Chapter 6 CONTINUOUS-TIME, IMPULSE-MODULATED, AND DISCRETE-TIME SIGNALS. 6.6 Sampling Theorem 6.7 Aliasing 6.8 Interrelations
|
|
- Drusilla Byrd
- 6 years ago
- Views:
Transcription
1 Chapter 6 CONTINUOUS-TIME, IMPULSE-MODULATED, AND DISCRETE-TIME SIGNALS 6.6 Sampling Theorem 6.7 Aliasing 6.8 Interrelations Copyright c Andreas Antoniou Victoria, BC, Canada aantoniou@ieee.org September 12, 2008 Frame # 1 Slide # 1 A. Antoniou Digital Signal Processing Secs
2 Introduction In order to process a continuous-time signal using digital signal processing methodologies, it is first necessary to convert the continuous-time signal into a discrete-time signal by applying sampling. Frame # 2 Slide # 2 A. Antoniou Digital Signal Processing Secs
3 Introduction In order to process a continuous-time signal using digital signal processing methodologies, it is first necessary to convert the continuous-time signal into a discrete-time signal by applying sampling. Sampling obviously entails discarding part of the continuous-time signal and the question will immediately arise as to whether the sampling process will corrupt the signal. Frame # 2 Slide # 3 A. Antoniou Digital Signal Processing Secs
4 Introduction In order to process a continuous-time signal using digital signal processing methodologies, it is first necessary to convert the continuous-time signal into a discrete-time signal by applying sampling. Sampling obviously entails discarding part of the continuous-time signal and the question will immediately arise as to whether the sampling process will corrupt the signal. It turns out that under a certain condition that is part of the sampling theorem, the information content of the continuous-time signal can be fully preserved. Frame # 2 Slide # 4 A. Antoniou Digital Signal Processing Secs
5 The Sampling Theorem The sampling theorem states: A bandlimited signal x(t) forwhich X (jω) =0 for ω ω s 2 where ω s =2π/T, can be uniquely determined from its values x(nt ). Frame # 3 Slide # 5 A. Antoniou Digital Signal Processing Secs
6 The Sampling Theorem The sampling theorem states: A bandlimited signal x(t) forwhich X (jω) =0 for ω ω s 2 where ω s =2π/T, can be uniquely determined from its values x(nt ). Alternatively, in what amounts to the same thing, a continuous-time signal whose spectrum is zero outside the baseband (i.e., ω s /2toω s /2) can, in theory, be recovered completely from an impulse-modulated version of the signal. Frame # 3 Slide # 6 A. Antoniou Digital Signal Processing Secs
7 The Sampling Theorem Cont d Consider a two-sided bandlimited signal whose spectrum satisfies the condition of the sampling theorem. Frame # 4 Slide # 7 A. Antoniou Digital Signal Processing Secs
8 The Sampling Theorem Cont d Consider a two-sided bandlimited signal whose spectrum satisfies the condition of the sampling theorem. By virtue of Poison s summation formula, i.e., ˆX (jω) = 1 T X (jω + jnω s ) impulse modulation will produce sidebands that are well separated from one another. Frame # 4 Slide # 8 A. Antoniou Digital Signal Processing Secs
9 The Sampling Theorem Cont d X( jω) ω s 2 (a) ω s 2 ω ^ X( jω+ jω X( jω) T 1 X( jω) s ) T 1 X( jω jω s ) T 1 ω s ω s 2 (b) ω s 2 ω s ω Frame # 5 Slide # 9 A. Antoniou Digital Signal Processing Secs
10 The Sampling Theorem Cont d Now if we pass the impulse-modulated signal through an ideal lowpass filter with a frequency response { T for ω<ω s /2 H(jω) = 0 otherwise then frequencies in the sidebands will be rejected and we will be left with the frequencies in the baseband, which constitute the original continuous-time signal. Frame# 6 Slide# 10 A. Antoniou Digital Signal Processing Secs
11 The Sampling Theorem Cont d Now if we pass the impulse-modulated signal through an ideal lowpass filter with a frequency response { T for ω<ω s /2 H(jω) = 0 otherwise then frequencies in the sidebands will be rejected and we will be left with the frequencies in the baseband, which constitute the original continuous-time signal. A baseband gain of T is used to cancel out the scaling constant 1/T introduced by Poisson s summation formula. Frame# 6 Slide# 11 A. Antoniou Digital Signal Processing Secs
12 The Sampling Theorem Cont d ω s 2 (a) ^ X( jω+ jω X( jω) T 1 X( jω) s ) T 1 X( jω jω s ) T 1 ω s 2 ω ω s ω s 2 (b) H( jω) ω s 2 ω s ω T ω s 2 (c) ω s 2 ω = X( jω) ^ TX( jω) ω s 2 (d) ω s 2 ω Frame# 7 Slide# 12 A. Antoniou Digital Signal Processing Secs
13 The Sampling Theorem Cont d What has been done through a graphical illustration can now be repeated with mathematics. Frame# 8 Slide# 13 A. Antoniou Digital Signal Processing Secs
14 The Sampling Theorem Cont d What has been done through a graphical illustration can now be repeated with mathematics. If the impulse-modulated signal is passed through a lowpass filter with a frequency response H(jω) as defined before, then the Fourier transform of the output of the filter will be Y (jω) =H(jω) ˆX (jω) where H(jω) = { T for ω<ω s /2 0 otherwise and ˆX (jω) = 1 T X (jω + jnω s ) Frame# 8 Slide# 14 A. Antoniou Digital Signal Processing Secs
15 The Sampling Theorem Cont d Y (jω) =H(jω) ˆX(jω) If we apply the inverse Fourier transform, we get ] y(t) =F [H(jω) 1 x(nt )e jωnt = x(nt )F 1 [H(jω)e jωnt ] (A) Frame# 9 Slide# 15 A. Antoniou Digital Signal Processing Secs
16 The Sampling Theorem Cont d Y (jω) =H(jω) ˆX(jω) If we apply the inverse Fourier transform, we get ] y(t) =F [H(jω) 1 x(nt )e jωnt = x(nt )F 1 [H(jω)e jωnt ] (A) The frequency response of a lowpass filter is actually a frequency-domain pulse of height T and base ω s, i.e., H(jω) =Tp ωs (ω) and hence from the table of Fourier transforms, we have T sin(ω s t/2) H(jω) (B) πt Frame# 9 Slide# 16 A. Antoniou Digital Signal Processing Secs
17 The Sampling Theorem Cont d y(t) = x(nt )F 1 [H(jω)e jωnt ] T sin(ω s t/2) H(jω) πt From the time-shifting theorem of the Fourier transform T sin[ω s (t nt )/2] π(t nt ) H(jω)e jωnt (A) (B) (C) Frame # 10 Slide # 17 A. Antoniou Digital Signal Processing Secs
18 The Sampling Theorem Cont d y(t) = x(nt )F 1 [H(jω)e jωnt ] T sin(ω s t/2) H(jω) πt From the time-shifting theorem of the Fourier transform T sin[ω s (t nt )/2] π(t nt ) H(jω)e jωnt (A) (B) (C) Therefore, from Eqs. (A) and (C), we conclude that y(t) = x(nt ) sin[ω s(t nt )/2] ω s (t nt )/2 Frame # 10 Slide # 18 A. Antoniou Digital Signal Processing Secs
19 The Sampling Theorem Cont d y(t) = x(nt )F 1 [H(jω)e jωnt ] T sin(ω s t/2) H(jω) πt From the time-shifting theorem of the Fourier transform T sin[ω s (t nt )/2] π(t nt ) H(jω)e jωnt (A) (B) (C) Therefore, from Eqs. (A) and (C), we conclude that y(t) = x(nt ) sin[ω s(t nt )/2] ω s (t nt )/2 For t = nt, we have y(nt )=x(nt )forn =0, 1,..., kt, and for all other values of t the output of the lowpass filter is an interpolated version of x(t) according to the sampling theorem. Frame # 10 Slide # 19 A. Antoniou Digital Signal Processing Secs
20 Aliasing If the spectrum of the continuous-time signal does not satisfy the condition imposed by the sampling theorem, i.e., if X (jω) 0 for ω ω s 2 then sideband frequencies will be aliased into baseband frequencies. Frame # 11 Slide # 20 A. Antoniou Digital Signal Processing Secs
21 Aliasing If the spectrum of the continuous-time signal does not satisfy the condition imposed by the sampling theorem, i.e., if X (jω) 0 for ω ω s 2 then sideband frequencies will be aliased into baseband frequencies. As a result, ˆX (jω) will not be equal to X (jω)/t within the baseband. Frame # 11 Slide # 21 A. Antoniou Digital Signal Processing Secs
22 Aliasing If the spectrum of the continuous-time signal does not satisfy the condition imposed by the sampling theorem, i.e., if X (jω) 0 for ω ω s 2 then sideband frequencies will be aliased into baseband frequencies. As a result, ˆX (jω) will not be equal to X (jω)/t within the baseband. Under these circumstances, the use of an ideal lowpass filter willyieldadistortedversionofx(t) atbest. Frame # 11 Slide # 22 A. Antoniou Digital Signal Processing Secs
23 Aliasing Cont d Aliasing can be illustrated by examining an impulse-modulated signal generated by sampling the continuous-time signal x(t) =u(t)e at sin ω 0 t X( jω) ω 30 (a) Frame # 12 Slide # 23 A. Antoniou Digital Signal Processing Secs
24 Aliasing Cont d Aliasing can be illustrated by examining an impulse-modulated signal generated by sampling the continuous-time signal x(t) =u(t)e at sin ω 0 t The frequency spectrum of x(t), X (jω), extends over the infinite range <ω< X( jω) ω 30 (a) Frame # 12 Slide # 24 A. Antoniou Digital Signal Processing Secs
25 Aliasing Cont d The frequency spectrum of impulse-modulated signal ˆx(t) can be obtained as ˆX (jω) = 1 T X (jω + jnω s ) by using Poisson s summation formula. Frame # 13 Slide # 25 A. Antoniou Digital Signal Processing Secs
26 Aliasing Cont d ˆX (jω) = 1 T X (jω + jnω s ) The shifted copies of X (jω) or sidebands, namely,..., X (jω j2ω s ), X (jω jω s ), X (jω + jω s ), X (jω + j2ω s ),... overlap with the baseband ω s /2 <ω<ω s /2 and, therefore, the above sum can be expressed as where ˆX (jω) = 1 [X (jω)+e(jω)] T E (jω) = k= k 0 X (jω + jkω s ) is the contribution of the sidebands to the baseband. Frame # 14 Slide # 26 A. Antoniou Digital Signal Processing Secs
27 Aliasing Cont d Now if we filter the impulse-modulated signal, ˆx(t), using an ideal lowpass filter with a frequency response { T for ω s /2 <ω<ω s /2 H(jω) = 0 otherwise we will get a signal y(t) whose frequency spectrum is given by Y (jω) =H(jω) ˆX (jω) = H(jω) 1 T = X (jω)+e(jω) X (jω + jnω s ) Frame # 15 Slide # 27 A. Antoniou Digital Signal Processing Secs
28 Aliasing Cont d Y (jω) =X (jω)+e(jω) In other words, the output of the filter will be signal x(t) plus an error e(t) =F 1 E(jω) which is commonly referred to as the aliasing error. Frame # 16 Slide # 28 A. Antoniou Digital Signal Processing Secs
29 Aliasing Cont d With a sampling frequency of 12.5 rad/s, E (jω), i.e., the discrepancy between the solid and dashed curves in the figure is quite large X(jω) X D (e jωt ) Filtered X D (e jωt ) Frame # 17 Slide # 29 A. Antoniou Digital Signal Processing Secs
30 Aliasing Cont d As the sampling frequency is increased to 25, the sidebands are spread out and E (jω) will be decreased quite a bit as shown X(jω+jωs)/T Frame # 18 Slide # 30 A. Antoniou Digital Signal Processing Secs
31 Aliasing Cont d A further increase to 40 rad/s will render E (jω) for all practical purposes negligible as can be seen Frame # 19 Slide # 31 A. Antoniou Digital Signal Processing Secs
32 Summary of Interrelations Impulse-modulate signal: ˆx(t) = x(nt )δ(t nt ) (6.42c) Frame # 20 Slide # 32 A. Antoniou Digital Signal Processing Secs
33 Summary of Interrelations Impulse-modulate signal: ˆx(t) = x(nt )δ(t nt ) (6.42c) Spectrum of impulse modulated signal or discrete-time signal in terms of the spectrum of the original continuous-time signal: ˆX(jω) =X D (e jωt )= 1 T X (jω + jnω s ) (6.45a) where X D (e jωt )= x(nt )e jωnt Frame # 20 Slide # 33 A. Antoniou Digital Signal Processing Secs
34 Summary of Interrelations Cont d Spectrum of impulse-modulated signal (or discrete-time signal) in terms of the spectrum of the original continuous-time signal for a right-sided signal: ˆX (jω) =X D (e jωt )= x(0+) T X (jω+jnω s )(6.45b) Frame # 21 Slide # 34 A. Antoniou Digital Signal Processing Secs
35 Summary of Interrelations Cont d Spectrum of impulse-modulated signal (or discrete-time signal) in terms of the spectrum of the original continuous-time signal for a right-sided signal: ˆX (jω) =X D (e jωt )= x(0+) T X (jω+jnω s )(6.45b) Laplace transform of impulse-modulated signal in terms of the Laplace transform of the original continuous-time signal for a right-sided signal: ˆX(s) =X D (z) = x(0+) 2 where z = e st. + 1 T X (s + jnω s ) (6.46a) Frame # 21 Slide # 35 A. Antoniou Digital Signal Processing Secs
36 Summary of Interrelations Cont d Recovery of a continuous-time signal by lowpass filtering an impulse-modulated filter frequency domain: Y (jω) =H(jω) ˆX (jω) (6.48) where H(jω) = { T for ω <ω s /2 0 for ω ω s /2 Frame # 22 Slide # 36 A. Antoniou Digital Signal Processing Secs
37 Summary of Interrelations Cont d Recovery of a continuous-time signal by lowpass filtering an impulse-modulated filter frequency domain: Y (jω) =H(jω) ˆX (jω) (6.48) where H(jω) = { T for ω <ω s /2 0 for ω ω s /2 Recovery of a continuous-time signal by lowpass filtering an impulse-modulated filter time-domain: y(t) = x(nt ) sin[ω s(t nt )/2] ω s (t nt )/2 (6.51) Frame # 22 Slide # 37 A. Antoniou Digital Signal Processing Secs
38 Graphical Representation of Interrelations X(s) L L 1 jω s s jω F x(t) X( jω) F 1 Eq. (6.51) Eq. (6.42d) or (6.42e) Eq. (6.48) Eq. (6.45a) or (6.45b) x(t) ˆ F F 1 X( ˆ jω) Replace numbers by impulses Replace impulses by numbers z e jωt 1 jω ln z T x(nt ) Z Z 1 X D (z) Frame # 23 Slide # 38 A. Antoniou Digital Signal Processing Secs
39 This slide concludes the presentation. Thank you for your attention. Frame # 24 Slide # 39 A. Antoniou Digital Signal Processing Secs
Chapter 5 THE APPLICATION OF THE Z TRANSFORM Aliasing
Chapter 5 THE APPLICATION OF THE Z TRANSFORM 5.5.4 Aliasing Copyright c 2005- Andreas Antoniou Victoria, BC, Canada Email: aantoniou@ieee.org February 14, 2008 Frame # 1 Slide # 1 A. Antoniou Digital Signal
More informationDigital Signal Processing
Digital Signal Processing Lecture 9 Discrete-Time Processing of Continuous-Time Signals Alp Ertürk alp.erturk@kocaeli.edu.tr Analog to Digital Conversion Most real life signals are analog signals These
More informationSignals and Systems Lecture 6: Fourier Applications
Signals and Systems Lecture 6: Fourier Applications Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology Winter 2012 arzaneh Abdollahi Signal and Systems Lecture 6
More informationChapter 1 INTRODUCTION TO DIGITAL SIGNAL PROCESSING 1.6 Analog Filters 1.7 Applications of Analog Filters
Chapter 1 INTRODUCTION TO DIGITAL SIGNAL PROCESSING 1.6 Analog Filters 1.7 Applications of Analog Filters Copyright c 2005 Andreas Antoniou Victoria, BC, Canada Email: aantoniou@ieee.org July 14, 2018
More informationSampling of Continuous-Time Signals. Reference chapter 4 in Oppenheim and Schafer.
Sampling of Continuous-Time Signals Reference chapter 4 in Oppenheim and Schafer. Periodic Sampling of Continuous Signals T = sampling period fs = sampling frequency when expressing frequencies in radians
More informationDigital Processing of Continuous-Time Signals
Chapter 4 Digital Processing of Continuous-Time Signals 清大電機系林嘉文 cwlin@ee.nthu.edu.tw 03-5731152 Original PowerPoint slides prepared by S. K. Mitra 4-1-1 Digital Processing of Continuous-Time Signals Digital
More informationDigital Processing of
Chapter 4 Digital Processing of Continuous-Time Signals 清大電機系林嘉文 cwlin@ee.nthu.edu.tw 03-5731152 Original PowerPoint slides prepared by S. K. Mitra 4-1-1 Digital Processing of Continuous-Time Signals Digital
More informationChapter 5 THE APPLICATION OF THE Z TRANSFORM. 5.6 Transfer Functions for Digital Filters 5.7 Amplitude and Delay Distortion
Chapter 5 THE APPLICATION OF THE Z TRANSFORM 5.6 Transfer Functions for Digital Filters 5.7 Amplitude and Delay Distortion Copyright c 2005- Andreas Antoniou Victoria, BC, Canada Email: aantoniou@ieee.org
More informationOutline. Discrete time signals. Impulse sampling z-transform Frequency response Stability INF4420. Jørgen Andreas Michaelsen Spring / 37 2 / 37
INF4420 Discrete time signals Jørgen Andreas Michaelsen Spring 2013 1 / 37 Outline Impulse sampling z-transform Frequency response Stability Spring 2013 Discrete time signals 2 2 / 37 Introduction More
More informationChapter 1 INTRODUCTION TO DIGITAL SIGNAL PROCESSING. 1.1 Introduction 1.2 The Sampling Process
Chapter 1 INTRODUCTION TO DIGITAL SIGNAL PROCESSING 1.1 Introduction 1.2 The Sampling Process Copyright c 2005- Andreas Antoniou Victoria, BC, Canada Email: aantoniou@ieee.org January 31, 2008 Frame #
More informationSignals and Systems. Lecture 13 Wednesday 6 th December 2017 DR TANIA STATHAKI
Signals and Systems Lecture 13 Wednesday 6 th December 2017 DR TANIA STATHAKI READER (ASSOCIATE PROFFESOR) IN SIGNAL PROCESSING IMPERIAL COLLEGE LONDON Continuous time versus discrete time Continuous time
More informationSignals and Systems Lecture 6: Fourier Applications
Signals and Systems Lecture 6: Fourier Applications Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology Winter 2012 arzaneh Abdollahi Signal and Systems Lecture 6
More informationSampling, interpolation and decimation issues
S-72.333 Postgraduate Course in Radiocommunications Fall 2000 Sampling, interpolation and decimation issues Jari Koskelo 28.11.2000. Introduction The topics of this presentation are sampling, interpolation
More informationSampling and Signal Processing
Sampling and Signal Processing Sampling Methods Sampling is most commonly done with two devices, the sample-and-hold (S/H) and the analog-to-digital-converter (ADC) The S/H acquires a continuous-time signal
More informationRevision of Lecture 2
Revision of Lecture 2 Pulse shaping Tx/Rx filter pair Design of Tx/Rx filters (pulse shaping): to achieve zero ISI and to maximise received signal to noise ratio Combined Tx/Rx filters: Nyquist system
More informationSampling and Reconstruction of Analog Signals
Sampling and Reconstruction of Analog Signals Chapter Intended Learning Outcomes: (i) Ability to convert an analog signal to a discrete-time sequence via sampling (ii) Ability to construct an analog signal
More informationSpectrogram Review The Sampling Problem: 2π Ambiguity Fourier Series. Lecture 6: Sampling. ECE 401: Signal and Image Analysis. University of Illinois
Lecture 6: Sampling ECE 401: Signal and Image Analysis University of Illinois 2/7/2017 1 Spectrogram Review 2 The Sampling Problem: 2π Ambiguity 3 Fourier Series Outline 1 Spectrogram Review 2 The Sampling
More informationModule 3 : Sampling and Reconstruction Problem Set 3
Module 3 : Sampling and Reconstruction Problem Set 3 Problem 1 Shown in figure below is a system in which the sampling signal is an impulse train with alternating sign. The sampling signal p(t), the Fourier
More informationIntuitive Guide to Fourier Analysis. Charan Langton Victor Levin
Intuitive Guide to Fourier Analysis Charan Langton Victor Levin Much of this book relies on math developed by important persons in the field over the last 2 years. When known or possible, the authors have
More informationOther Modulation Techniques - CAP, QAM, DMT
Other Modulation Techniques - CAP, QAM, DMT Prof. David Johns (johns@eecg.toronto.edu) (www.eecg.toronto.edu/~johns) slide 1 of 47 Complex Signals Concept useful for describing a pair of real signals Let
More informationECE 301, final exam of the session of Prof. Chih-Chun Wang Saturday 10:20am 12:20pm, December 20, 2008, STEW 130,
ECE 301, final exam of the session of Prof. Chih-Chun Wang Saturday 10:20am 12:20pm, December 20, 2008, STEW 130, 1. Enter your name, student ID number, e-mail address, and signature in the space provided
More informationLecture 2 Review of Signals and Systems: Part 1. EE4900/EE6720 Digital Communications
EE4900/EE6420: Digital Communications 1 Lecture 2 Review of Signals and Systems: Part 1 Block Diagrams of Communication System Digital Communication System 2 Informatio n (sound, video, text, data, ) Transducer
More informationLECTURER NOTE SMJE3163 DSP
LECTURER NOTE SMJE363 DSP (04/05-) ------------------------------------------------------------------------- Week3 IIR Filter Design -------------------------------------------------------------------------
More informationExperiment 8: Sampling
Prepared By: 1 Experiment 8: Sampling Objective The objective of this Lab is to understand concepts and observe the effects of periodically sampling a continuous signal at different sampling rates, changing
More informationThe University of Texas at Austin Dept. of Electrical and Computer Engineering Final Exam
The University of Texas at Austin Dept. of Electrical and Computer Engineering Final Exam Date: December 18, 2017 Course: EE 313 Evans Name: Last, First The exam is scheduled to last three hours. Open
More informationChapter-2 SAMPLING PROCESS
Chapter-2 SAMPLING PROCESS SAMPLING: A message signal may originate from a digital or analog source. If the message signal is analog in nature, then it has to be converted into digital form before it can
More informationContinuous-Time Signal Analysis FOURIER Transform - Applications DR. SIGIT PW JAROT ECE 2221
Continuous-Time Signal Analysis FOURIER Transform - Applications DR. SIGIT PW JAROT ECE 2221 Inspiring Message from Imam Shafii You will not acquire knowledge unless you have 6 (SIX) THINGS Intelligence
More informationLecture Schedule: Week Date Lecture Title
http://elec3004.org Sampling & More 2014 School of Information Technology and Electrical Engineering at The University of Queensland Lecture Schedule: Week Date Lecture Title 1 2-Mar Introduction 3-Mar
More informationIslamic University of Gaza. Faculty of Engineering Electrical Engineering Department Spring-2011
Islamic University of Gaza Faculty of Engineering Electrical Engineering Department Spring-2011 DSP Laboratory (EELE 4110) Lab#4 Sampling and Quantization OBJECTIVES: When you have completed this assignment,
More informationECE 2111 Signals and Systems Spring 2012, UMD Experiment 9: Sampling
ECE 2111 Signals and Systems Spring 2012, UMD Experiment 9: Sampling Objective: In this experiment the properties and limitations of the sampling theorem are investigated. A specific sampling circuit will
More informationECE503: Digital Filter Design Lecture 9
ECE503: Digital Filter Design Lecture 9 D. Richard Brown III WPI 26-March-2012 WPI D. Richard Brown III 26-March-2012 1 / 33 Lecture 9 Topics Within the broad topic of digital filter design, we are going
More informationON LOW-PASS RECONSTRUCTION AND STOCHASTIC MODELING OF PWM SIGNALS NOYAN CEM SEVÜKTEKİN THESIS
ON LOW-PASS RECONSTRUCTION AND STOCHASTIC MODELING OF PWM SIGNALS BY NOYAN CEM SEVÜKTEKİN THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Electrical and
More informationHandout 13: Intersymbol Interference
ENGG 2310-B: Principles of Communication Systems 2018 19 First Term Handout 13: Intersymbol Interference Instructor: Wing-Kin Ma November 19, 2018 Suggested Reading: Chapter 8 of Simon Haykin and Michael
More informationFinal Exam. EE313 Signals and Systems. Fall 1999, Prof. Brian L. Evans, Unique No
Final Exam EE313 Signals and Systems Fall 1999, Prof. Brian L. Evans, Unique No. 14510 December 11, 1999 The exam is scheduled to last 50 minutes. Open books and open notes. You may refer to your homework
More informationRecall. Sampling. Why discrete time? Why discrete time? Many signals are continuous-time signals Light Object wave CCD
Recall Many signals are continuous-time signals Light Object wave CCD Sampling mic Lens change of voltage change of voltage 2 Why discrete time? With the advance of computer technology, we want to process
More informationLab 1: First Order CT Systems, Blockdiagrams, Introduction
ECEN 3300 Linear Systems Spring 2010 1-18-10 P. Mathys Lab 1: First Order CT Systems, Blockdiagrams, Introduction to Simulink 1 Introduction Many continuous time (CT) systems of practical interest can
More informationECE503: Digital Signal Processing Lecture 1
ECE503: Digital Signal Processing Lecture 1 D. Richard Brown III WPI 12-January-2012 WPI D. Richard Brown III 12-January-2012 1 / 56 Lecture 1 Major Topics 1. Administrative details: Course web page. Syllabus
More informationAdvanced Digital Signal Processing Part 2: Digital Processing of Continuous-Time Signals
Advanced Digital Signal Processing Part 2: Digital Processing of Continuous-Time Signals Gerhard Schmidt Christian-Albrechts-Universität zu Kiel Faculty of Engineering Institute of Electrical Engineering
More informationPROBLEM SET 5. Reminder: Quiz 1will be on March 6, during the regular class hour. Details to follow. z = e jω h[n] H(e jω ) H(z) DTFT.
PROBLEM SET 5 Issued: 2/4/9 Due: 2/22/9 Reading: During the past week we continued our discussion of the impact of pole/zero locations on frequency response, focusing on allpass systems, minimum and maximum-phase
More informationSampling and Reconstruction
Sampling and Reconstruction Peter Rautek, Eduard Gröller, Thomas Theußl Institute of Computer Graphics and Algorithms Vienna University of Technology Motivation Theory and practice of sampling and reconstruction
More informationPrinciples of Baseband Digital Data Transmission
Principles of Baseband Digital Data Transmission Prof. Wangrok Oh Dept. of Information Communications Eng. Chungnam National University Prof. Wangrok Oh(CNU) / 3 Overview Baseband Digital Data Transmission
More informationDigital Communication System
Digital Communication System Purpose: communicate information at certain rate between geographically separated locations reliably (quality) Important point: rate, quality spectral bandwidth requirement
More informationSIGNALS AND SYSTEMS LABORATORY 13: Digital Communication
SIGNALS AND SYSTEMS LABORATORY 13: Digital Communication INTRODUCTION Digital Communication refers to the transmission of binary, or digital, information over analog channels. In this laboratory you will
More informationYEDITEPE UNIVERSITY ENGINEERING FACULTY COMMUNICATION SYSTEMS LABORATORY EE 354 COMMUNICATION SYSTEMS
YEDITEPE UNIVERSITY ENGINEERING FACULTY COMMUNICATION SYSTEMS LABORATORY EE 354 COMMUNICATION SYSTEMS EXPERIMENT 3: SAMPLING & TIME DIVISION MULTIPLEX (TDM) Objective: Experimental verification of the
More informationDigital Signal Processing (Subject Code: 7EC2)
CIITM, JAIPUR (DEPARTMENT OF ELECTRONICS & COMMUNICATION) Notes Digital Signal Processing (Subject Code: 7EC2) Prepared Class: B. Tech. IV Year, VII Semester Syllabus UNIT 1: SAMPLING - Discrete time processing
More informationLab 4: First/Second Order DT Systems and a Communications Example (Second Draft)
ECEN 33 Linear Systems Spring 3-- P. Mathys Lab 4: First/Second Order DT Systems and a Communications Example (Second Draft Introduction The main components from which linear and time-invariant discrete-time
More informationNON-UNIFORM SIGNALING OVER BAND-LIMITED CHANNELS: A Multirate Signal Processing Approach. Omid Jahromi, ID:
NON-UNIFORM SIGNALING OVER BAND-LIMITED CHANNELS: A Multirate Signal Processing Approach ECE 1520S DATA COMMUNICATIONS-I Final Exam Project By: Omid Jahromi, ID: 009857325 Systems Control Group, Dept.
More informationWireless Communication
ECEN 242 Wireless Electronics for Communication Spring 22-3-2 P. Mathys Wireless Communication Brief History In 893 Nikola Tesla (Serbian-American, 856 943) gave lectures in Philadelphia before the Franklin
More informationLecture 7 Frequency Modulation
Lecture 7 Frequency Modulation Fundamentals of Digital Signal Processing Spring, 2012 Wei-Ta Chu 2012/3/15 1 Time-Frequency Spectrum We have seen that a wide range of interesting waveforms can be synthesized
More informationLecture 12 - Analog Communication (II)
Lecture 12 - Analog Communication (II) James Barnes (James.Barnes@colostate.edu) Spring 2014 Colorado State University Dept of Electrical and Computer Engineering ECE423 1 / 12 Outline QAM: quadrature
More informationCOMMUNICATION SYSTEMS-II (In continuation with Part-I)
MODULATING A SIGNAL COMMUNICATION SYSTEMS-II (In continuation with Part-I) TRANSMITTING SIGNALS : In order to transmit the original low frequency baseband message efficiently over long distances, the signal
More informationThe University of Texas at Austin Dept. of Electrical and Computer Engineering Midterm #2
The University of Texas at Austin Dept. of Electrical and Computer Engineering Midterm #2 Date: November 18, 2010 Course: EE 313 Evans Name: Last, First The exam is scheduled to last 75 minutes. Open books
More informationANALOGUE AND DIGITAL COMMUNICATION
ANALOGUE AND DIGITAL COMMUNICATION Syed M. Zafi S. Shah Umair M. Qureshi Lecture xxx: Analogue to Digital Conversion Topics Pulse Modulation Systems Advantages & Disadvantages Pulse Code Modulation Pulse
More informationGeneralized Trans Multiplexer
Generalized Trans Multiplexer 2006/08/24 Osamu Ichiyoshi Foreword The trans-multiplexer (TMUX) is an example to show the power of Digital Signal Processing (DSP) in communication technology. The TMUX was
More informationMultirate Signal Processing Lecture 7, Sampling Gerald Schuller, TU Ilmenau
Multirate Signal Processing Lecture 7, Sampling Gerald Schuller, TU Ilmenau (Also see: Lecture ADSP, Slides 06) In discrete, digital signal we use the normalized frequency, T = / f s =: it is without a
More informationDigital Communication System
Digital Communication System Purpose: communicate information at required rate between geographically separated locations reliably (quality) Important point: rate, quality spectral bandwidth, power requirements
More informationFinal Exam Practice Questions for Music 421, with Solutions
Final Exam Practice Questions for Music 4, with Solutions Elementary Fourier Relationships. For the window w = [/,,/ ], what is (a) the dc magnitude of the window transform? + (b) the magnitude at half
More informationMultirate Digital Signal Processing
Multirate Digital Signal Processing Basic Sampling Rate Alteration Devices Up-sampler - Used to increase the sampling rate by an integer factor Down-sampler - Used to increase the sampling rate by an integer
More informationFrequency-Domain Sharing and Fourier Series
MIT 6.02 DRAFT Lecture Notes Fall 200 (Last update: November 9, 200) Comments, questions or bug reports? Please contact 6.02-staff@mit.edu LECTURE 4 Frequency-Domain Sharing and Fourier Series In earlier
More informationThe quality of the transmission signal The characteristics of the transmission medium. Some type of transmission medium is required for transmission:
Data Transmission The successful transmission of data depends upon two factors: The quality of the transmission signal The characteristics of the transmission medium Some type of transmission medium is
More informationSignals and Systems Lecture 9 Communication Systems Frequency-Division Multiplexing and Frequency Modulation (FM)
Signals and Systems Lecture 9 Communication Systems Frequency-Division Multiplexing and Frequency Modulation (FM) April 11, 2008 Today s Topics 1. Frequency-division multiplexing 2. Frequency modulation
More informationApplication of Fourier Transform in Signal Processing
1 Application of Fourier Transform in Signal Processing Lina Sun,Derong You,Daoyun Qi Information Engineering College, Yantai University of Technology, Shandong, China Abstract: Fourier transform is a
More informationCommunication Channels
Communication Channels wires (PCB trace or conductor on IC) optical fiber (attenuation 4dB/km) broadcast TV (50 kw transmit) voice telephone line (under -9 dbm or 110 µw) walkie-talkie: 500 mw, 467 MHz
More informationBand-Limited Simulation of Analog Synthesizer Modules by Additive Synthesis
Band-Limited Simulation of Analog Synthesizer Modules by Additive Synthesis Amar Chaudhary Center for New Music and Audio Technologies University of California, Berkeley amar@cnmat.berkeley.edu March 12,
More informationFinal Exam Solutions June 14, 2006
Name or 6-Digit Code: PSU Student ID Number: Final Exam Solutions June 14, 2006 ECE 223: Signals & Systems II Dr. McNames Keep your exam flat during the entire exam. If you have to leave the exam temporarily,
More informationLine Coding for Digital Communication
Line Coding for Digital Communication How do we transmit bits over a wire, RF, fiber? Line codes, many options Power spectrum of line codes, how much bandwidth do they take Clock signal and synchronization
More informationFundamentals of Digital Communication
Fundamentals of Digital Communication Network Infrastructures A.A. 2017/18 Digital communication system Analog Digital Input Signal Analog/ Digital Low Pass Filter Sampler Quantizer Source Encoder Channel
More informationDepartment of Electronic Engineering NED University of Engineering & Technology. LABORATORY WORKBOOK For the Course SIGNALS & SYSTEMS (TC-202)
Department of Electronic Engineering NED University of Engineering & Technology LABORATORY WORKBOOK For the Course SIGNALS & SYSTEMS (TC-202) Instructor Name: Student Name: Roll Number: Semester: Batch:
More informationLaboratory Assignment 5 Amplitude Modulation
Laboratory Assignment 5 Amplitude Modulation PURPOSE In this assignment, you will explore the use of digital computers for the analysis, design, synthesis, and simulation of an amplitude modulation (AM)
More informationGeorge Mason University Signals and Systems I Spring 2016
George Mason University Signals and Systems I Spring 2016 Laboratory Project #4 Assigned: Week of March 14, 2016 Due Date: Laboratory Section, Week of April 4, 2016 Report Format and Guidelines for Laboratory
More informationMoving from continuous- to discrete-time
Moving from continuous- to discrete-time Sampling ideas Uniform, periodic sampling rate, e.g. CDs at 44.1KHz First we will need to consider periodic signals in order to appreciate how to interpret discrete-time
More informationPYKC 27 Feb 2017 EA2.3 Electronics 2 Lecture PYKC 27 Feb 2017 EA2.3 Electronics 2 Lecture 11-2
In this lecture, I will introduce the mathematical model for discrete time signals as sequence of samples. You will also take a first look at a useful alternative representation of discrete signals known
More informationCHAPTER 5. Additional Problems (a) The AM signal is defined by st () = A c. k a A c 1
CHAPTER 5 Additional Problems 5.7 (a) The AM signal is defined by st () A c ( + k a mt ()) cos( ω c k a A c + ------------ + t cos( ω c To obtain 5% modulation, we choose k a, which results in the modulated
More informationChapter 5 Amplitude Modulation. Contents
Chapter 5 Amplitude Modulation Contents Slide 1 Amplitude Modulation Slide 2 The Envelope and No Overmodulation Slide 3 Example for Single Tone Modulation Slide 4 Measuring the Modulation Index Slide 5
More informationEE5713 : Advanced Digital Communications
EE573 : Advanced Digital Communications Week 4, 5: Inter Symbol Interference (ISI) Nyquist Criteria for ISI Pulse Shaping and Raised-Cosine Filter Eye Pattern Error Performance Degradation (On Board) Demodulation
More informationEECS 216 Winter 2008 Lab 2: FM Detector Part I: Intro & Pre-lab Assignment
EECS 216 Winter 2008 Lab 2: Part I: Intro & Pre-lab Assignment c Kim Winick 2008 1 Introduction In the first few weeks of EECS 216, you learned how to determine the response of an LTI system by convolving
More informationLecture 17: BJT/FET Mixers/Mixer Noise
EECS 142 Lecture 17: BJT/FET Mixers/Mixer Noise Prof. Ali M. Niknejad University of California, Berkeley Copyright c 2005 by Ali M. Niknejad A. M. Niknejad University of California, Berkeley EECS 142 Lecture
More informationFrom Fourier Series to Analysis of Non-stationary Signals - VII
From Fourier Series to Analysis of Non-stationary Signals - VII prof. Miroslav Vlcek November 23, 2010 Contents Short Time Fourier Transform 1 Short Time Fourier Transform 2 Contents Short Time Fourier
More informationBasics of Digital Filtering
4 Basics of Digital Filtering Willis J. Tompkins and Pradeep Tagare In this chapter we introduce the concept of digital filtering and look at the advantages, disadvantages, and differences between analog
More informationSampling and Pulse Trains
Sampling and Pulse Trains Sampling and interpolation Practical interpolation Pulse trains Analog multiplexing Sampling Theorem Sampling theorem: a signal g(t) with bandwidth B can be reconstructed exactly
More informationComplex Sounds. Reading: Yost Ch. 4
Complex Sounds Reading: Yost Ch. 4 Natural Sounds Most sounds in our everyday lives are not simple sinusoidal sounds, but are complex sounds, consisting of a sum of many sinusoids. The amplitude and frequency
More informationExercises for chapter 2
Exercises for chapter Digital Communications A baseband PAM system uses as receiver filter f(t) a matched filter, f(t) = g( t), having two choices for transmission filter g(t) g a (t) = ( ) { t Π =, t,
More information2.1 BASIC CONCEPTS Basic Operations on Signals Time Shifting. Figure 2.2 Time shifting of a signal. Time Reversal.
1 2.1 BASIC CONCEPTS 2.1.1 Basic Operations on Signals Time Shifting. Figure 2.2 Time shifting of a signal. Time Reversal. 2 Time Scaling. Figure 2.4 Time scaling of a signal. 2.1.2 Classification of Signals
More informationSpeech, music, images, and video are examples of analog signals. Each of these signals is characterized by its bandwidth, dynamic range, and the
Speech, music, images, and video are examples of analog signals. Each of these signals is characterized by its bandwidth, dynamic range, and the nature of the signal. For instance, in the case of audio
More informationCS3291: Digital Signal Processing
CS39 Exam Jan 005 //08 /BMGC University of Manchester Department of Computer Science First Semester Year 3 Examination Paper CS39: Digital Signal Processing Date of Examination: January 005 Answer THREE
More informationTheory of Telecommunications Networks
Theory of Telecommunications Networks Anton Čižmár Ján Papaj Department of electronics and multimedia telecommunications CONTENTS Preface... 5 1 Introduction... 6 1.1 Mathematical models for communication
More informationMusic 270a: Fundamentals of Digital Audio and Discrete-Time Signals
Music 270a: Fundamentals of Digital Audio and Discrete-Time Signals Tamara Smyth, trsmyth@ucsd.edu Department of Music, University of California, San Diego October 3, 2016 1 Continuous vs. Discrete signals
More informationEXAMINATION FOR THE DEGREE OF B.E. Semester 1 June COMMUNICATIONS IV (ELEC ENG 4035)
EXAMINATION FOR THE DEGREE OF B.E. Semester 1 June 2007 101902 COMMUNICATIONS IV (ELEC ENG 4035) Official Reading Time: Writing Time: Total Duration: 10 mins 120 mins 130 mins Instructions: This is a closed
More informationSynthesis Techniques. Juan P Bello
Synthesis Techniques Juan P Bello Synthesis It implies the artificial construction of a complex body by combining its elements. Complex body: acoustic signal (sound) Elements: parameters and/or basic signals
More informationAliasing and Antialiasing. What is Aliasing? What is Aliasing? What is Aliasing?
What is Aliasing? Errors and Artifacts arising during rendering, due to the conversion from a continuously defined illumination field to a discrete raster grid of pixels 1 2 What is Aliasing? What is Aliasing?
More informationChapter 2: Signal Representation
Chapter 2: Signal Representation Aveek Dutta Assistant Professor Department of Electrical and Computer Engineering University at Albany Spring 2018 Images and equations adopted from: Digital Communications
More informationSignals and Systems Using MATLAB
Signals and Systems Using MATLAB Second Edition Luis F. Chaparro Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh, PA, USA AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK
More informationAnnex. 1.3 Measuring information
Annex This appendix discusses the interrelated concepts of information, information source, channel capacity, and bandwidth. The first three concepts relate to a digital channel, while bandwidth concerns
More informationCommunications IB Paper 6 Handout 3: Digitisation and Digital Signals
Communications IB Paper 6 Handout 3: Digitisation and Digital Signals Jossy Sayir Signal Processing and Communications Lab Department of Engineering University of Cambridge jossy.sayir@eng.cam.ac.uk Lent
More informationLecture 10. Digital Modulation
Digital Modulation Lecture 10 On-Off keying (OOK), or amplitude shift keying (ASK) Phase shift keying (PSK), particularly binary PSK (BPSK) Frequency shift keying Typical spectra Modulation/demodulation
More informationWeaver SSB Modulation/Demodulation - A Tutorial
Weaver SSB odulation/demodulation - A Tutorial Derek Rowell February 18, 2017 1 Introduction In 1956 D. K. Weaver 1 proposed a new modulation scheme for single-sideband-suppressedcarrier (SSB) generation.
More informationTHE CITADEL THE MILITARY COLLEGE OF SOUTH CAROLINA. Department of Electrical and Computer Engineering. ELEC 423 Digital Signal Processing
THE CITADEL THE MILITARY COLLEGE OF SOUTH CAROLINA Department of Electrical and Computer Engineering ELEC 423 Digital Signal Processing Project 2 Due date: November 12 th, 2013 I) Introduction In ELEC
More informationHandout 11: Digital Baseband Transmission
ENGG 23-B: Principles of Communication Systems 27 8 First Term Handout : Digital Baseband Transmission Instructor: Wing-Kin Ma November 7, 27 Suggested Reading: Chapter 8 of Simon Haykin and Michael Moher,
More informationHandout 2: Fourier Transform
ENGG 2310-B: Principles of Communication Systems Handout 2: Fourier ransform 2018 19 First erm Instructor: Wing-Kin Ma September 3, 2018 Suggested Reading: Chapter 2 of Simon Haykin and Michael Moher,
More informationINF4420. Switched capacitor circuits. Spring Jørgen Andreas Michaelsen
INF4420 Switched capacitor circuits Spring 2012 Jørgen Andreas Michaelsen (jorgenam@ifi.uio.no) Outline Switched capacitor introduction MOSFET as an analog switch z-transform Switched capacitor integrators
More information