Continuous-Time Convolution Linear Systems and Signals Lecture 5 Spring 2008.

Slides:



Advertisements
Similar presentations
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
Advertisements

EE445S Real-Time Digital Signal Processing Lab Spring 2014 Lecture 15 Quadrature Amplitude Modulation (QAM) Transmitter Prof. Brian L. Evans Dept. of Electrical.
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE445S Real-Time Digital Signal Processing Lab Spring.
Chapter 8: The Discrete Fourier Transform
MM3FC Mathematical Modeling 3 LECTURE 3
Discrete-Time Signals and Systems Linear Systems and Signals Lecture 7 Spring 2008.
Lecture 4: Linear Systems and Convolution
Linear Time-Invariant Systems (LTI) Superposition Convolution.
Week 10. Chapter 9 1.Convolution sum & integral 1.Kronecker delta and Dirac delta 2.Impulse response and convolution 2.Impulse response & frequency response.
Question 3 A digital, time discrete channel with intersymbol interference (ISI) has the following impulse response h = [h-1, h0, h1]T = [-0.25, 1, 0.75]T.
20 October 2003WASPAA New Paltz, NY1 Implementation of real time partitioned convolution on a DSP board Enrico Armelloni, Christian Giottoli, Angelo.
Lecture 5: Linear Systems and Convolution
Lecture 6: Linear Systems and Convolution
Discrete-Time Convolution Linear Systems and Signals Lecture 8 Spring 2008.
Why to Apply Digital Transmission?
Finite Impuse Response Filters. Filters A filter is a system that processes a signal in some desired fashion. –A continuous-time signal or continuous.
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE445S Real-Time Digital Signal Processing Lab Spring.
Slides by Prof. Brian L. Evans and Dr. Serene Banerjee Dept. of Electrical and Computer Engineering The University of Texas at Austin EE345S Real-Time.
Discrete-Time Fourier Series
Discrete-Time and System (A Review)
1 Signals & Systems Spring 2009 Week 3 Instructor: Mariam Shafqat UET Taxila.
Digital Pulse Amplitude Modulation (PAM)
EE3010 SaS, L7 1/19 Lecture 7: Linear Systems and Convolution Specific objectives for today: We’re looking at continuous time signals and systems Understand.
Time-Domain Representations of LTI Systems
Chapter 3 Convolution Representation
DISCRETE-TIME SIGNALS and SYSTEMS
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE 382C-9 Embedded Software Systems Lecture 14 Communication.
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE445S Real-Time Digital Signal Processing Lab Fall.
Interpolation and Pulse Shaping
ECE 8443 – Pattern Recognition EE 3512 – Signals: Continuous and Discrete Objectives: Convolution Definition Graphical Convolution Examples Properties.
Baseband Demodulation/Detection
Signal and Systems Prof. H. Sameti Chapter #2: 1) Representation of DT signals in terms of shifted unit samples System properties and examples 2) Convolution.
Fourier Analysis of Discrete-Time Systems
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE345S Real-Time Digital Signal Processing Lab Spring.
Continuous-Time Convolution EE 313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian.
Linear Time-Invariant Systems
ECE 4710: Lecture #5 1 Linear Systems Linear System Input Signal x(t) Output Signal y(t) h(t)  H( f ) Voltage Spectrum (via FT) AutoCorrelation Function.
Representation of CT Signals (Review)
Chapter 2. Signals and Linear Systems
EE313 Linear Systems and Signals Spring 2013 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE345S Real-Time Digital Signal Processing Lab Fall.
Matched Filtering and Digital Pulse Amplitude Modulation (PAM)
Fourier Analysis of Signals and Systems
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
Course Outline (Tentative) Fundamental Concepts of Signals and Systems Signals Systems Linear Time-Invariant (LTI) Systems Convolution integral and sum.
How ADSL Modems Work Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin
Signal and System I The representation of discrete-time signals in terms of impulse Example.
Signals and Systems Analysis NET 351 Instructor: Dr. Amer El-Khairy د. عامر الخيري.
ECE 8443 – Pattern Recognition ECE 3163 – Signals and Systems Objectives: Frequency Response Response of a Sinusoid DT MA Filter Filter Design DT WMA Filter.
Linear Constant-Coefficient Difference Equations
Signals and Systems Lecture #6 EE3010_Lecture6Al-Dhaifallah_Term3321.
Description and Analysis of Systems Chapter 3. 03/06/06M. J. Roberts - All Rights Reserved2 Systems Systems have inputs and outputs Systems accept excitation.
Chapter 2. Signals and Linear Systems
Analysis of Linear Time Invariant (LTI) Systems
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin Lecture 3
Digital Signal Processing Lecture 3 LTI System
Finite Impuse Response Filters. Filters A filter is a system that processes a signal in some desired fashion. –A continuous-time signal or continuous.
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE445S Real-Time Digital Signal Processing Lab Spring.
Slides by Prof. Brian L. Evans and Dr. Serene Banerjee Dept. of Electrical and Computer Engineering The University of Texas at Austin EE445S Real-Time.
Linear Constant-Coefficient Difference Equations
Properties of LTI Systems
Lect2 Time Domain Analysis
Signal Processing First
Signal and Systems Chapter 2: LTI Systems
LECTURE 05: CONVOLUTION OF DISCRETE-TIME SIGNALS
LECTURE 07: CONVOLUTION FOR CT SYSTEMS
Finite Impulse Response Filters
Signals and Systems Lecture 18: FIR Filters.
Digital Signal Processing
Convolution sum & Integral
Presentation transcript:

Continuous-Time Convolution Linear Systems and Signals Lecture 5 Spring 2008

5 - 2 Convolution Demos Johns Hopkins University Demonstrations Convolution applet to animate convolution of simple signals and hand-sketched signals Convolve two rectangular pulses of same width gives a triangle (see handout E) Some conclusions from the animations Convolution of two causal signals gives a causal result Non-zero duration (called extent) of convolution is the sum of extents of the two signals being convolved

5 - 3 Transmit One Bit Transmission over communication channel (e.g. telephone line) is analog hh t 1 pp t A ‘1’ bit t pp -A-A ‘0’ bit Model channel as LTI system with impulse response h(t) Communication Channel inputoutput x(t)x(t)y(t)y(t) t t receive ‘1’ bit -A T h receive ‘0’ bit h+ph+p t h+ph+p hh hh Assume that T h < T p A T h

5 - 4 Transmit Two Bits (Interference) Transmitting two bits (pulses) back-to-back will cause overlap (interference) at the receiver How do we prevent intersymbol interference at the receiver? hh t 1 Assume that T h < T p t pp A ‘1’ bit ‘0’ bit pp *= -A T h t pp ‘1’ bit ‘0’ bit h+ph+p intersymbol interference

5 - 5 Transmit Two Bits (No Interference) Prevent intersymbol interference by waiting T h seconds between pulses (called a guard period) Disadvantages? hh t 1 Assume that T h < T p *= t pp A ‘1’ bit ‘0’ bit h+ph+p t -A T h pp ‘1’ bit ‘0’ bit h+ph+p hh

5 - 6 h[n]h[n] y[n]y[n]x[n]x[n] LTI system represented by its impulse response h(t)h(t) y(t)y(t)x(t)x(t) Discrete-time Convolution Preview Discrete-time convolution For every value of n, we compute a new summation Continuous-time convolution For every value of t, we compute a new integral

5 - 7 z -1 … … x[n]x[n]  y[n]y[n] h[0]h[1]h[2]h[N-1] Discrete-time Convolution Preview Assuming that h[n] has finite duration from n = 0, …, N-1 Block diagram of an implementation (finite impulse response digital filter): see slide 2-4

5 - 8 Corporate Technical Ladder Test Engineer BS degree Test other people’s designs Starting salary: $55,000 Design Engineer MS degree, or BS degree plus 2 years experience and design short courses Design new products Starting salary: $65,000 What about the Ph.D.? ¾ of Ph.D.’s to industry ¼ of Ph.D.’s to academia BSEE Tech. BSEE MSEE PhDEE 1 PhDEE 2 Technician Test Eng. Design Eng. Proj. Management Technical Staff (R&D) VP, Eng. CTO Director Eng. (1)Ph.D. based on system prototyping (2)Ph.D. with significant theoretical results