The following are some examples of topics we offer for Bachelor's and Master's theses. If you cannot find a suitable topic but would like to work on something related to data science, machine learning, or statistical signal processing, with applications in mobile communications, neuroscience, or medicine, please talk to us.
Übrigens: Wir schreiben unsere Themen generell auf Englisch aus. Sie dürfen Ihre Abschlussarbeit bei uns aber auch gerne auf Deutsch schreiben.
Fusion of brain imaging data from different modalities
suitable for: Master's students
Prerequisites: Knowledge of statistical signal processing and linear algebra. Experience with MATLAB or Python is helpful.
Image Credit: shutterstock.com
Contact: Tanuj Hasija
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.
Tensor Decompositions for classification of patients with epilepsy and healthy controls based on medical data
suitable for: Bachelor's students
Prerequisites: Knowledge of linear algebra. Experience with MATLAB or Python is helpful.
3-dimensional tensor with 3 modes: subjects (i.e., patients), time, channels
Contact: Isabell Lehmann
More than 65 million people worldwide, which is nearly 1% of the world population, suffer from epilepsy. Epilepsy is a disease that causes uncontrolled seizures, which are sudden voltage discharges in the brain. These discharges can cause changes in behavior, movements or feelings, and in levels of consciousness. Under physical exhaustion, people with epilepsy (PWE) show an increased risk for cardiac death. Since the heart is under direct control of the brain through the autonomic nervous system, this thesis aims to investigate whether the brain reacts differently in an exhaustive exercise situation for PWE compared to healthy controls.
The goal of this thesis is to detect exercise-related patterns that differentiate between PWE and healthy controls using statistical signal processing and machine learning techniques, in particular, tensor decompositions. Tensors are multidimensional arrays, which preserve the higher-dimensional structure of the data. For example, time-series data collected from different subjects and channels can naturally be stored in a 3-dimensional tensor (see figure on the left). If meaningful patterns are found during the analysis, they can be used as features for classifying PWE and controls using machine learning techniques.
Interference management in wireless communication
suitable for: Master's students
Prerequisites: 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.
Image Credit: Zahir, Talha, et al., IEEE Communications Surveys & Tutorials (2013) and Yucek, Tevfik, and Huseyin, Arslan, IEEE Communications Surveys & Tutorials (2009)
Contact: Mohammad Soleymani
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.
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.