Signal Processing - January 2017 - 22

One of our undergraduate students who was published at
■ 5 students can build upon the work they did in the
ICASSP'14 was Jack Dagdagan (see Figure 5). In his paper
Proseminar and Project Seminar
[6], he developed a robust method for testing stationarity in the
■ 9 acquiring real data and working with it must be well
presence of outliers. Jack recollects, "In my bachelor's thesis
planned, otherwise it would exceed the three-months nomiproject, I evaluated my algorithm first with simulated data
nal length
and assumed a certain outlier model. I was not sure whether
■ 9 all of the aforementioned projects, i.e., Proseminar,
the method would still perform reasonably well when using
Project Seminar, and the bachelor's thesis project, are gradreal data. So I was very excited when I started recording real
ed after the seminar by the professor.
data. I noticed that the computational complexity was much
higher with real data, since the simulated data had a sample
Fourth and fifth year
size of only 1,024 and I recorded some seconds of audio with
The following programs and activities are part of the masa 48,000-Hz sample rate. I realized that the outliers were
ter's program of ETiT, which forms the final two-year stage
completely different to the outliers in the simulations. But
of the undergraduate education at TU Darmstadt.
I was very happy that the performance of my method was
still very good. If you develop an algorithm
DSP Practical
and evaluate with simulations, you could
Fourth-year students can attend the DSP
in their fourth and fifth
optimize by tweaking your parameters such
Practical, either in parallel to or after the
years, signal processing
that your algorithm fits the model perfectcourse "Digital Signal Processing." It
students at TU Darmstadt
ly. However, if your model is very special,
offers the chance to further familiarize
your method won't work in reality. But if
onself with signal processing programs,
are ready to tackle some
you test your algorithm on real data and still
such as MATLAB, and put theory into
more challenging and
achieve good results, it shows that either
practice. Students participating in this lab
realistic problems.
your model was very diverse or that your
are able to apply the concepts from the
algorithm runs very well independently of
lectures. This covers mainly the design of
the model you are using."
finite impulse response and infinite impulse response filters
Another lesson that Jack learned from his hands-on expeas well as parametric and nonparametric spectrum estimarience is the difference between theory and practice. "In
tion; examples of the latter are shown in Figure 6. Real-world
theory, you learn the definitions of stationarity, such as widesignals, such as speech and audio signals, touch-tone telesense stationarity (WSS)," he says. "However, in practice, you
phone dialing signals, temperature recordings, or biomedical
realize that you can never have perfect WSS. Thus, you need
measurements, are either provided to or recorded by the stuto set a threshold above which you determine the signal not
dents. UTAs help to supervise the undergraduate students
to be stationary anymore. Before my hands-on experience in
during the experiments. For example, the biomedical experithis bachelor's thesis project, I would not have thought of stament, where students record each others electrocardiogram
tionarity in this way."
(ECG) signal and perform spectrum estimation, was designed
with the help of a UTA. The experiments are conducted in the
5
■
Outstanding bachelor's thesis projects that use realSPG Lab, which is described in the "Laboratories" section.
world data can lead to publications and conference visits
The DSP Practical is composed of eight practicals and two
real-data acquisition sessions. Approximately ten groups of
two to three students work together to solve signal processing
tasks. As an introductory part for every experiment, students
receive handouts with the underlying theory and some preparatory questions. The students' understanding of the theory is
checked by the supervisor at the beginning of each experiment.
In this way, we ensure the students' adequate preparation for
the practicals. Furthermore, for every experiment, each group
writes a report in which they wrap-up their results, answer all
questions, and include plots and code from the experiment. At
the end of the semester, a final exam is held.
■ 5 Students are able to apply concepts learned in the DSP
course using real-world data
■ 5 students can further familiarize themselves with signal
processing tools, such as MATLAB
■ 5 students can acquire their own measurements
■ K the time slot per experiment is tight
FigUre 5. Jack Dagdagan, an undergraduate student at TU Darmstadt,
■ K tasks are explicitly predefined, and the time for trial and
presenting the results of his bachelor's thesis project at ICASSP'14 in
error is very limited.
Florence, Italy.
22

IEEE SIgnal ProcESSIng MagazInE

January 2017

Table of Contents for the Digital Edition of Signal Processing - January 2017