analytical modeling of semiconductor devices and sensors, and electronic instrumentation and measurement.Joshua Ward, Fairchild Semiconductor Josh Ward was a senior level Electrical Engineering student at the University of Southern Maine and was working as a Thin Films Process Technician at Fairchild Semiconductor Corporation, S. Portland while working on this project. He will complete his coursework and graduate from U.S.M. with a B.S. degree in Electrical Engineering in May 2008. Upon graduation he expects to be promoted to Process Engineer at Fairchild Semiconductor. Josh’s interests are semiconductor device fabrication, CMOS integrated circuit design and automated testing.Robert N
the time thatcan be devoted to this algebra practice. A simple method of partial fraction expansion, to check the studentswork or do the work for them, allows the students to learn more about how mathematical expressionsrepresent the circuits operation. In Matlab, the residue@,D) command provides a partial fraction expansion of a polynomial withnumerator, N, and denominator, D. For instance, consider a simple series RLC circuit with the output takenacross the capacitor. After transformation of this circuit into the Laplace domain and a few calculations,Equation [1] is found to represent the Laplace polynomial of the output of the circuit, where VO(S) is theoutput across the capacitor and VI(S) is the generalized input waveform.A common
in the rejected heat by a shaded and unshaded condenser isdue to solar flux received by the condenser face area. Thus, to investigate the effects of a shadedcondenser on the COP of the refrigeration cycle, solar flux was skipped for the correlatingequations and compared to the normal case when solar flux is available.The improvement in the COP of the cycle was defined as:𝐼 (1)where the subscripts “s” and “u” stand for shaded and unshaded cases, respectively.To evaluate the COP of each case, equations (2) and (3) were used for shaded and unshadedCOP, respectively.𝐶𝑂𝑃 (2) ,𝐶𝑂𝑃
; 0S ( x) = .......................(1) + 1, 0 < x < π Nowadays, however, the availability of software that can evaluate and plot functions easily makesit very convenient to show Gibbs Phenomenon in a way that makes it accessible to all students.Indeed, Gottlieb and Shu[1], who have studied Gibbs phenomenon extensively, display an effectiveanimation of this phenomenon on their website. However, in that animation, students observe theresults of a completed process and it is difficult for the novices among them to learn from thedetails that led to the final results they see on the animation. Our experience indicates that, afterstudents have plotted partial sums on their own, such an animation becomes a
fundamentals of theengineering design process.Over the past four years the author has been responsible for teaching the introductoryengineering course for students on the Missouri State University (MSU) campus who areparticipating in the Cooperative Engineering Program operated by Missouri University ofScience & Technology (Missouri S&T) in cooperation with MSU.During that time, a number of different ideas and topics have introduced in the course. Somewere successful but others failed for a variety of reasons. This paper discusses both thesuccesses and failures and offers suggestions for other teaching similar courses.IntroductionOn August 21, 2006, the Governor of the state of Missouri, along with the Curators of theUniversity of Missouri
Blue llow data po ointsare with t motor ru the unning at 100 power: B with a s 0% Blue small hub fo the pulley, Yellow wit a or , th [5]large hub The Red data points ar with the m b. d re motor runnin at 50% wi the small hub . ng ith lGiven the graphs and an overview on basic p e d w physics, the
besteducational systems in the Middle East where higher education institutions constitute aprosperous source of fresh engineers for the Gulf region and it is regarded as an engineeringeducational center in the Middle East6.Theoretical frameworkThe Theory of Reasoned Action (TRA) helps characterizing human behavior as intentional andrational. This model provides a social psychological framework proved to be useful in explainingseveral types of behavior7,8. It suggests that someone‟s Behavioral Intention (BI) depends onAttitude (A) and Subjective Norm (SN). This framework will help predicting the intention forholding a doctoral degree in engineering. The Behavioral Intention (BI) defines the objective toenroll a PhD program in the future. Attitude (A
engineering design, in preparation for a society that increasinglydemands technological literacy of its citizens.AcknowledgementsThis study was supported by the National Science Foundation under grant DRL-1316762. Anyopinions, findings, and conclusions expressed in this material are those of the authors and do notnecessarily reflect the views of the National Science Foundation.ReferencesAhmed, S., Wallace, K. M., & Blessing, L. (2003). Understanding the Differences Between How Novice and Experienced Designers Approach Design Tasks, Research in Engineering Design, 14 (2003) pp 1-11.Atman, C.J., R.S. Adams, S. Mosborg, M.E. Cardella, J. Turns, and J. Saleem (2007). “Engineering Design Processes: A Comparison of Students and Expert
based technologies, biological transport and moreover crucial for understanding the behavior of water in confined nanopores. V. References [1] S. Yesudasan, “Extended MARTINI water model for Fig. 4: This graph represents the calculation of water using the heat transfer studies,” Molecular Physics, vol. 118, SPC/E model with different diameters. no. 13, p. e1692151, Jul. 2020, doi
obtain a second-order, constant-coefficient, non-homogeneous ODE: d2 C sC(x, s) − C(x, 0) = D 2 . dx 2Step 2: Solve the ordinary differential equation. Rearrange into a standard form: ddxC2 − D s C = − C(x,0
b since using “Add Trendline” cannot Table 1: Record the time for specific heights of the water during an experiment Time (s) Height (cm) 12 11 10 9 8 7 6 5 4 3
Development modules are embedded within existing2nd year courses (Basic Analog Electric Circuits, Basic Digital Circuits, and Introduction toElectronics) in hybrid/remote modality for all students to experience. A select group of students(6 in total- 3 from HBCUs/MSIs and 3 from PWI(s)) are chosen for the continuation to a summerinternship with pre- and post- internship mentorship and training. Collaborators RPI and NotreDame have the same structure of participants. Thus, the total numbers are: 6 IEC-HBCUs(Howard, Tuskegee, UMES, North Carolina A&T, Prairie View A&M, and FAMU/FSU), and a © American Society for Engineering Education ASEE 2025total of 12 students with pre
, DC, pp. 1– 77, 2012.[5] National Research Council, “Promising Practices in Undergraduate Science, Technology, Engineering, and Mathematics Education: Summary of Two Workshops,” The National Academies Press, Washington, DC, 2011. Accessed on 13 June 2016 from http://www.nap.edu/catalog.php?record_id=13099[6] T. A. Litzinger and L. R. Lattuca, “Translating Research into Widespread Practice in Engineering Education,” in A. Johri and B. Olds. (Eds.), Cambridge Handbook of Engineering Education Research, Cambridge University Press, New York, pp. 375–392, 2014.[7] S. Zappe, K. Hochstedt, E. Kisenwether, & A. Shartrand, “Teaching to innovate: Beliefs and perceptions of instructors who teach
Paper ID #37539Application of Internet of Things in Online Robotics ClassZhou Zhang (Dr.)Yizhe Chang Yizhe Chang is an assistant professor in mechanical engineering.Andy Zhang (Professor) © American Society for Engineering Education, 2022 Powered by www.slayte.com 2022 ASEE Annual Conference & Exposition Minneapolis, Minnesota, USA, Conference: June 26 – 29, 2020 Zhang, Z., Chang, Y., Esche, S. K, Zhang, A., Application of Internet of Things in Online Robotics Class
time;connection, enabling them to conduct experiments even ifthey don’t have access to a physical laboratory. • web server, responsible for making system information available (signals from sensors, equipment images, WebLab's have been implemented in several institutions etc.) at interface accessible by the user remotely;since the 90's, presenting solutions for remote operationgenerally using commercially available software or dedicatednetworks [1]-[6]. At the Instituto Mauá de Tecnologia, several • user interface
37 17.45% 55 21.15% Prefer not to answer 27 12.74% 29 11.15% I identify as a person with a disability 23 10.85% 11 4.23% Group(s) not listed above: 21 9.91% 7 2.69% I identify as LGBTQ+ 7 3.30% 8 3.08% Total sample n 212 260Note: Reference sample for check all that apply is the preceding multiple choice question(gender)Group(s) not listed above: Hub Regional Pre-Survey Hub Local Pre-Survey -Black -Jewish -Veteran
the 1st generation, low income, urban and rural highschool student populations. As evidenced by their SAT Math achievement scores and high GPA’swhich prompted their admission, these students are smart. However, they received their STEMeducation in low performing urban and rural high schools and were raised in highly challengedunder-resourced neighborhoods. Research shows that these talented students succumb to theintensity of the 1st and 2nd year university math/science courses. The S-STEM BEATS projectbuilds upon prior NSF S-STEM and STEP projects lessons and practices which proved S-STEMscholars will thrive best when embedded and engaged in an academic innovation ecosystem whichallows students to benefit from the support talents and
find the numerical solution directly from the aboveequation and initial conditions. Matlab code using Symbolic Toolbox and the response plot areshown in Figure 2. % Solve the ode and assign the solution to variable 'x' x = dsolve('D2x + 2*Dx + 5*x = 3','Dx(0) = 0','x(0) = 0','t') % Plot the response from 0 to 5 seconds ezplot(x, [0,7]) % Assign labels to axes and a title to the plot xlabel('Time (s)') ylabel('Response by dsolve') % Plot gridlines grid on % Define axis axis([0 7 0 0.8]); Figure 2 - Matlab code using Symbolic Toolbox and response plotingTwo approaches are introduced to the students to find the solution of an ODE in Simulink. Thefirst one is based on
daemon,# identified by the full daemon path listed below. Paste (Ctrl_v) the saved TEST-Code on this I/O Panel,# click Load and then Super-Calculate to recover the solution. You can email a solution in this manner.############################################################################################## Daemon Path: Test>Daemons>Systems>Open>SteadyState>Specific>RefrigCycle>PC-Model; Version: v-8.0bj04##--------------------Start of TEST-Codes----------------------------------------------------------------------------- States { State-1: H2O; Given: { p1= 12.5 MPa; T1= 550 deg-C; Vel1= 0.0 m/s; z1= 0.0 m; mdot1= 24.0 kg/s; }\ State-2: H2O; Given: { p2= 20.0 kPa; s2= "s1
equilibrate between temperature readings.Analysis:The heat diffusion equation for one dimensional, steady state conduction with constantthermal conductivity is as follows: 1 3 1T 5 1 2T 7k 8 = 0 2 2 = 0 1x 4 1 x 6 1xThe general solution is as follows.T(x) = C1 x + C2Boundary conditions are determined from the student’s experiment. The followingexample uses data for a polycarbonate block 1 cm thick. Polycarbonate was chosenbecause its glass transition temperature is about 150oC and therefore it won’t soften ormelt on the mug warmer surface.T(0) = Tw,s 1 T(0) = 122 o C and T(L) = Tp,s 1 T(0.01m) = 88.8 o CThe particular solution is in symbolic and numeric form: T 1TT(x) = p,s w,s x + Tw,s
( x, 0 ) = 0, ( x, Y ) = f y =Y ( x, t ) ∂y ∂yWith Fint erior ( x, y; s ) and Fy =Y ( x; s ) denoting the Laplace transforms of f int erior ( x, y, t ) and f y =Y ( x, t ) , respectively, USFKAD expresses the Laplace-transformed solution asΨ = Ψ1 + Ψ 2Ψ1 = ∑ κ sin κ x x cosh κ x2 + sy A ( s; κ x ) x Page 11.188.7 π 2π 3πwith κx = , , ,... X X X 2A ( s; κ x ) = ∫ 0X dx sin κ x x M
bringing the entrepreneurial mindset to engineering education. c American Society for Engineering Education, 2017 The rise of rapid prototyping in a biomedical engineering design sequenceIntroductionPrototyping has always played an important role in the design process as way to determineconceptual viability and iterate upon an idea. Over the last decade, the decreasing costs,improved accuracy, and wide-spread availability of rapid prototyping (RP) technology haslowered the barriers to early-stage prototyping. At universities, the result has been the rise ofmaker’s spaces, skill-based pop-up classes and rapid design challenges. In this paper, we explorethe history of rapid prototyping throughout the 1990’s and 2000
, which was verified with this data. Stage 3: In this stage, the orthogonal arrays (OA) and signal-to-noise (S/N) ratios are calculated and used to determine the most useful set of predictive variables. Larger S/N ratios are preferred and indicate a possible useful predictive variable. 3 Stage 4: The variables that were identified as significant due to a positive S/N are used to develop a forecasting model. Table 1. Descriptive Statistics of Raw Data Completers Range Factor N Mean Median
) executive Board Positions are:President, MAES Vice President, SHPE Vice President, Vice-President of Internal Affairs, Vice-President of External Affairs - Corporate, Vice-President of External Affairs - Jr. Chapters,Treasurer, Historian and Webmaster. The Vice-President of External Affairs – Jr. Chaptersoversees the Jr. Chapter Representative Committee, which is composed of the different Jr.Chapter Representatives for every high school having an established Jr. Chapter. Every Jr.Chapter has their own executive board team that work with their respective chapter advisor(s). Agraphical representation of the administrative structure is shown below.As shown in the schematic (Figure 1), the Jr. Chapter Representatives report directly to the Vice
Effect of Different Masses 12 9 (m/s) 8 (m/s) 10 loop loop 8 7 v v 6
,and acceptance by consumers. Here, one of the engineering students, also authors of this studyreviewed microfluidics for plant cell studies to address the problems and concerns. Theundergraduate student has used these research activities for his Engineer 2020 requirements.Overall, these studies greatly benefit undergraduate engineering students for their future academicstudies at different institutions.ReferencesAgudelo, C. G., Sanati Nezhad, A., Ghanbari, M., Naghavi, M., Packirisamy, M., & Geitmann, A.(2013). T ip C hip: a modular, MEMS‐based platform for experimentation and phenotyping of tip‐growing cells. The Plant Journal, 73(6), 1057-1068.Bascom, C. S., Wu, S. Z., Nelson, K., Oakey, J., & Bezanilla, M. (2016). Long-term growth
developed that willcontinue to fuel this growth? Science and engineering (S&E) enrollments have remainedrelatively stagnant for the past 20 years. If this trend continues, what will happen to theeconomy? The authors recognize the need to increase overall enrollments in S&E fields, and theopportunity to increase enrollments by attracting and retaining students from underrepresenteddemographic population groups. Women represent over half the nation’s population and nearlyhalf of the undergraduate enrollment, yet are dramatically underrepresented in the technical andacademic community. Increasing participation of underrepresented groups in S&E will not onlyincrease the available technical workforce, but will also interject ideas and
Session 3475 Teaching Lessons from Engineering Feedback Model for New Educators Dr. Ramesh Gaonkar Computer & Electrical Engineering Technology SUNY, Onondaga Community College Syracuse, New YorkAbstract:*The Shannon s communication model is often used as a presentation vehicle in a teaching andlearning environment. The model includes an input, an output, and a receiver or a transmitter.In engineering, we view this model as a open loop system. A classroom lecture by itself
Session 3475 Teaching Lessons from Engineering Feedback Model for New Educators Dr. Ramesh Gaonkar Computer & Electrical Engineering Technology SUNY, Onondaga Community College Syracuse, New YorkAbstract:*The Shannon s communication model is often used as a presentation vehicle in a teaching andlearning environment. The model includes an input, an output, and a receiver or a transmitter.In engineering, we view this model as a open loop system. A classroom lecture by itself
hierarchical ornetwork form, with labeled nodes (in circles or boxes) denoting concepts, and linking words orphrases specifying the relationships among concepts. Two or more concepts that are connectedby linking words or phrases form a proposition (i.e., a meaningful statement). Figure 1 showsthe structure and characteristics of concept maps 8.Since its development in 1972 by Joseph Novak and his colleagues 7, 8, who sought to follow andunderstand changes in children‟s knowledge of science, concept mapping has been adopted innearly every discipline ranging from STEM (science, technology, engineering, mathematics),psychology, and medicine to business, economics, accounting, history, and literature by