Logo System Theory Group

Signal & System Theory Group

Professor Peter Schreier
Universität Paderborn, Germany
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.

Medical image processing of X-ray images

Description

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.

Prerequisites
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).
Contact
Aaron Pries, [javascript protected email address]

Signal detection with application to Cognitive Radio

Description


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.

Prerequisites
Knowledge of statistical signal processing is essential and a background in digital communications is desirable. Prior experience with MATLAB or Python is helpful.
Contact
Stefanie Horstmann, [javascript protected email address]

Interference Management in Wireless Communications

Description


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.

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.
Contact
Mohammad Soleymani, [javascript protected email address]

Fusion of brain imaging data from different modalities

Description


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.

Prerequisites
Knowledge of statistical signal processing and linear algebra. Experience with MATLAB is helpful.
Contact
Tanuj Hasija, [javascript protected email address]

Beamforming strategy optimization for detection

Description


Credit: Liu Chunshan, et al. IEEE Transactions on Wireless
Communications (2017).
Beamforming is a spatial filtering technique commonly used in wireless communications and radar systems, which allows signal transmission and reception to be targeted to particular angles. It may also be applied to detection, such as a radar scanning certain airspace to detect targets, or a base station broadcasting pilot signals for itself to be detected. As illustrated in the figures, the transmitter can either use a single wide beam or a sequence of sharper beams to cover the intended angle interval. The question is, which strategy is better in the sense of detection power?






Credit: Liu Chunshan, et al. IEEE Transactions on Wireless
Communications (2017).
Under a certain power constraint, the sharper the beam is, the higher an SNR can be achieved. However, as the airspace is divided into more cells, the observation time allocated to each cell is averaged down, suggesting fewer samples will be available for each scan. The goal of the project is to figure out the best beamforming strategy for detection, namely: How many beamformers should be used to provide the optimal detection performance? To solve this, the distribution of the detector must be analyzed to investigate how its detection power is impacted by SNR and number of samples. Subsequently, optimization of its performance will be carried out to solve this problem. The work may be started with certain simpler detectors (e.g., John's test). Relevant mathematical tools will be provided to you to enable such statistical analysis.

Prerequisites
Some background in statistics and linear algebra is required. Prior experience with Matlab will also be very helpful.
Contact
Yu-Hang Xiao, [javascript protected email address]