Course Signal Processing

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Responsible: Prof. Dr. Rainer Bartz

Course

Meets requirements of following modules(MID)

• in active programs
  • Ba TIN2012 SIG
  • Ba TIN2010 DSS

Course Organization

Version created 2013-06-20 VID 1 valid from WS 2012/13 valid to

Course identifiers Long name Signal Processing CID F07_SIG CEID (exam identifier)

Contact hours per week (SWS) Lecture 2 Exercise (unsplit) Exercise (split) 1 Lab 1 Project Seminar Tutorial(voluntary)

Total contact hours Lecture 30 Exercise (unsplit) Exercise (split) 15 Lab 15 Project Seminar Tutorial (voluntary)

Max. capacity Exercise (unsplit) Exercise (split) 40 Lab 10 Project Seminar

Total effort (hours): 150

Instruction language

• German, 70%
• English, 30%

Study Level

• undergraduate

Prerequisites

• basic programming skills
• sequences and series
• trigonometric, exponential and logarithmic functions
• polynomial division
• limits, infinite series, partial fraction expansion
• differential and integral calculus

Textbooks, Recommended Reading

• Carlson, G. E.: Signal and Linear System Analysis, John Wiley & Sons, Inc.

Instructors

• Prof. Dr. Rainer Bartz

Supporting Scientific Staff

• tba

Transcipt Entry

Signal Processing

Assessment

Type
wE	written exam

Total effort [hours]
wE	10

Frequency: 2-3/year

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Course components

Lecture/Exercise

Objectives

Contents

• basic concepts (signal, system, characteristics)
• signals
  • discrete time reference signals (impulse, step, ...)
  • Fourier transform of discrete-time signals
  • z-transform of discrete-time signals
• systems; signal transmission
  • discrete-time (DT) LTI sytems
    • difference equations and block diagrams
    • DT convolution
    • z-transform of a delay element
    • the z-transfer function
    • responses on reference signals
    • general system responses
    • pole-zero plot and stability
    • FIR and IIR systems
  • design of DT filter systems
    • canonical system structures: DF1, DF2
    • FIR and IIR filter systems; comparison

Acquired Skills

• students acquire fundamental knowledge on theory and applications of discrete-time signals and systems
• they understand the behavior of typical systems
• they can apply algorithms for convolution, Fourier-, and z-transform, and implement them in software
• they are able to design a system, to model a system, and to analyze it in time and frequency domain
• they can apply system theory to real-world systems

Operational Competences

• students can implement a discrete-time system based on given requirements

Additional Component Assessment

Type
fPS	supervised/assisted problem solving

Contribution to course grade
fPS	not rated

Frequency: 1/year

Lab

Objectives

Contents

• sampling input and output signals of a CT system
• basic algorithms of signal processing
• software implementation of a DT system from a requirements specification

Acquired Skills

• students can use state of the art tools for system modelling, simulation, and implementation
• they understand the relationship between CT and DT systems and can explain the most important effects

Operational Competences

• students are able to solve problems in small teams
• they can analyze measurement results and extract knowledge about the underlying system
• they are able to model and simulate a real-world system
• they can detect a wrong sample rate and adjust it
• they are able to implement basic algorithms of digital signal processing

Additional Component Assessment

Type
fSC	2-3 lab experiments

Contribution to course grade
fSC	prerequisite for course exam

Frequency: 1/year