If you are interested in a thesis in statistical signal processing/communications/biomedicine, but can't find a
suitable topic below, please feel free to contact us.
Übrigens: Auch wenn ein Thema auf Englisch ausgeschrieben ist, können Sie Ihre Arbeit auf Deutsch schreiben.
Bachelor/Master's level
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Description
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We develop deep learning based approaches to solve medical image processing problems.
A frequent problem is the localization of specific anatomical body parts, for example
the femur (thigh bone) or the jaw. In medical problems, usually only a small dataset is
available and the images can have low quality. That applies to the analysis of fluoroscopic
(low-dose) X-ray images, where the images have low contrast. We address these challenges by
incorporating high-level information about the objects, for example simple geometrical models
or more complex statistical models.
In this thesis, you will contribute to the development of algorithms that identify patterns in bone
structures or implants using (statistical) models. The exact problem can be varied, examples could be
the segmentation of specific bone structures or the classification of bone fractures.
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Prerequisites
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Knowledge of signal theory and statistics is required, a background in image
processing is desirable but not essential. Programming skills (MATLAB or Python) are required.
The scope of the project will be adjusted to match
the level of offering (bachelor or master).
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Contact
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Aaron Pries, [javascript protected email address]
Bachelor/Master's level
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Description
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Credit: Wang et al., IEEE Journal of Selected Topics in
Signal Processing (2011)
Since the usable radio spectrum is of limited physical extent it is a resource of high demand. To manage its
access it is divided into sub-bands and has licenses assigned to them. However, the licensed frequency bands
are typically underutilized. In order to improve the wireless spectrum utilization, cognitive radio (CR) is
a new communications paradigm that manages the spectrum access dynamically. Hence, unused license bands are
accessed by “cognitive” users opportunistically. To provide interference-free communication for
all users vacant frequency sub-bands have to be detected reliably.
Credit: Wang et al., IEEE Journal of Selected Topics in
Signal Processing (2011)
A robust spectrum sensing technique is based on cyclostationary (CS) feature detection. Since digital
communication schemes produce CS signals, this property can be exploited when deciding whether a given
frequency sub-band is occupied. In practice, however, we have to deal with almost-CS signals,
which are much trickier to detect. In this thesis, you will look at detectors of (almost-)
cyclostationarity. Theoretical questions can include the implementation of multiple hypothesis tests to
detect the presence of an (almost-) CS signal or the estimation of the signal's cycle period.
Alternatively, practical aspects can be explored by implementing different detectors on a hardware
testbed and evaluating their performance under realistic conditions.
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Prerequisites
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Knowledge of statistical signal processing is essential and a background in digital communications is desirable.
Prior experience with MATLAB or Python is helpful.
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Contact
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Stefanie Horstmann, [javascript protected email address]
Master's level
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Description
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Credit: Zahir, Talha, et al.,
IEEE communications surveys & tutorials (2013)
Interference from other transmitters is the main bottleneck for most wireless communication systems, which
are called interference-limited systems. Due to the bandwidth shortage, these systems must tolerate
some interference in order to enhance the spectrum usage. There are different approaches to handle
interference in wireless systems. Among them is employing improper signaling. In improper signals, the
powers of the real and imaginary parts are not equal, and/or there is correlation between the real and
imaginary parts of the signal. The capacity-achieving signal in traditional systems is a proper signal;
however, it has been shown that improper signaling can increase the rate in interference-limited systems.
Credit: Yucek, Tevfik, and Huseyin, Arslan,
IEEE communications surveys & tutorials (2009)
In this thesis, you will study the performance of an interference management technique in a wireless
communication system. To this end, we first define a scenario in which the system is
interference-limited. For instance, we can consider an underlay/overlay cognitive radio (CR) system. In
CR systems, licensed or primary users (PU) share the spectrum with unlicensed or secondary users (SU)
under the constraint that the PU's communications are not affected by the SU's transmission. In such
system, we should design the transmission strategy of SU to get the desired system performance.
Moreover, different transmission techniques (e.g., OFDM and single carrier) can be considered in the
thesis. You will consider different interference management techniques including improper signaling and
interference alignment. Then, you will optimize the performance of the defined scenario by analytical
tools. Finally, you will evaluate your results by simulations using MATLAB.
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Prerequisites
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Knowledge of wireless communications and mathematical tools, i.e., linear algebra and/or optimization methods is
required. Depending on your background, scope and focus of the task can be adjusted. Some prior experience with
MATLAB is also expected.
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Contact
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Mohammad Soleymani, [javascript protected email address]
Bachelor/Master's level
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Description
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Object segmentation within images is an easy task for a human but so much more
difficult for a computer/machine. It becomes especially challenging when the object
to be segmented belongs to the human body since anatomy has a tremendous
variability among individuals. This is the case in Computer Assisted Surgery
(CAS) applications for the treatment of hip fracture where the femur bone must
be segmented. In this case the task is even harder as the images are generated with
a low dose X-ray source. The results are noisy and have low-contrast. Landmarkbased
segmentation is a promising approach to solve this problem: given prior
knowledge of the position and local appearance of landmarks (right figure), a
supervised segmentation method finds the position of these landmarks in new
images. In the last couple of decades Active Shape Model algorithm has been
widely used for this purpose. It uses a statistical shape model learned from annotated
training images (see figure below).
One of the most important steps in the Active Shape Model approach is the annotation
of the landmarks in the training images, which is generally done manually.
The most common choice is to use anatomical points, for instance “place landmark
24 always in the beginning of the femoral head sphere”. However, this strategy is
only based on experience tip and is not optimal in any sense. In this thesis you
will use femur shape contours from a set of training images and then develop an
algorithm for automatic placement of the landmarks, which is in some sense optimal.
The aim is to place points so that the statistical shape model best captures
the bone variation among individuals, but with minimal representation error and
maximum segmentation performance.
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Prerequisites
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Knowledge of signal and probability theory; a background in image processing
is desirable but not essential. Programming skills (at least MATLAB). A desire to
learn and explore new fields is the most important prerequisite. The scope of the
project will be adjusted to match the level of offering (bachelor or master).
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Contact
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Alma Eguizabal, [javascript protected email address]
Master's level
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Description
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Credit: blogs.scientificamerican.com
In biomedical imaging for the study of brain function, an increasing number of studies are collecting
multiple measurements from different modalities, in particular, functional magnetic resonance
imaging (fMRI), electroencephalography (EEG), and structural MRI (sMRI). All of these are noninvasive brain
imaging techniques: fMRI measures the changes in blood-oxygenation in the brain,
EEG measures the brain electrical field through the scalp, and sMRI provides information about the type of
brain tissue. These modalities provide complementary information. For instance, fMRI
has very good spatial resolution, but bad temporal resolution, whereas EEG has high temporal resolution but
poor spatial localization. It is thus of interest to fuse the measurements obtained from
these different techniques to combine their respective advantages.
Approaches for data fusion can be classified as either model based or data driven. Model-based
approaches require detailed a priori knowledge about the experiment to be performed and
the properties of the data. When performing complex experiments, the underlying dynamics become
very difficult to model, in which case data-driven analysis methods (e.g., correlation analysis
techniques such as CCA) are to be preferred. In this thesis, you will investigate different data-driven
techniques for making
group inferences. For instance, we might be interested in analyzing fMRI data from the same
subjects scanned at different alcohol levels while performing a given task.
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Prerequisites
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Knowledge of statistical signal processing and linear algebra. Experience with MATLAB is helpful.
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Contact
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Tanuj Hasija, [javascript protected email address]
Master's level
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Description
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Due to the broadcast nature of the wireless medium, interference between users presents the major limitation
in wireless communications. Typically, interference is avoided by assigning each user a different time-slot
or frequency band (TDMA and FDMA, respectively). From an information theoretic standpoint, however, the
performance could be substantially improved by interference coordination techniques. Such techniques are
based on user cooperation, whereby transmitters adjust their parameters (transmit power, transmit
directions, etc.) exploiting information about the other users (typically the channel responses, which must
be previously acquired), with the goal of minimizing the impact of the interference.
Among the different interference coordination techniques, improper signaling, which is the transmission
of signals with correlated in-quadrature and in-phase signals, is increasing in popularity due to the
fact that it can be used in conjunction with other techniques and without incurring any additional cost.
In this thesis, you will analyze the benefits of improper signaling for different relevant multiuser
networks. A special focus will be devoted to the analysis and development of algorithms for both
single-antenna and multiple-antenna systems.
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Prerequisites
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It is essential to have some knowledge of linear algebra and communications. Some prior experience with Matlab
is also important.
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Contact
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Christian Lameiro, [javascript protected email address]
