# Digital Signal Processing

## Content

This one-semester graduate course provides a first introduction to the classical theory of digital signal processing including the following topics:

- Mathematical foundations: Complex calculus, random variables, stochastic processes
- Linear time-invariant systems
- Sampling, reconstruction, quantization
- Convolution, correlation
- Digital Filters
- Fourier Transformation

complemented by lab sessions, both pen and paper as well as practical Matlab exercises. There will be one extended Matlab assignment dealing with the processing of audio signals.

## Lecturer

## Tutor

Alexander Artiga Gonzalez

## Schedule

Lecture | Mo. | 10:00 - 11:30 | Z 613 |

Thu. | 11:45 - 13:15 | Z 613 | |

Lab Session | Tu. | 11:45 - 13:15 | Z 613 |

## 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

- Information Engineering und Computer Science, both for Bachelor Advanced Studies and Master
- Mathematics
- Physics

Preknowledge in analysis and stochastics is required.

## Prerequisites

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

- basic math courses offered in our bachelor programs
- algorithms and data structures
- introduction to computer science including programming

## 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

Matlab will be used extensively during the course and is installed locally on every computer in Z 613. For completing assignments you can use the computer pool in V 304. Remote access from home might be slow. Matlab is installed on a server:

- for Linux: /net/lin_local/matlab/bin/matlab
- for Windows: \\titan01\lin_local\matlab\win\bin\matlab.bat

## Literature / Weblinks

- Alan Oppenheim, Ronald Schafer, John Buck, Discrete-time Signal Processing, Prentice-Hall, 2010.
- Alan Oppenheim, Ronald Schafer, John Buck, Zeitdiskrete Signalverarbeitung, 2. Auflage, Pearson Studium, 2004.
- Vinay Ingke, John Proakis, Digital Signal Processing using MATLAB, Third Edition, Cengage Learning, 2012.

### Reading on Complex Calculus

An Introduction to Complex Analysis for Engineers

### 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 First MATLAB Demos
- DSP on MATLAB Central
- Getting Started with MATLAB
- Interactive Digital Signal Processing Laboratory
- Signal Processing Toolbox

### DSP Video Lectures

- Digital Signal Processing at the University of California in Berkeley, Fall 2008
- Digital Signal Processing by Prof. E. Ambikairajah
- Structure and Interpretation of Systems and Signals at the University of California in Berkeley