Introduction Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE 313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf

1-2 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?

1-3 Signal Processing Systems Speech and audio Speech compression (cell phones) Speech synthesis and recognition Audio CD players Audio compression: AC3, MPEG 1 layer 3 audio (MP3) Image and video compression Image compression: JPEG, JPEG 2000 Video CDs: MPEG 1 DVD, digital cable, HDTV: MPEG 2 Wireless video: MPEG 4/H.263, MPEG 4 Advanced Video Coding/H.264 Moving Picture Experts Group (MPEG) Joint Picture Experts Group (JPEG)

1-4 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 a, b, g, n, etc. (“WiFi”)

1-5 EE345S may be used for advanced laboratory pre-requisite for senior design project. Related BS ECE Technical Areas Communication/networking EE345S Real-Time DSP Lab EE360K Digital Comm. EE371C Wireless Comm Lab EE372N Telecom. Networks EE379K-15 Info. Theory Undergraduates may request permission to take grad courses Signal/image processing EE345S Real-Time DSP Lab EE351M DSP (theory) EE371R Digital Image and Video Processing Embedded Systems EE345M Embedded and Real-Time Systems EE345S Real-Time DSP Lab EE360M Dig. Sys. Design EE360N Computer Arch. EE360R VLSI CAD Introduction

1-6 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

1-7 1 Analog vs. Digital Amplitude At each time value, analog signal amplitude takes real or complex value (a.k.a. continuous-valued) Digital signal amplitude takes values from a discrete set (a.k.a. discrete-valued) Analog continuous- time signal Digital continuous- time signal

1-8 Deterministic vs. Random Signals Deterministic signal amplitudes Can be mathematically described, e.g. x(t) = cos(2  f 0 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 flip 1 Matlab/Mathscript Code flipnumber = 1:10; y = sign(randn(10,1)); stem(flipnumber, y);