If you want to sign up for this seminar, you need to register with the following information in the form

• surname, first name,
• e-mail address,
• matriculation number,

Please note that the registration period starts 30.09.2024 at 08:00 h and ends 25.10.2024 at 23:59 h. All applications before and after this registration period will not be taken into account.

Registration will be possible within the before mentioned time by sending an e-mail with the desired seminar topic, name and matriculation number to This email address is being protected from spambots. You need JavaScript enabled to view it..

Only one student per topic is permitted (first come - first serve).

The registration is binding. A deregistration is only possible by sending an e-mail with your name and matriculation number to This email address is being protected from spambots. You need JavaScript enabled to view it. until Sunday, 27.10.2024 at 23:59 h. All later cancellations of registration will be considered as having failed the seminar.

Students write a scientific report on a topic closely related to the current research of the DSS group. Potential topics, therefore, deal with digital signal processing related to medical signal processing.

Students will also present their findings in front of the other participants and the DSS group.

Non-invasive electroanatomical imaging (EAI) can leverage advanced techniques to facilitate the reconstruction of electrical activity within the heart, providing valuable insights into cardiac arrhythmias and other conditions. In this seminar, you will explore and critically evaluate various machine learning-based approaches to EAI. Through a comparative analysis of research papers, you will investigate different algorithms, data preprocessing techniques, evaluation metrics, and validation strategies. Particular emphasis will be placed on the quality of data, the role of regularization in model training, the comparative performance of different methods, and their clinical applicability. You will assess the outcomes of these methods and how well they align with the demands of precision in non-invasive cardiac imaging.

The automatic examination of speech usually involves the extraction of various features to detect and evaluate pathological speakers. Since many factors such as age, gender, size, pitch, etc. influence the voice, even intelligible speakers can fall into pathological ranges for individual features. To obtain an overall intelligibility score that is still independent of individuals, machine learning algorithms can be trained to produce an average human intelligibility score. In this seminar, different machine learning techniques and their used data basis will be highlighted and discussed, which strengths and weaknesses such an approach entails and how such an evaluation can complement a classical feature extraction.

Ultrasound imaging is a well known and widely applied medical technology. In recent years, the tracking of such examinations has gained in interest, as a full pose reconstruction (position and orientation) of the device enables different enhancement stages, from image fusion, guided interventions to freehand 3D imaging. This can achieved using different tracking approaches, such as optical or magnetic system among many others. In this seminar, you will perform a literature review researching different options for such systems, with a focus on magnetic approaches. Additionally, image enhancement options based on tracking should be looked into, as well as supplementary tracking information garnered from the recorded images. Your goal will be a comparison of multiple approaches, highlighting advantages and disadvantages as well as general preliminary assumptions and current limitations.

Active magnetic motion sensing describes a movement feature reconstruction based on a combination of magnetic sensors and actuators. This approach is also called magnetic localization, magnetic motion tracking or indoor navigation. Size, bandwidth and magnetic noise are three common characteristics of magnetic sensors that are crucial for this type of application. In this seminar, you will do a literature review on suitable sensor types by finding at least nine further papers on the topic. You will first look for the abovement characteristics and than identify at least three more characteristics that are relevant for the application. Afterwards you will do a comparison of all identified sensor types regarding these six characteristics.