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					 EE 313 Linear Systems and Signals          Fall 2010


                 Introduction

             Prof. Brian L. Evans
Dept. of Electrical and Computer Engineering
      The University of Texas at Austin

      Initial conversion of content to PowerPoint
              by Dr. Wade C. Schwartzkopf
          Coverage of Course
• Analysis of linear subsystems within control,
  communication, and signal processing systems
• Examples of electronic control systems?
  Antilock brakes
  Engine control
  Chemical processing plant
• Emerging trend: brake by wire
• Examples of signal processing and
  communication systems?
                                              1-2
     Signal Processing Systems
• Speech and audio                        Moving Picture
  Speech compression (cell phones)        Experts Group
  Speech synthesis and recognition          (MPEG)
  Audio CD players
  Audio compression: AC3, MPEG 1 layer 3 audio (MP3)
• Image and video compression
                                            Joint Picture
  Image compression: JPEG, JPEG 2000
                                           Experts Group
  Video CDs: MPEG 1                            (JPEG)
  DVD, digital cable, HDTV: MPEG 2
  Wireless video: MPEG 4/H.263,
    MPEG 4 Advanced Video Coding/H.264
                                                       1-3
       Communication Systems
• Digital subscriber lines (DSL)
• Cable modems
• Cellular phones
   First generation (1G): Advanced Mobile Phone Service
   Second generation (2G): Global System for Mobile (GSM)
     and Interim Standard-95 (Code Division Multiple Access)
   Third generation (3G): cdma2000, Wideband CDMA
   Fourth generation (4G): Long Term Evolution, Wi-Max
• Local area wireless Internet access
   IEEE 802.11a, b, g, n, etc. (“WiFi”)
                                                        1-4
                                    Introduction




    Related BS ECE Technical Areas
Communication/networking                           Signal/image processing
 EE345S Real-Time DSP Lab                           EE345S Real-Time DSP Lab
 EE360K Digital Comm.                               EE351M DSP (theory)
 EE371C Wireless Comm Lab                           EE371R Digital Image and
                                                      Video Processing
 EE372N Telecom. Networks
                                                     Embedded Systems
 EE379K-15 Info. Theory
                                                    EE345M Embedded and
                                                      Real-Time Systems
                                                    EE345S Real-Time DSP Lab
  Undergraduates may request
 permission to take grad courses                    EE360M Dig. Sys. Design
                                                    EE360N Computer Arch.
EE345S may be used for advanced laboratory
pre-requisite for senior design project.            EE360R VLSI CAD 1-5
            Signals as Functions
• Function of an independent variable
   Temperature vs. time
   Closing value of a stock market vs. day
• Continuous-time signals
   x(t) where t can take any real value
   x(t) may be 0 for a given range of values of t
• Discrete-time signals
   x[n] where n  {...-3,-2,-1,0,1,2,3...}
   Sometimes use “sample index” instead of “time” for n
• Values for x may be real or complex
                                                          1-6
   Analog vs. Digital Amplitude
• At each time value, analog signal amplitude takes
  real or complex value (a.k.a. continuous-valued)
                                            Analog
                                          continuous-
                                          time signal

• Digital signal amplitude takes values from a
  discrete set (a.k.a. discrete-valued)
                                     1      Digital
                                          continuous-
                                          time signal
                                     -1          1-7
Deterministic vs. Random Signals
• Deterministic signal amplitudes
   Can be mathematically described, e.g. x(t) = cos(2 p f0 t)
• Random signal amplitudes
   Cannot be predicted exactly
   Cannot be described by a mathematical function
   Distribution of amplitude values can be defined
• Consider flipping fair coin (uniform distribution)
   Let 1 be heads and -1 be tails
                                           Matlab/Mathscript Code
                                           flipnumber = 1:10;
   1
                                           y = sign(randn(10,1));
                                           stem(flipnumber, y);
                                    flip
   -1                                                           1-8

				
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posted:10/25/2011
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