Signal Processing - November 2016 - 125

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 mem-
ber's lab.
More commonly, we have more stu-
dent 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 under-
graduates benefit from this because they
get more one-on-one opportunities with a
top Ph.D. student than they might other-
wise get with a busy full-time 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 multi-
semester, and the more senior undergrad-
uate students are also involved in the
mentoring. Additionally, we have a Pro-
fessor of Practice, Ed Richter, who men-
tors 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 low-level 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 navi-
gation 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 communi-
cation scheme using the National
Instruments Ethernet/IP tool kit that
allows the students to position the
robotic arm using LabVIEW or

Figure 2. An industrial robotic arm for student projects.

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

MATLAB. Then, we attached a
Microsoft XBox 360 Kinect camera to
the chassis of the robot and developed
a Kinect SDK application that pass-
es images from the camera to a
MATLAB program. The students use
this platform to calibrate the Kinect
camera to the Robotic coordinate sys-
tem 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-
IEEE Signal Processing Magazine

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November 2016

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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 64-channel
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 acqui-
sition systems that we use in our
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Table of Contents for the Digital Edition of Signal Processing - November 2016
