Course Analog Signals and Systems


Responsible: Prof. Dr. Rainer Bartz

Course

Meets requirements of following modules(MID)

Course Organization

Version
created 2013-06-20
VID 4
valid from WS 2012/13
valid to
Course identifiers
Long name Analog Signals and Systems
CID F07_ASS
CEID (exam identifier)

Contact hours per week (SWS)
Lecture 2
Exercise (unsplit)
Exercise (split) 2
Lab
Project
Seminar
Tutorial(voluntary) 1
Total contact hours
Lecture 30
Exercise (unsplit)
Exercise (split) 30
Lab
Project
Seminar
Tutorial (voluntary) 15
Max. capacity
Exercise (unsplit)
Exercise (split) 40
Lab
Project
Seminar

Total effort (hours): 150

Instruction language

  • German, 80%
  • English, 20%

Study Level

  • Undergraduate

Prerequisites

  • all mathematical foundation courses of the program
  • trigonometric, exponential and logarithmic functions
  • limits, infinite series, partial fraction expansion
  • differential and integral calculus
  • fundamentals of electrical engineering
  • RLC-circuits; complex numbers and functions

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

Analog Signals and Systems

Assessment

Type
wE written exam

Total effort [hours]
wE 10

Frequency: 2-3/year


Course components

Lecture/Exercise

Objectives

Contents
  • basic concepts
    • signal and system; examples
    • classification of signals
    • common signals: cos, exp, step, ramp, impulse (Dirac)
    • characteristics of signals: symmetry, energy, power, RMS
    • odd-even decomposition of signals
    • basic operations with signals: time-scaling, time-reversal, time-shift, and their combinations
    • characteristics of systems: memory, causality, stability
    • block diagrams and their components
  • signals
    • Fourier series
    • Fourier transform (1D) of CT signals
      • definition of the Fourier transform
      • Fourier transform pairs and theorems; examples
      • Parseval's theorem
      • auto-correlation function and energy density spectrum
      • cross-correlation function
    • Laplace transform
      • double-sided Laplace transform
      • the complex s-plane
      • single-sided Laplace transform
      • Laplace transform pairs and theorems; examples
      • initial and final value theorem
      • inverse transform using partial fraction expansion
      • relationship to Fourier transform
    • sampling
      • Fourier transform of impulse train
      • ideal sampling
      • spectrum of sampled signals
      • sampling theorem
      • aliasing, examples
  • systems; signal transmission
    • continuous time (CT) LTI systems
      • linear, and time-invariant (LTI) systems
      • working with block diagrams
      • impulse input and impulse response
      • step input and step response
      • convolution integral and its evaluation
      • determining characteristics of LTI systems: causality, stability
      • Bode-plot of the frequency response
        • 7 building blocks to construct a Bode-plot
      • the transfer function
      • pole-zero plot and stability
    • design of CT filter systems
      • distortionless transmission
      • basic filter types: low pass, high pass, band pass, band stop filter

Acquired Skills
  • students acquire fundamental knowledge on theory and applications of continuous-time signals and systems
  • they understand the behavior of typical systems
  • they can apply important algorithms for convolution, Fourier-, and Laplace-transform
  • they are able to model a system and to analyze it in time and frequency domain
  • they can apply system theory to real-world systems (like electrical circuits)

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

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