Ein Symbolbild, das die AG repräsentiert

Digital Signal Processing


Hanhe Lin


Lecture Tu. 10:00 - 11:30 Z 613
  Th. 10:00 - 11:30 Z 613
Lab Session


11:45 - 13:15

Z 613
Exam 1 To be announced
Exam 2 To be announced

Target Audience

The course belongs to the topic areas Foundations of Computer Science and Applied Computer Science and is for students of the following degree programs

Preknowledge in analysis is required.


None, if taken as a master course. If taken as an advanced course in the bachelor program:

Course works and credits

The course ends with a final exam (either oral or written, depending on the number of course participants). 50 % of the marks for homework assignments is required for the admission to this final exam. If you earn at least 70 % of the marks for homework assignments, the final grade is 1/3 of a grade better than the exam grade. If you earn at least 90 %, the bonus is 2/3 of a grade.

Matlab Access

University of Konstanz is a member of the state-wide Matlab agreement. University staff as well as students may use the software including all tool boxes for non-commercial, academic research and education. The software may be used on university workstations as well as on private computers. More information can be found on State-wide Matlab agreement page.

Literature / Weblinks

Demo program on sampling/aliasing/prefiltering

DSP Demo

This demo, produced by Le Duan, currently runs on Mac OS X.
It is open source at

Reading on Complex Calculus

An Introduction to Complex Analysis for Engineers

Handouts on Complex Numbers

DSP Book References

Digital Signal Processing Using MATLAB

This supplement to any standard DSP text is one of the first books to successfully integrate the use of MATLAB in the study of DSP concepts. In this book, MATLAB is used as a computing tool to explore traditional DSP topics, and solve problems to gain insight. This greatly expands the range and complexity of problems that students can effectively study in the course. Since DSP applications are primarily algorithms implemented on a DSP processor or software, a fair amount of programming is required. Using interactive software such as MATLAB makes it possible to place more emphasis on learning new and difficult concepts than on programming algorithms. Interesting practical examples are discussed and useful problems are explored. This updated second edition includes new homework problems and revises the scripts in the book, available functions, and m-files to MATLAB V7.

This hands on, multi-media package provides a motivating introduction to fundamental concepts, specifically discrete-time systems, for beginning engineering readers. Designed and written by experienced and well- respected authors, this class-tested learning package can also be used as a self-teaching tool for anyone eager to discover more about DSP applications, multi-media signals, and MATLAB. Unique features, such as visual learning demonstrations, MATLAB laboratories and a bank of solved home-work problems are just a few things that make this an essential learning tool for mastering fundamental concepts in today's electrical and computer engineering institutions.

This text is derived from DSP First: A Multimedia Approach, published in 1997, which filled an emerging need for a new entry-level course not centered on analog circuits in the ECE curriculum. It was also successfully used in 80 universities as a core text for linear systems and beginning signal processing courses. This derivative product, Signal Processing First [SPF] contains similar content and presentation style, but focuses on analog signal processing.

Online Version

This is the standard text for introductory advanced undergraduate and first-year graduate level courses in signal processing. The text gives a coherent and exhaustive treatment of discrete-time linear systems, sampling, filtering and filter design, reconstruction, the discrete-time Fourier and z-transforms, Fourier analysis of signals, the fast Fourier transform, and spectral estimation. The author develops the basic theory independently for each of the transform domains and provides illustrative examples throughout to aid the reader. Discussions of applications in the areas of speech processing, consumer electronics, acoustics, radar, geophysical signal processing, and remote sensing help to place the theory in context. The text assumes a background in advanced calculus, including an introduction to complex variables and a basic familiarity with signals and linear systems theory. If you have this background, the book forms an up-to-date and self-contained introduction to discrete-time signal processing that is appropriate for students and researchers.

DSP in Matlab

DSP Video Lectures

Interactive Online Applets