Course Diskrete-Time Signals and Systems

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Responsible: Prof. Dr. Rainer Bartz

Course

Meets requirements of following modules(MID)

• in active programs
  • Ba ET2012 DSS
  • Ba ET2010 DSS
  • Ba TIN2010 DSS

Course Organization

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

Course identifiers Long name Diskrete-Time Signals and Systems CID F07_DSS 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, 80%
• English, 20%

Study Level

• undergraduate

Prerequisites

• content of F07_ASS
• sequences and series

Textbooks, Recommended Reading

• Carlson, G. E.: Signal and Linear System Analysis, John Wiley & Sons, Inc.
• Girod, B.: Einführung in die Systemtheorie, Teubner Verlag
• von Grünigen, D. Ch.: Digitale Signalverarbeitung, Fachbuchverlag Leipzig
• Hsu, H.P.: Signals and Systems, Schaums Outlines
• Meyer, M.: Signalverarbeitung, Verlag Vieweg
• Ohm, J.-R.; Lüke, H. D.: Signalübertragung, Springer-Verlag
• Oppenheim, A.V.; Wilsky, A.S.:Signals & Systems, Prentice Hall
• Werner, M.: Signale und Systeme, Verlag Vieweg

Instructors

• Prof. Dr. Rainer Bartz
• Prof. Dr. Harald Elders-Boll
• Prof. Dr. Andreas Lohner

Supporting Scientific Staff

• Dipl.-Ing. Martin Seckler
• Dipl.-Ing. Norbert Kellersohn

Transcipt Entry

Diskrete-Time Signals and Systems

Assessment

Type
wE	written exam

Total effort [hours]
wE	10

Frequency: 2-3/year

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Course components

Lecture/Exercise

Objectives

Contents

• signals
  • Fourier transform (DTFT) of discrete-time signals
    • theorems and examples
  • discrete Fourier transform (DFT)
    • derivation and definition of the DFT (and inverse DFT)
    • resolution in time and frequenzy domain
  • z-transform
    • single-sided z-transform
    • z-transform pairs and theorems
    • initial and final value theorem
    • inverse transform using partial fraction expansion
    • time signal evaluation through power series expansion
    • relationship to DTFT
• systems; signal transmission
  • discrete-time (DT) LTI sytems
    • difference equations and block diagrams
    • DT unit impulse and impulse response
    • DT step and step response
    • DT convolution
    • z-transform of a delay element
    • the z-transfer function
    • pole-zero plot and stability
    • FIR and IIR systems
  • design of DT filter systems
    • canonical system structures: DF1, DF2
    • ideal DT low pass filter
    • design of IIR filter
    • design of FIR filter
    • comparison between FIR and IIR filter

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
• 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
fAP	(optional) assessed problem solving
fSP	supervised/assisted problem solving

Contribution to course grade
fAP	(if offered) rated: 20%
fSP	not rated

Frequency: 1/year

Lab

Objectives

Contents

• sampling input and output signals of a CT system
• basic algorithms of signal processing
• design of a small system from a requirements specification

Acquired Skills

• students can use state of the art tools for system modelling and simulation
• 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