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SP education

1053-5888/16©2016IEEE IEEE SIgnal ProcESSIng MagazInE | November 2016 |

Ed Richter and Arye Nehorai

Enriching the Undergraduate Program with Research Projects

hile some engineering departments struggle with low enrollment today, the Preston M. Green Electrical and

Systems Engineering (ESE) Department at Washington University in St. Louis, Missouri, more than tripled over the last ten years. This was done under the guid­ance of Dr. Arye Nehorai, who was appointed its chair in 2006. This growth, including 34% female enrollment, is mostly due to students joining our department during or after their freshman year. The growth was fueled by improve­ments to the undergraduate educational experience including creating an under­graduate research program, improving the instruction quality, revising old cours­es, creating new courses, modernizing

our laboratories, creating study abroad programs, and communicating the new opportunities in the ESE Department to the engineering student body. This arti­cle focuses on the undergraduate re ­search program.

IntroductionThe undergraduate research program was created in response to student interest and current trends in education and gave us an opportunity to add more exciting design elements to our curriculum. Our students gain valuable experience in research, design, algorithm development, project implementation, and verification. These are skills that students can share during job interviews for both academia and industry. Also, some of our undergraduate researchers have decided to pursue careers in academia as a direct result of

their involvement in undergraduate research projects.

Over the years, we have streamlined our approach and developed the program described here that works well for stu­dents interested in gaining independent project experience at the undergraduate level. This approach includes both under­graduate participation in faculty research as well as projects with Ph.D. students and our Professor of Practice, Ed Richter, as mentors. Overall, we have had more than 200 students from the ESE Depart­ment and from other departments partici­pate in our undergraduate research projects program.

ESE297: Introduction to Undergraduate Research ProjectsFreshmen and sophomores who are inter­ested in undergraduate research projects are encouraged to take our Introduction to Undergraduate Research Projects (ESE297) course [1]. This three­credit course provides exposure to signal pro­cessing topics and introduces the design process early in the curriculum. By intro­ducing design earlier, the students are not only preparing for undergraduate research projects but also for our Capstone Design course. The students learn valuable team­work skills by working in groups of two. They are guided through the design pro­cess for two signal processing projects. These projects were selected after a few early undergraduate researchers and their mentors worked on these projects for a few semesters.

Digital Object Identifier 10.1109/MSP.2016.2601652 Date of publication: 4 November 2016

EDITORS’ NOTEThe article in this “SP Education” column is the second in a special series on hands-on and design projects for signal processing education. The authors from Washington University in St. Louis, Missouri, United States, share their experiences and best practices of introducing undergraduate research projects as a key element in the undergraduate education of their electrical and system engineering program. Many of the projects are related to signal processing. As we will see, the program has energized the students’ understanding and interests toward signal processing and, more generally, electrical engineering, and contributed to an impressive record of attracting students to pursue the field. We hope you will find the article inspiring, and stay tuned for more arti-cles in the series in upcoming issues.

—Hana Godrich, lead guest editorand Kenneth Lam, area editor, columns and forums

W

124 IEEE SIgnal ProcESSIng MagazInE | November 2016 |

The first project is acoustical source localization using a four­element microphone array [2]. The students are guided through an algorithm for triangu­lating a source location using a four­ele­ment microphone array. Next, they develop the signal processing implemen­tation using LabVIEW as the program­ming language and a simulator that we provide them. Once the triangulation works in simulation, they test their imple­mentation on the actual acoustic array. In the next phase of the project, the students implement the triangulation using a cen­tral computer and two modified National Instruments Dani Robots, each equipped with Wi­Fi and a microphone pair as shown in Figure 1(a). In their final presen­tation, we move a chirping speaker around and their mobile robots automati­cally track it.

The second project in this course is a multidisciplinary electroencephalogram (EEG) signal processing application to

create a brain–computer interface (BCI) to control robot motion in one dimension. We use a commercial 14­channel EEG electrode array from Emotiv, portions of the BCI2000 framework, and MATLAB as the programming language. The stu­dents are guided through the algorithm development to measure the average sig­nal power in 2­Hz bandwidths for the 14 electrodes and to build a classifier to detect a power change in the “best” chan­nel/frequency pairs.

We start with the clinical data provid­ed with the BCI2000 framework collected during an experiment where the subject was asked to raise his legs or to relax. The clinical data file contains the raw data from the EEG sensors as well as the tim­ing information of the action and rest tri­als. The BCI2000 framework also provides the MATLAB functions to extract the raw data and timing informa­tion from the file. The students then implement the signal processing and

classification in MATLAB working on the clinical data. After they validate their implementation against the BCI2000 results for the same data, they collect their own data with the Emotiv headset while being prompted to clench their fists or rest. Using their collected data as the training set, the students design their own classifier using their “best” channel/fre­quency pairs. When they are satisfied with the receiver operating characteristic (ROC) curve of their system, they design/implement a real­time LabVIEW/MAT­LAB/Emotiv software development kit (SDK) application that processes the EEG data in real time and moves the mobile robot every time they clench their fists as shown in Figure 1(b) [3].

ESE497: Undergraduate Research ProjectsThe top students from ESE297 are encouraged to sign up for the course Undergraduate Research Projects (ESE497) [4]. However, ESE297 is not necessarily a prerequisite for ESE497. We also encourage the top third­ and fourth­year students from across the engineering school to get involved in our undergradu­ate research projects. The students are required to work on their projects for at least ten hours per week for two credits, create a blog/webpage of their project, and participate in several poster sessions. The required presentations are a good experience for the students, where feed­back is provided from their peers as well as from the faculty. Where applicable, we encourage our students to publish their projects in a peer­reviewed journal.

Students are given a choice of 50 proj­ects [5] listed on our website that cover topics such as signal and image process­ing, machine learning, optimization, power and energy, controls and opera­tions research. We interview each student to find a good match between his or her interest and the available projects. One of the themes of our projects is multidisci­plinary robotic sensing [6], merging sig­nal and image processing, robotics, and biomedical and environmental sensing. We are also flexible when an entrepre­neurial­minded student comes to us with a good idea. Sometimes the student’s ideas/projects are part of our design

(a)

(b)

Figure 1. Platforms used in ESE297: (a) the mobile microphone array and (b) a student controls a robot with EEG signals from the Emotiv headset.

125IEEE SIgnal ProcESSIng MagazInE | November 2016 |

competition, like our Engineering School Discovery Competition. Additionally, many of our younger faculty post projects directly related to their research interests to attract potential Ph.D. students. When the proposed projects are part of a faculty member’s active research, that faculty member is the mentor and the students work on their projects in the faculty mem­ber’s lab.

More commonly, we have more stu­dent interest than our faculty can support. For some of these cases, we carefully select Ph.D. students working in a related area to mentor these students. The under­graduates benefit from this because they get more one­on­one opportunities with a top Ph.D. student than they might other­wise get with a busy full­time faculty member. It is mutually beneficial for the Ph.D. student as they get an opportunity to work with our bright undergraduates. In many cases, the projects are multi­semester, and the more senior undergrad­uate students are also involved in the mentoring. Additionally, we have a Pro­fessor of Practice, Ed Richter, who men­tors projects and supports other mentors in their efforts.

We have developed many platforms that students can use for their projects and we provide “Getting Started Guides” [7] for these platforms on our undergraduate research webpage. This infrastructure allows the students to focus on the signal processing algorithm development instead of spending the semester figuring out the low­level communication details to some new piece of hardware. Several of the platforms available to our students are listed below:1) ESE297 students can continue to work

with the mobile robots used in ESE297 to develop autonomous navi­gation algorithms.

2) ESE297 students can continue to work with the EEG headset to develop more advanced classification applications for BCI projects [8].

3) In 2012, we purchased an industrial robotic arm (Figure 2) [9] made by Fanuc, and we developed a communi­cation scheme using the National Instruments Ethernet/IP tool kit that allows the students to position the robotic arm using LabVIEW or

MATLAB. Then, we attached a Microsoft XBox 360 Kinect camera to the chassis of the robot and developed a Kinect SDK application that pass­es images from the camera to a MATLAB program. The students use this platform to calibrate the Kinect camera to the Robotic coordinate sys­tem and develop image processing applications to locate objects for the robotic arm to pick up.

4) In 2010, we purchased several National Instruments USRP 2920 and USRP 2921 for wireless communication and array signal processing applications. Students have used this platform for localization and Doppler measure­

ments (video [10] by Prof. Ed Richter and Dr. Martin Hurtado).

5) In 2014, we purchased two inertial movement unit sensors [11] as part of our collaboration with researchers at KTH in Sweden. Students have used this platform to analyze tremors in patients who have Parkinson’s disease.

6) In 2012, we developed a 64­channel microphone array as shown in Figure 3. Students can work with the array to develop audio beamforming applications (beamformed audio file created by Ricky Chen and Xiangyang Mou [12]).

7) Our labs are equipped with data acqui­sition systems that we use in our

Figure 3. Sixteen channels of a 64-channel microphone array.

Figure 2. An industrial robotic arm for student projects.

126 IEEE SIgnal ProcESSIng MagazInE | November 2016 |

courses but that are also available for undergraduate research projects that can use the computer as the digital sig­nal processor. Real­time applications with data acquisition can be easily written in both LabVIEW and/ or MATLAB.

Undergraduate research examplesSome of the more successful undergrad­uate research projects to date from the ESE497 research course are as follows:

■■ Yuni Teh (supervisors: Arye Nehorai and Ed Richter), Human­Machine Interface: Myoelectric Control Scheme to Restore Upper Extremity Motor Function, Spring 2015 [13]

■■ Ren Liang Liang, Tsinghua Uni­versity (supervisors: Arye Nehorai, Martin Hurtado, and Ed Richter), Radio Tomographic Imaging­ Based Fall Detection, Summer 2015 [14]

■■ Yifan Wang and Stephen Gower (supervisor: Arye Nehorai), Robotic Avoidance Using Kinect and UST, Summer 2015 [15]

■■ Daniel Wasseman (supervisor: Zachary Feinstein), Grid Search Algorithm for Set Optimization, Spring 2015 [16]

■■ David Sehloff and Celso Torres (supervisors: Arye Nehorai and Ed Richter), Predicting the Solar

Resource and Power Load, Spring 2015 [17].

ESE Department growthWe created our undergraduate research program in 2006 as part of the effort to attract more students to electrical and/or systems engineering. The result of these efforts has shown a big jump in undergraduate research enrollment in 2009/2010 shown in Figure 4. This is one of the reasons our department has tripled in size since 2008 as shown in the bold red line in Figure 5.

Other factors contributing to the growth of the ESE Department in ­clude improving the instruction quality, modernizing our laboratories, adding introductory courses in electrical and systems engineering, and offering study­ abroad opportunities. Additionally, we have improved our outreach to attract the undecided students and inform students from other departments about our unique programs. These presenta­tions and web documents highlight our flexible curricula and give exam­ples to show how to add a second major in electrical or systems engi­neering. One more way we are suc­cessful in increasing the enrollment is by interacting with students and get­ting their feedback. In particular, Dr. Nehorai contacts every student who decides to move to our department to understand his or her moti vation for changing majors, and he has personal­ly mentored more than 100 students in his lab over the last ten years. The success of these efforts is shown in  Figure 5. Our entering class size is small (shown by the dark blue bar) but the class size has increased from fresh­man year (dark blue) to senior year (purple) every year since 2010 as stu­dents join our department after arriv­ing at Washington University.

ConclusionsStudent interest in undergraduate research projects continues to grow. We  have tried to meet that demand by  developing a “training” course (ESE297) and by offering many diverse topics and platforms for them to use

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127IEEE SIgnal ProcESSIng MagazInE | November 2016 |

for their own projects. In general, the students find ESE297 and undergradu­ate research projects (ESE497) ex ­tremely rewarding, albeit challenging. The students really appreciate the opportunity to apply the theory they have been studying. The one­on­one mentoring with tenured, tenure­track faculty, Professor of Practice, or Ph.D. students is a valuable opportunity to our students. The feedback they get from their peers and the faculty about their presentations forces them to think about their work in a new light. The students can often showcase these projects in job interviews and graduate school applica­tions. Our undergraduate research pro­gram is just one way that we have expanded the enrollment in the ESE Department over the last ten years.

AuthorsEd Richter (ed@ese.wustl.edu) is a Professor of Practice in the Preston M. Green Department of Electrical and Systems Engineering at Washington University in St. Louis. He brings 30 years of real­world engineering experi­ence to the classroom and to undergrad­uate research projects.

Arye Nehorai (nehorai@ese.wustl.edu) is the Eugene and Martha Lohman Professor of Electrical Engineering in

the Preston M. Green Department of Electrical and Systems Engineering at Washington University in St. Louis. He served as chair of this department from 2006 to 2016. Under his leader­ship, the department’s undergraduate enrollment has more than tripled in four years and the master’s enroll­ment grew sevenfold in the same time period. He is a Fellow of the IEEE, Royal Statistical Society, and the American Association for the Ad ­vance ment of Science.

For More Information All references in this article can also be found by visiting https://sites.wustl.edu/spmrefs2016.

References[1] ESE297—Introduction to undergraduate research [Online]. Available: http://classes.engineering.wustl.edu /ese497/ index.php/ ESE297_­_ Int ro_ to_Undergraduate_Research

[2] Mobile microphone array video [Online]. Available: http://classes.engineering.wustl.edu/ese497/index.php/Project2:_Triangulation_with_sbRIO_robots

[3] BCI robot control video [Online]. Available: http://classes.engineering.wustl.edu/ese497/index.php/BCI_Projects

[4] ESE497: Undergraduate research projects [Online]. Available: http://classes.engineering.wustl.edu/ese497/index.php/Main_Page

[5] Wash. Univ. St. Louis available undergraduate research projects [Online]. Available: http://ese.wustl .edu/undergraduateprograms/Pages/Available UndergraduateResearchProjects.aspx

[6] Robotic sensing project [Online]. Available: ht tp://classes.engineer ing.wust l.edu /ese497/images/3/3c/Robotic_Sensing_V4.pdf

[7] Getting started guides [Online]. Available: http://classes.engineering.wustl.edu/ese497/index.php/Main_Page#Getting_Started_Guides

[8] BCI hand control download

[9] Fanuc Robot control [Online]. Available: http://classes.engineering.wustl.edu/ese497/index.php/Matlab_Fanuc_Robot_Control

[10] Doppler shift velocity measurement with NI USRP [Online]. Available: http://classes.engineering.wustl.edu/ese497/index.php/Getting_Started_with_the_USRP

[11] Openshoe website [Online]. Available: http://www.openshoe.org

[12] Beamformed audio file [Online]. Available: ht tp://ese.wust l .edu /ContentFi les / Resea rch /UndergraduateResearch /CompletedProjects/WebPages/su15/MouXiangyang/TwoSource.wav

[13] Y. Teh. (2015). Human­machine interface: Myoelectric control scheme to restore upper extre­mity motor function, ESE 497 [Online]. Available: https://sites.wustl.edu/spmrefs2016/yuniteh

[14] L. L. Ren. (2015). Radio tomographic imaging­based fall detection, Tsinghua Univ., ESE 497 [Onl ine]. Avai lable: ht tps://sites.wust l.edu / spmrefs2016/renliangliang

[15] Y. Wang and S. Gower (2015). Robotic avoid­ance using Kinect and UST, ESE 497 [Online]. Available: https://sites.wustl.edu/spmrefs2016/ wanggower

[16] D. Wasseman. (2015). Grid search algorithm for set optimization, ESE 497 [Online] Available: https://sites.wustl.edu/spmrefs2016/danielwasseman

[17] D. Sehloff and C. Torres. (2015). Predicting the solar resource and power load, ESE 497 [Online]. Available: https://sites.wustl.edu/spmrefs2016/ sehloffrorres

SP

Digital Object Identifier 10.1109/MSP.2016.2621078

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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