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.

Slides:



Advertisements
Similar presentations
ECE 4371, Fall, 2014 Introduction to Telecommunication Engineering/Telecommunication Laboratory Zhu Han Department of Electrical and Computer Engineering.
Advertisements

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.
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE445S Real-Time Digital Signal Processing Lab Spring.
CEN352, Dr. Ghulam Muhammad King Saud University
Qassim University College of Engineering Electrical Engineering Department Course: EE301: Signals and Systems Analysis The sampling Process Instructor:
Discrete-Time Signals and Systems Linear Systems and Signals Lecture 7 Spring 2008.
Sampling of continuous-Time Signal What is sampling? How to describe sampling mathematically? Is sampling arbitrary?
Overview of Sampling Theory
Continuous-time Signal Sampling Prof. Siripong Potisuk.
Continuous-Time Convolution EE 313 Linear Systems and Signals Fall 2005 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian.
Continuous-Time Signal Analysis: The Fourier Transform
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.
Chapter 7 CT Signal Analysis : Fourier Transform Basil Hamed
Leo Lam © Signals and Systems EE235. Transformers Leo Lam ©
Differential Equations EE 313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian.
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.
Leo Lam © Signals and Systems EE235. Leo Lam © Futile Q: What did the monsterous voltage source say to the chunk of wire? A: "YOUR.
Frequency Domain Representation of Sinusoids: Continuous Time Consider a sinusoid in continuous time: Frequency Domain Representation: magnitude phase.
1 Today's lecture −Concept of Aliasing −Spectrum for Discrete Time Domain −Over-Sampling and Under-Sampling −Aliasing −Folding −Ideal Reconstruction −D-to-A.
EE513 Audio Signals and Systems Digital Signal Processing (Systems) Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
… Representation of a CT Signal Using Impulse Functions
ECE 352 Systems II Manish K. Gupta, PhD Office: Caldwell Lab ece. osu. ece. osu. edu Home Page:
EE421, Fall 1998 Michigan Technological University Timothy J. Schulz 08-Sept, 98EE421, Lecture 11 Digital Signal Processing (DSP) Systems l Digital processing.
ELEC ENG 4035 Communications IV1 School of Electrical & Electronic Engineering 1 Section 2: Frequency Domain Analysis Contents 2.1 Fourier Series 2.2 Fourier.
Chapter 5 Frequency Domain Analysis of Systems. Consider the following CT LTI system: absolutely integrable,Assumption: the impulse response h(t) is absolutely.
Signals and Systems Lecture 20: Chapter 4 Sampling & Aliasing.
Leo Lam © Signals and Systems EE235 Lecture 28.
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE345S Real-Time Digital Signal Processing Lab Fall.
Sampling Theorems. Periodic Sampling Most signals are continuous in time. Example: voice, music, images ADC and DAC is needed to convert from continuous-time.
Interpolation and Pulse Shaping
Chapter 5 Frequency Domain Analysis of Systems. Consider the following CT LTI system: absolutely integrable,Assumption: the impulse response h(t) is absolutely.
Quadrature Amplitude Modulation (QAM) Transmitter
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.
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.
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.
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.
Signals Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE 313 Linear Systems and Signals Fall 2010.
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin Lecture 4 EE 345S Real-Time.
Leo Lam © Signals and Systems EE235 Leo Lam.
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.
Generating Sinusoidal Signals Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE 445S Real-Time Digital.
ECE 352 Systems II Manish K. Gupta, PhD Office: Caldwell Lab Home Page:
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.
Leo Lam © Signals and Systems EE235. Leo Lam © Fourier Transform Q: What did the Fourier transform of the arbitrary signal say to.
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.
Common Signals Prof. Brian L. Evans
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.
Sampling and Aliasing.
Continuous-time Signal Sampling
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.
Sampling Theory ADC Types EE174 – SJSU Tan Nguyen.
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin Lecture 3
1 Digital Signal Processing (DSP) By: Prof. M.R.Asharif Department of Information Engineering University of the Ryukyus, Okinawa, Japan.
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE445S Real-Time Digital Signal Processing Lab Spring.
Sampling and Aliasing Prof. Brian L. Evans
Periodic Signals Prof. Brian L. Evans
Sampling and Aliasing Prof. Brian L. Evans
Module 3 Pulse Modulation.
Sampling and Quantization
Lecture Signals with limited frequency range
Sampling and Reconstruction
EE Audio Signals and Systems
Interpolation and Pulse Shaping
Rectangular Sampling.
CEN352, Dr. Ghulam Muhammad King Saud University
Chapter 3 Sampling.
Today's lecture System Implementation Discrete Time signals generation
Sampling and Aliasing.
DIGITAL CONTROL SYSTEM WEEK 3 NUMERICAL APPROXIMATION
State Space approach State Variables of a Dynamical System
Presentation transcript:

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 and Computer Engineering The University of Texas at Austin Sampling Theorem

Sampling: Time Domain Many signals originate as continuous-time signals, e.g. conventional music or voice By sampling a continuous-time signal at isolated, equally-spaced points in time, we obtain a sequence of numbers n  {…, -2, -1, 0, 1, 2,…} T s is the sampling period. Sampled analog waveform impulse train s(t)s(t) t TsTs TsTs

Sampling: Frequency Domain Replicates spectrum of continuous-time signal At offsets that are integer multiples of sampling frequency Fourier series of impulse train where  s = 2  f s Example  G()G() ss  s  s  s  F()F() 2  f max -2  f max Modulation by cos(2  s t) Modulation by cos(  s t)

Shannon Sampling Theorem A continuous-time signal x(t) with frequencies no higher than f max can be reconstructed from its samples x[n] = x(n T s ) if the samples are taken at a rate f s which is greater than 2 f max. Nyquist rate = 2 f max Nyquist frequency = f s /2. What happens if f s = 2f max ? Consider a sinusoid sin(2  f max t) Use a sampling period of T s = 1/f s = 1/2f max. Sketch: sinusoid with zeros at t = 0, 1/2f max, 1/f max, …

Shannon Sampling Theorem Assumption Continuous-time signal has no frequency content above f max Sampling time is exactly the same between any two samples Sequence of numbers obtained by sampling is represented in exact precision Conversion of sequence to continuous time is ideal In Practice

Why 44.1 kHz for Audio CDs? Sound is audible in 20 Hz to 20 kHz range: f max = 20 kHz and the Nyquist rate 2 f max = 40 kHz What is the extra 10% of the bandwidth used? Rolloff from passband to stopband in the magnitude response of the anti-aliasing filter Okay, 44 kHz makes sense. Why 44.1 kHz? At the time the choice was made, only recorders capable of storing such high rates were VCRs. NTSC: 490 lines/frame, 3 samples/line, 30 frames/s = samples/s PAL: 588 lines/frame, 3 samples/line, 25 frames/s = samples/s

Sampling As sampling rate increases, sampled waveform looks more and more like the original Many applications (e.g. communication systems) care more about frequency content in the waveform and not its shape Zero crossings: frequency content of a sinusoid Distance between two zero crossings: one half period. With the sampling theorem satisfied, sampled sinusoid crosses zero at the right times even though its waveform shape may be difficult to recognize

Aliasing Analog sinusoid x(t) = A cos(2  f 0 t +  ) Sample at T s = 1/f s x[n] = x(T s n) = A cos(2  f 0 T s n +  ) Keeping the sampling period same, sample y(t) = A cos(2  (f 0 + lf s )t +  ) where l is an integer y[n]= y(T s n) = A cos(2  (f 0 + lf s )T s n +  ) = A cos(2  f 0 T s n + 2  lf s T s n +  ) = A cos(2  f 0 T s n + 2  l n +  ) = A cos(2  f 0 T s n +  ) = x[n] Here, f s T s = 1 Since l is an integer, cos(x + 2  l) = cos(x) y[n] indistinguishable from x[n] Frequencies f 0 + l f s for l  0 are aliases of frequency f 0