IEEE Signal Processing - May 2018 - 116

Students (%)

60
40

Student Response: Is the Module Intellectually Stimulating?
2015
2016
2017

20
0

N/A

Mostly No

Neither Mostly Yes

Yes

Figure 10. Feedback from students on whether the module was intellectually stimulating. More than
80% of students found that the assignments were engaging and intellectually stimulating.

Students (%)

60
40

Student Response: Satisfied with the Quality of the Module?
2015
2016
2017

20
0

N/A

Mostly No

Neither Mostly Yes

Yes

Figure 11. Feedback from students on their satisfaction with the module, with more than 80% of
students satisfied.

windows" to explore possible prod-
uct developments
■ gain understanding about the need
for medical ethics when working
with human subjects
■ experience that difficulties in exper-
imentation and signal analysis are
surmountable
■ explore the cross-disciplinary, ethi-
cal, and social aspects of our work
■ challenge themselves, engage their
curiosity, and have the confidence to
be intellectually adventurous.
We hope that we have shown that
bringing research into the classroom is
now not only feasible but a paradigm
shift. This has also broadened the stu-
dents' perspective on research and em-
ployment opportunities, among other
things, in next-generation health care
[13]. Hopefully our readers, motivated by
this article, will set out to further explore
the ways of enriching the signal process-
ing curriculum with relevant real-world
examples of global importance, such as
wearable health. To this end, our data
acquisition platform includes our own
116

affordable, portable, and "hackable" bio-
signal amplifier, the iAmp, a set of data
acquisition, data format, and data transfer
routines, and allows for the incorpora-
tion of other biosignal modalities (neural,
movement) into future experiments.

Acknowledgments
We thank Prof. Arye Nehorai from
Washington University in St. Louis,
Missouri, for his insightful comments.
We also appreciate help from David
Looney, Theerasak Chanwimalueang,
Ilia Kisil, Giuseppe Calvi, Tricia Adjei,
Alexander Stott, and Apit Hemakom,
who have been graduate teaching assis-
tants in our courses and were instru-
mental in the refinement and smooth
running of the exercises. Sincere thanks
are also given to the anonymous refer-
ees whose helpful comments improved
the quality of our article.

Authors
Sithan Kanna (shri.kanagasabapathy
08@imperial.ac.uk) received his M.Eng.
and Ph.D. degrees in electrical and elec-
IEEE Signal Processing Magazine

May 2018

tronic engineering from Imperial College
London, United Kingdom, in 2012 and
2016, respectively, where he was award-
ed the Rector's Scholarship for his gradu-
ate studies. His Ph.D. and postdoctoral
work involved statistical signal process-
ing and machine-learning applications in
the smart grid and eHealth. He is cur-
rently a data scientist in a management
consulting firm, working on advanced
analytics for international businesses.
Wilhelm von Rosenberg (wilhelm.
von-rosenberg12@imperial.ac.uk) re-
ceived his B.Sc. degree in physics from
Heidelberg University, Germany, in
2012. He received his M.Sc. degree in
bioengineering in 2013 and his Ph.D. de-
gree in electrical engineering in 2017,
both from Imperial College London,
United Kingdom. His research focuses
on the propagation of neural and cardiac
signals through body tissues, recording
mobile vital signs and electroencephalo-
gram, and the quantification of mental
and physical states from biosignals.
Currently, he is an algorithm developer
for autonomous vehicles.
Valentin Goverdovsky (valentin
.goverdovsky05@imperial.ac.uk) received
his M.Eng. and Ph.D. degrees in electri-
cal and electronic engineering from
Imperial College London, United King-
dom, in 2009 and 2014, respectively.
His postdoctoral work focused on ultra-
portable wearable sensing, including the
recording of the electroencephalogram
and vital signs from the ear canal, the
so-called hearables. He won the Eric
Laithwaithe Award for Excellence in
Postgraduate Research at Imperial College
London in 2014 for his postdoctoral work
and is currently with Booking.com.
Anthony G. Constantinides (a
.constantinides@imperial.ac.uk) is
emeritus professor at Imperial College
London, United Kingdom. He is a pio-
neer of signal processing and has been
actively involved in research on various
aspects of digital signal processing and
digital communications for more than
50 years. He is a Fellow of the IEEE and
the Royal Academy of Engineering and
the 2012 recipient of the IEEE Leon K.
Kirchmayer Graduate Teaching Award.
(continued on page 130)

http://www.Booking.com

Table of Contents for the Digital Edition of IEEE Signal Processing - May 2018