowledgement This material is based in part upon work supported by the National Science Foundationunder Grant Numbers 1535456 and 1712618. Any opinions, findings, and conclusions orrecommendations expressed in this material are those of the author(s) and do not necessarilyreflect the views of the National Science Foundation. References1. Bilimoria, D., Joy, S. & Liang, X. Breaking barriers and creating inclusiveness: Lessons of organizational transformation to advance women faculty in academic science and engineering. Hum. Resour. Manage. 47, 423–441 (2008
-cost setup of an FMS educational platform has the potential of achieving variousobjectives, which include teaching the fundamental concepts and applications of roboticsand automation in FMS, enabling students to participate in hands-on innovative laboratoryexercises, and exposing students to the innovative methodologies in FMS.7 References[1] Hu, S. J., Ko, J., Weyand, L., ElMaraghy, H. A., Lien, T. K., Koren, Y., Bley, H., Chryssolouris, G., Nasr, N. and Shpitalni, M., 2011. “Assembly System Design and Operations for Product Variety.” CIRP Annals-Manufacturing Technology, 60(2), 15-733.[2] Makris, S., Michalos, G., Eytan, A., and Chryssolouris, G. 2012. “Cooperating Robots for Reconfigurable Assembly Operations
forintegrated STEM education in early childhood classrooms. Moreover, we will gather data oninteractions among members of the interdisciplinary design teams, and subject these data todiscourse analysis in order to observe the types and nature of interaction among ECE and ENGstudents. These data will be used to triangulate the results of the analysis of survey data and willsupport revisions and enhancements to learning opportunities afforded to students in futureofferings of these courses. References Bailey, R. (2007). Effects of industrial experience and coursework during sophomore andjunior years on student learning if engineering design. Journal of Mechanical Design, 129(4),662-667. Brophy, S
detailed engineering drawings, animations, and photorealisticrenderings. Typically, each week (two class periods) consisted of the following: Period 1: o Instructor presented the lesson(s) overview PowerPoint to the students. o Students were assigned to complete the lesson(s). The instructor emphasized following the guide with great detail and accuracy. Period 2: o Class reviewed the previous lesson(s). Instructor displayed and discussed modeling practices/procedures for the corresponding models provided in the lesson(s). o Instructor administered an online quiz in regard to that weeks’ lesson(s) o Students were assigned to complete the lesson(s
class, did not have any work experience.These numbers are important to show why it is essential to design the class with a focus onpractical applications, and structure the assignments with this focus, which we will discuss inmore detail below. Table 1 Learning Outcomes Covered by Each Assignment/Activity Targeted Learning Assignment/Activity Description Outcome(s) Case studies, quantitative problems, conceptual short Homework
(CAM), and Computer Aided Engineering (CAE) [1]. The riseof digital manufacturing and the reliance on these technologies to reduce development timewhile improving product design and quality has been exponentially increasing over the past fewyear [2]. The reliance on digital manufacturing by industry has grown as high performancecomputing technology evolves. Recognizing the far reaching implications of this technology onresearch, several government programs in the 1980’s and 1990’s promoted the growth of highperformance computing. Today, programs such as XSEDE promote the use of high performancecomputing to conduct research in multiple fields such as engineering by supporting scholars andresearchers in using these computational resources [3
Teaching Innovation Professorship. The authors would like tothank the students for their feedback. This study complied with the University of Toronto’spolicies on research ethics.7.0 References[1] K. Barns , R. C. Marateo, and S. P. Ferris, “Teaching and Learning with the Net Generation,”Innovate: Journal of Online Education, vol. 3, no. 4, April 2007.[2] M. Prensky, “Digital Natives, Digital Immigrants,” On the Horizon, vol. 9, no. 5, pp. 67–85,Oct. 2001.[3] B. Mitra, J. Lewin-Jones, H. Barrett & S. Williamson, ‘The use of video to enable deeplearning”, Research in Post-compulsory Education, vol. 14, no. 4, pp. 405- 414, July 2010[4] A. Clifton, and C. Mann, “Can YouTube enhance student nurse learning”, Nurse EducationToday, vol. 31, no. 4
acknowledge the contributions of our USD colleagues to this workincluding Drs. Samantha Breslin, Michelle Camacho, Diana Chen, Austin Choi-Fitzpatrick,Odesma Dalrymple, Laura Gelles, Ming Huang, Gordon Hoople, Imane Khalil, Alex Mejia,Breanne Przestrzelski, and Elizabeth Reddy. We thank our Advisory Board members, Drs. AlanCheville, Donna Riley, and Linda Vanasupa, for helping us to reimagine what we can do throughthis grant. Finally, we thank the students who have engaged with these activities and providedhelpful feedback.This work is supported by the National Science Foundation’s Revolutionizing Engineering andComputer Science Departments (RED) program through Award #1519453.References1 R. Olson, S. Lord, M. Camacho, M. Huang, L. Perry, B
; advanced methods for improving hardware and physical network security; evolvable hardware; and evolutionary and recon- figurable computing. He is a senior member of the IEEE organization and several societies, a member of the ASEE and ACM organizations.H. Shelton Jacinto, Boise State University H S. Jacinto received his BS degree in electrical and computer engineering from Boise State University, Boise, Idaho, USA, in 2017, and is currently pursuing a PhD in electrical and computer engineering from Boise State University. From 2015 to 2017 he worked with Idaho National Labs conducting research on self-powered wireless sensor networks and their security. From 2016 he now works in the High Per- formance Reconfigurable
Dec. 9, 2017].[11] J. A. Fredricks and S. D. Simpkins, “Promoting positive youth development through organized after-school activities: Taking a closer look at participation of ethnic minority youth,” Child Development Perspectives, vol. 6, no. 3, pp. 280–287, Sep. 2012.[12] B. A. Danielak, A. Gupta, and A. Elby, “The marginalized identities of sense-makers: reframing engineering student retention,” in 2010 IEEE Frontiers in Education Conference (FIE), 2010, pp. S1H–1–S1H–6.[13] R.M. Marra, K.A. Rodgers, D. Shen, and B. Bogue, “Women engineering students and self-efficacy: A multi-year, multi-institution study of women engineering student self- efficacy,” Journal of Engineering Education, vol. 98, no
of prior studies of STEM identity. Asengineering identity frameworks are further refined we can start to investigate theongoing work of identity formation amongst individuals and groups, thus broadening ourunderstanding of what it means to be an engineer.AcknowledgementsThis research was funded by the National Science Foundation through grants #1636449and #1636404. The authors wish to thank department chairs, faculty members,instructors, and students who made the collection of this data possible. Any opinions,findings, and conclusions in this article are the authors’ and do not necessarily reflect theviews of the National Science Foundation.ReferencesBlake-Beard, S., Bayne, M. L., Crosby, F. J., & Muller, C. B. (2011). Matching by race
question was asked on homework in these offerings as well. The homework assignments were provided to the students in the current offering but not assigned for credit. “What major assumption(s) did you make in problem 2 [holding the shank of the leg in the air] to make the problem tractable (able to calculate the muscle force) and describe in one sentence why this is/these are valid?” Essentially the same question regarding simplifications (single muscle acting at a point, frictionless joint) was asked in the four previous offerings. “The way our bodies are structured, the forces in the muscles and bones are very high for relatively low external forces (e.g., the 5 lb. weight I held out at arm’s length in class resulted in tens of