|Event Type: Lecture with Exercises|
|Module Representative: Prof. Dr.-Ing. Georg Schmitz|
|Lecturer: Prof. Dr.-Ing. Georg Schmitz, research assistants|
|APPOINTMENTS IN WINTER SEMESTER|
|Start: Thursday, 10/20/2022|
|Lecture: Thursdays, 08:15 - 09.45 AM, ID 03/445|
|Exercise: Fridays, 08:15 - 09.45 AM, ID 03/445|
|Date by arrangement with the lecturer.|
|Type of Exam:||oral|
After successful completion of the module, students have a basic knowledge of acoustic field theory in fluid media, solids and piezoelectric materials. They are able to apply this knowledge to concrete physical problems and solve wave propagation problems. In doing so, they are able to analyze the given problems and make a decision on the best way to solve them (e.g. analytical calculation versus simulations). Students will be familiar with the design of medical ultrasound equipment and understand the digital signal acquisition and processing techniques used based on acoustic field theory. They can implement important signal processing algorithms themselves, apply them to measurement data and explain their solution path. The students know the most important international sources for technical literature and can use them Through the exercises in small groups, partly on computers, the students are able to practically implement what they have learned in a team, to explain approaches to solutions and to argue for them.
Imaging and therapy with ultrasound are of great importance in medical technology. In this lecture, the fundamentals of ultrasound physics and, building on this, technical elements and concepts of systems for medical diagnostics and therapy are covered. Much of the content taught on ultrasound technology is also applicable to industrial applications, such as non-destructive materials testing.
Topics of the lecture are
Knowledge of systems theory, Fourier transforms, and signal processing equivalent to those taught as fundamentals in the undergraduate electrical engineering and information technology courses.
Lecture and exercise materials will be made available via Moodle. Self-enrollment in the course is possible from 10/18/2022 with the password "Auld".