Available Student Projects
Within the Pro/Projektseminar, students will learn how to tackle a research project. They will work together with research associates and develop a detailed knowledge of a topic of current research. Here, initial MATLAB simulations are supposed to accompany the project work. Furthermore, students will learn how to effectively read and use technical literature, how to report the research finding in writing and in a seminar. The Pro/Projektseminar serves as a preparation for a Bachelor/Master thesis in the signal processing group.
In a Bachelor/Master thesis, students will get insight into a topic of current research. They will learn how to apply signal processing theory to solve reallife problems, and how to use MATLAB for implementing algorithms and carrying out simulations. Real data may be available as well, depending on the chosen topic. Students are encouraged to realise their own ideas within the project. Generally, the projects can be adapted to the student's interest. The following list provides a selection of student projects which are currently offered by the group:
6 items found. Show all theses.

Robust Localization Methods
Seminar paper, Bachelor thesis, Master thesis, Proseminar
Locationbased service is used in a great variety of contexts, such as search and rescue operations, navigation and tracking, and locationbased mobile advertising. The position information, as a key component of locationbased services, needs to be acquired from different positioning systems: the existing global satellite navigation systems and local positioning systems. The target's position can be inferred from the positionrelated measurements, such as timeofarrival. Such an inference usually relies on a statistical measurement model that characterizes the statistical relation between the measurements and the positionrelated measure, such as distance. go
Supervisor: Di Jin

Bootstrap Algorithms for Signal Processing
Seminar paper, Bachelor thesis, Master thesis, Proseminar
The bootstrap is a method for inferring the distribution of a statistic derived from a sample. It is a computerbased method, which substitutes considerable amounts of computation in place of theoretical analysis. Despite its power, the bootstrap has found little application in engineering. In most applications the bootstrap has been used to approximate the distribution or some other characteristics of an estimator. However, the bootstrap can achieve much more. For example, the bootstrap can be used to choose an estimator among a family of estimators, or to estimate the order of a linear or a nonlinear model. These problems are of high practical value in engineering. go
Supervisor: Prof. Dr.Ing. Abdelhak M. Zoubir

Robust Classification and Clustering in Wireless Sensor Networks
Seminar paper, Bachelor thesis, Proseminar
Distributed adaptive signal processing and communication networking are rapidly advancing research areas which enable new and powerful signal processing tasks, e.g., distributed speech enhancement in adverse environments. Today’s wireless sensor networks provide the possibility to monitor physical environments via small lowcost wireless devices. Given the large amount of sensed data, efficient and robust clustering and classification becomes a critical task in many applications. Typically, the devices must operate under stringent power and communication constraints and the transmission of observations to a fusion center is, in many cases, unfeasible or undesired. Furthermore, data recorded by wireless sensor networks may be affected by noise and errors. A challenging research question in such cases is the design of data clustering and classification rules when each sensor collects a set of unlabeled observations. By communicating with each other, the network of sensors achieves improved clustering and classification results compared to single node processing, while being insensitive to a certain amount of outliers or erroneous data. go
Supervisor: Dr.Ing. Michael Muma

Radar Signal Processing for Medical Gait Analysis
Seminar paper, Bachelor thesis, Master thesis, Proseminar
In the near future, radar signal processing for fall motion detection will be a key technology for assisted living. Radar systems as small as the palm of your hand will be installed in apartments to monitor the motions of the elderly person. Besides the detection of falls, it is important to assess the risk of falling, and possibly predict, and thus prevent, an upcoming fall. For this, gait analysis can be helpful. In fact, observing human gait plays a key role in many areas such as medical diagnosis, biomedical engineering, physiotherapy and rehabilitation. For example, changes in gait patterns can reveal many neurological conditions, such as Parkinson's, at an early stage.
Supervisor: AnnKathrin Seifert

Robust Regression for Estimating the Eyelid Position in Videokeratoscopic Images
Seminar paper, Bachelor thesis, Master thesis, Proseminar
The knowledge about the exact position of the eyelid in a videokeratoscopic image is useful in many ways, for example:
 As a preprocessing step for enhancing the videokeratoscopic image for a more accurate corneal topography estimation, especially for people with a small eyelid aperture.
 For studying the impact of the force caused by the eyelids on the corneal topography.
The detection of the eyelid can be divided into two steps:
 Detection of the edge points of the eyelid by using image segmentation techniques.
 Fitting of a parametric model to the detected edge points.
Supervisor: Dr.Ing. Michael Muma

Robust Model Selection for a Biomedical Application
Seminar paper, Bachelor thesis, Master thesis, Proseminar
Selecting an appropriate model from a potentially large number of candidate models is a task which is central to regression or time series modeling. Model selection thus deals with answering the question “which parameters can describe my data best?” In this context model selection criteria can be applied to find an appropriate balance between variability and complexity. A very complex model may fit the current data set well, but fails to describe subsequent data sets. A model that is too simple, on the other hand, may not fit any of the data sets. Robust model selection deals with finding appropriate models, even when the data sets contain outliers, which are defined as a minority of data points which differ substantially from the rest of the data set. go
Supervisor: Dr.Ing. Michael Muma