AC 2012-4308: INTRODUCING GRAPHICS PROCESSING FROM A SYS-TEMS PERSPECTIVE: A HARDWARE/SOFTWARE APPROACHMr. Michael Steffen, Iowa State University Michael Steffen is a Ph.D. candidate in computer engineering and NSF Graduate Research Fellow. His research interests include computer architecture, graphics hardware, computer graphics, and embedded systems, and specifically he focuses on improving SIMT processor thread efficiency using a mixture of custom architectures and programming models. He received a B.S. degrees in both mechanical engineer- ing and electrical engineering from Valparaiso University in 2007.Dr. Phillip H. Jones III, Iowa State University Phillip H. Jones received his B.S. degree in 1999 and
two chapters on mathematical topics is given at the beginningof the semester. The situation is even worse if the teachers rely on students’ knowledge frommath courses.2. There is disconnect between the theory discussed in the lectures and the experiments carriedout in the accompanying lab. This disconnect is made more severe by two factors: (a) sometimesthe lab either precedes or lags behind the lecture material and (b) the theory and the lab aretaught by two separate instructors who profess different teaching philosophies and havedifferent ideas about what is important and what is not.3. In the present teaching system the assessment of students’ understanding of the subject isinfrequent. Typically it consists of a final exam with one or two
Paper ID #17030Growing Experimental Centric Learning: The Role of Setting and Instruc-tional Use in Building Student OutcomesDr. Yacob Astatke, Morgan State University Dr. Yacob Astatke completed both his Doctor of Engineering and B.S.E.E. degrees from Morgan State University (MSU) and his M.S.E.E. from Johns Hopkins University. He has been a full time faculty mem- ber in the Electrical and Computer Engineering (ECE) department at MSU since August 1994 and cur- rently serves as the Associate Dean for Undergraduate Studies in the School of Engineering. Dr. Astatke is the winner of the 2013 American Society for Engineering
DSP and FPGA, and designing and implementing digital signalprocessing methods, and Radio RF components such as Antenna, LNA, Mixer and RF Filters.This paper investigates the contents and the dynamics of a set of curriculum for WirelessCommunications. A survey that is distributed across multiple industries in WirelessCommunication is analyzed to determine (a) the general topics (curriculum subsets) that shouldbe covered to create a complete curriculum in Wireless Communications, (b) specificinformation that should be transferred in each general topic (curriculum subset), and (c) topicsimportant for developing hands on experience and improving students skills (such as lab andenterprise experiences).1. IntroductionWireless communications has
discussed.Laboratory: Students work with a light-dependent resistor (LDR) shown in Fig. 3 (a) as an example ofresistive sensors discussed in the lectures and they are asked to measure LDR’s resistance using the NIVirtualBench digital multi-meter (DMM) under ambient light (R0) and compare it to the resistance that theymeasure for the same sensor in dark (Rdark) and in abundant light (Ramb) when they shine light on the LDRusing a flashlight. (a) (b) Figure 3: (a) light-dependent-resistor (LDR) (b) Wheatstone bridge light sensor circuit2) Bridge CircuitsLectures: The discussion on resistive and capacitive sensors is followed by the question that how a changein resistance or capacitance of a
still active in the program and on a path towards graduation, 1 student switched to arelated program and 11 did not graduate and are no longer active in the program. Removing the12 students from the 204 possible results in a persistence rate of nearly 94% for students thatsuccessfully complete this course sequence.Course grades were collected for each student record. The grade system at MSOE consists of“A”, “AB”, “B”, “BC”, “C”, “CD”, and “D.” These letter grades were mapped numerically into4, 3.5, 3, 2.5, 2, 1.5, and 1, respectively for analysis.Plots showing grade distribution where compiled for EE1910, EE2920, and EE2930. Thedifferences in grades, or delta, between EE1910-EE2920, and EE2920-EE2930 were alsocomputed for each student record
Page 25.486.2through the Electrical Engineering or Computer Engineering programs. Incorporation of globallearning into our curriculum gives us an opportunity to better prepare our students for careers astruly global engineers 3;6 .Three key results of this integration of global learning elements into the ECE curriculum are thefollowing: 1. Several elements of global learning were already present in our ECE curriculum, but were not formalized or assessed. The most common were: (a) Discussion of historical background of course topic; (b) Sustainability, in the form of efficient design or use of resources (for example, mini- mized logic, efficient code or spectrum usage). 2. Global learning
: thickness outer diameter Lroroid 0 r N 2 ( ) . 2 inner diameter A B 20T C 20T D Figure 1. The Common-mode Choke Construction. Page 14.1269.5 Another method of determining both self and mutual inductances simultaneously involvesthe measurement of the resonant frequencies of the two configurations of the coil in series with a10 nF capacitor as shown in Figure 2. From the measured
comprehensive numericalstudies and application-based projects, as further described below. The instruction started withthe wave nature of light, as depicted in the left column of Figure 1. Wave Nature of Light Particle Nature of Light Maxwell Eqn. Helmholtz Eqn. Einstein A/B Coefficient Uniform Plane Wave (UPW) Rate equation Properties of UPW, Phase Blackbody radiation Photon statistics Polarization, TE/TM waves Reflection/Transmission (R/T) on interface Rate equation Total Internal Reflection 3- and 4- level
consists of four parts: 1. The target, or subject: the information to be learned. 2. The source, or analog: the familiar thing to which the new information is compared. 3. The connector: the means by which the subject and analog are compared. 4. The ground: the description of the similarities and differences between the subject and analog.To facilitate the use of analogies, the ABCDE method of constructing an analogy is considered[23]. A. Analyze the subject: what is it you most want the learners to understand about the subject? B. Brainstorm potential analogs: what concrete items share the important features you have identified? C. Choose the analog: which candidate analog has the best
revealed that 53% of students were less than confident abouttheir chances of successfully passing this course. A similar survey conducted in the summer of2017 revealed that 21 and 19 out of 27 students had received a lower grade than B in Calculus IIIand Physics 220, respectively.III. METHODA. Faculty Training in Transparent AssignmentsThe instructor of the course in this study attended a transparent assignment workshop in thesummer of 2017 and was provided with a transparent assignment template and a checklist, andpreviously revised assignments. The instructor reviewed sample assignments and learned toidentify differences between a less and a more transparent assignment [4]. As practice, oneassignment was revised during the workshop.B. Indirect
Proceedings of the 49th ASEE/IEEE Frontiers in Education Conference, Cincinnati, OH, Oct. 16-19, 2019. 3. C. Zilles, M. West, D. Mussulman, and T. Bretl, “Making Testing Less Trying: Lessons Learned from Operating a Computer-Based Testing Facility,” in Frontiers in Engineering (FIE), 2018. 4. B. Chen, M. West, and C. Zilles, “Do performance trends suggest wide-spread collaborative cheating on asynchronous exams?,” in Proceedings of the Fourth ACM Conference on Learning at Scale, 2017. 5. B. Chen, M. West, and C. Zilles, “How much randomization is needed to deter collaborative cheating on asynchronous exams?”, in Proceedings of the Fourth ACM Conference on Learning at Scale, 2018.6. B. Bloom, “Learning for mastery
10engineering curriculum,” Power and Energy Society General Meeting, 2010 IEEE, pp.1-5, July 2010.[12] N.N. Schulz, “Integrating smart grid technologies into an electrical and computer engineering curriculum,”Innovative Smart Grid Technologies Asia (ISGT), 2011 IEEE PES, pp.1-4, November 2011.[13] J. Ren and M. Kezunovic, “Modeling and simulation tools for teaching protective relaying design andapplication for the smart grid,” Modern Electric Power Systems (MEPS), 2010 Proceedings of the InternationalSymposium, pp.1- 6, 2010.[14] H. Mohsenian Rad and A. Leon‐Garcia, “Distributed Internet based Load Altering Attacks against SmartPower Grids,” IEEE Transactions on Smart Grid, vol. 2, no. 4, pp. 667‐674, Dec 2011.[15] L. Xie, Y. Mo, and B. Sinopoli
problem really existed. We asked the question: "Do youfind that there is a problem with communication between students in college?" i.e. class assignmentcollaborations, group projects, and etc. The survey results can be seen as shown in 0(a). Our next question asked the following question: "Would you use a mobile application that wouldcreate a central place for better communication between students?" This was a possible solution weposed to the respondents. The survey results can be seen as shown in 0(b). We also asked the following question: "If you would not use the mobile application, do you thinkother students would benefit from this mobile application?" This is to ensure that our design ideawould still solve the problem. Those who
. This meansthat the most recent grade will be included in the cumulative GPA calculations for the studentirrespective of previous performance. The dataset used in this analysis contains the outcomes ofall course work and thus grades were only used in accordance to the “repeat-delete”policies.The courses examined are the six major courses presented during the junior year curriculum thatcover the three main topics of signals and systems, electronics, and electromagnetics. The coursenumbers are ECE 311/312, ECE 331/332, ECE 341/342, respectively. Table 2: Letter Grade to Numerical Grade Conversion A+/A/S A- B+ B B- C+ C C- D+ D D- F/I/W 4.00 3.67 3.33 3.00
first five labs are mainly construction labs where students are developing practical, hands-onskills and gaining familiarity with common prototyping practices. These skills include (a)utilizing a 3-D printer in order to create the chassis, wheels, and sensor mounts, (b) disassembly,modification, and reassembly of two servo motors, and (c) assembly and soldering two custom-designed printed circuit boards (PCB)—totaling approximately 50 components and 200 solderpoints. Once all the subsystems are complete, they are screwed together, along with a batterypack and front contact sensing bumper.In the final six labs, the students systematically build-up the various digital designs needed inorder to autonomously control their individually-built mobile
algebra. The following topics areamong those areas: a) How to multiply two matricesIf we multiply a m×n (m is the number of rows and n is the number of columns) matrix by a n×pmatrix, the result will be a m×p matrix. If the number of columns of the first matrix is not equalto the number of rows of the second matrix, we cannot multiply those two matrices.In multiplication of two matrices, if the order of the two matrices change, if still the dimensionsallow multiplication, the result of multiplication will be different than the previous multiplicationunlike the multiplication of two scalars. b) How to write a set of equations with multiple unknowns in the form of matrices.If we have a set of q linear equations with q unknowns, the equations
,andhencecomputationalmethodsforprocessingimagedataareofcriticalimportance. Extracting useful information from raw images involves a broad range ofmathematical techniques and algorithms including but not limited to optimization,modeling, discrete algorithms, and methods for high‐level image understanding.Researchers are creating new algorithms in a range of applications, from astronomy, toreconstructing volume data from medical scans, to automatically reconstructing 3Dgeometryfrom2Dphotos.WeeklyAgenda:Week1IntroductiontoDigitalCameraandPhotographya. IntroductiontoDigitalPhotographyb. IntroductiontoCameras a. CameraSensors b. Lensesc. IntroductiontoMatlabImageProcessingToolboxd. CameraBasicse. FundamentalofDigitalImagesf. ColorPhotographyg. IntroductiontoPhotographyFilters Labs1
3.3 3 Criterion B (an ability to design and conduct experiments, as well as to analyze and interpret data) Demonstrate a clear understanding of the Scientific Method and how to test hypotheses
very successful tool for load forecastingapplications and it was adopted widely in such applications during last two decades. Thebuilding block of the neural network is the neuron, the mathematical model of the neuron isgiven in Fig.2 (a). The mathematical expression of each single neuron can be given by: m yk [Wkj X j bk ] (1) j 1The structure of an artificial neural network (ANN) consisting of 13 neurons is shown in Fig.2(b). As shown in the figure, the ANN has four layers; one is the input layer, two hidden layersand one
simple and automated mechanism for students to provide constant anonymous feedback and schedule appointments.References [1] Shannon E Ross, Bradley C Niebling, and Teresa M Heckert. Sources of stress among college students. Social psychology, 61(5):841–846, 1999. [2] Liselotte N Dyrbye, Matthew R Thomas, and Tait D Shanafelt. Medical student distress: causes, consequences, and proposed solutions. In Mayo Clinic Proceedings, volume 80, pages 1613–1622. Elsevier, 2005. [3] Vivek B Waghachavare, Girish B Dhumale, Yugantara R Kadam, and Alka D Gore. A study of stress among students of professional colleges from an urban area in india. Sultan Qaboos University Medical Journal, 13(3): 429, 2013. [4] Andrea Dixon Rayle and Kuo-Yi
. DeHennis and K.D. Wise, “A Wireless Microsystem for the Remote Sensing of Pressure, Temperature, and Relative Humidity,” IEEE/ASME J. MEMS, vol. 14, no. 1, pp. 12–22, Feb. 2005.[9] ZigBee Alliance. Available: http://www.zigbee.org/en/index.asp[10] J. Frolik and M. Fortney, “A Low-Cost Wireless Platform for First-Year Interdisciplinary Projects,” IEEE Trans. on Education, vol. 49, no. 1, pp. 105–112, Feb. 2006. Page 13.37.10[11] B. Warneke, M. Last, B. Liebowitz, K.S.J. Pister, “Smart Dust: Communicating with a Cubic-Millimeter Computer,” IEEE Computer, pp. 44–51, Jan. 2001.
project is designed for the optimal linear system functioning as apredictor. The original example in a textbook looks like this:Let X 1 , X 2 ,Λ be a random sequence. Suppose that a second-order prediction system is to bedesigned such that a sample is predicted by the previous two samples. Find the systemparameters a and b that yield the minimum prediction error.If only this original example is used, students may practice on system design using the formulaprovided in the textbook without knowing its practical importance. Actually, optimal linearpredictor has very important applications in DSP (as well as DIP). An application-orientedcomputer projector can be designed based on this sample as below.Sample Project 2 (simplified): Record your voice
, students will be able to: 1. Apply advanced concepts in analyzing, designing and building digital systems (Outcomes: 1, 6) 2. Employ modern day tools in designing, testing and debugging complex digital systems (Outcomes: 1,7) 3. Rapidly prototype applications on programmable devices using high-level language (Outcomes: 1,6,7)B. QuestionsThe students will be asked six questions that will be rated on a five point scale, where ‘1’indicates “not at all” and ‘5’ indicates “very much”. Along with this, students will be given theopportunity to include written comments for additional three questions. This will help us to getvaluable feedback for improvement of the course. The questions will be: 1. The focus of this course was
process begins with a state diagram (a), which, given a state encoding(b), implies a truth table (c) which leads to combinational logic (d), a part of the standard architec-ture of a finite state machine (FSM) (e). To implement this in a laboratory environment, severaldecisions must be made.First, should the design be implemented using discrete 74x logic gates? While the simplicity isbeneficial, this approach has several drawbacks. First, the approach scales poorly when moving tolarger datapath designs. For example, the design shown in Figure 5 would require a prohibitive Page 26.1082.4number of devices and wiring. Second, it provides no
before and after studying used.electromagnetism electromagnetism in a media-concepts? rich environmentsConceptual Sangam, D ASEE To discuss the details of an Quantitative Lecture Pre and post Test scores indicate significantunderstanding of Jesiek, B. Conference instructional module concept increase in students learning which canresistive electric Proceedings, implemented and present inventory test, be attributed to
submit lab reports jointly in groups of two students. In the DynamicSystems course, the students were grouped based on their academic records, i.e., every groupwould have one A/B student, one or two C students and one D/E student, determined as theiraverage mark from the Physics and Chemistry course. The setting in ProLab enabled thestudents to work effectively in groups and to present their work to their own group (smallscreens) and to the whole class (large screens). The physical design of ProLab enabled a highdegree of flexibility and allowed rapid transition between different activities. The students areactivated by inclusion, as group members solving tasks, and as presenters for their class. Thepre-class activities were designed on an
Paper ID #34793Work in Progress: Investigating the Role of Entrepreneurial-mindedLearning (EML) in Enhancing Student Learning for a Freshman Engineer-ingCourseDr. Chandana P. Tamma, Marquette University Chandana P. Tamma received her PhD in Electrical Engineering (2009) from Rensselaer Polytechnic Institute, Troy. NY. She is currently an Adjunct Assistant Professor with the Department of Electrical and Computer Engineering at Marquette University, Milwaukee. WI.Mr. Matthew Curran, Marquette University Matt Curran supports efforts related to KEEN’s Entrepreneurial Mindset at Marquette University as a KEEN Project Associate
(PSerc) also supported the development of this class.References[1] About Colorado School of Mines (CSM), Retrieved December 8, 2005 from: http://www.mines.edu/all_about/[2] CSM – Division of Engineering, Retrieved December 8, 2005 from: http://egweb.mines.edu/[3] Silverstein, K., “Creating Energy Jobs,” EnergyBiz Insider, August 2005.[4] Chowdhury, B. H., “Power Education at the Crossroads”, IEEE Spectrum, Vol. 37, No. 10, October 2000, pp. 64-68.[5] Cowdrey, J., “Hydroelectric Power in a Municipal Water System”, The City of Boulder, CO Publication, February, 2000. Page 11.211.10[6] Ammerman, R.F., Sen
and M. Morgan, "The instructional effect of feedback in test- like events," Review of Educational Research, vol. 61, pp. 213-238, 1991.[2] L. Hirsch and C. Weibel, "Statistical Evidence that Web-Based Homework Helps," MAA Focus, p. 14, February 2003.[3] R. J. Marzano, D. J. Pickering and J. E. Pollock, Classroom instruction that works: Research-based strategies for increasing student achievement, Alexandria, VA: Association for Supervision and Curriculum Development, 2001.[4] J. P. Carpenter and B. D. Camp, "Using a Web-Based Homework System to Improve Accountability and Mastery in Calculus," in 2008 ASEE Annual Conference & Exposition, Pittsburgh, 2008.[5] B. Means, Y. Toyama, R. Murphy, M. Bakia, K. Jones and Center for