Lecture 7: Interpolation

Similar documents
Lecture 13: Discrete Time Fourier Transform (DTFT)

George Mason University Signals and Systems I Spring 2016

ECE 301 Fall 2010 Division 2 Homework 10 Solutions. { 1, if 2n t < 2n + 1, for any integer n, x(t) = 0, if 2n 1 t < 2n, for any integer n.

Representing a Signal

Signals & Systems. Lecture 5 Continuous-Time Fourier Transform. Alp Ertürk

Fourier series for continuous and discrete time signals

Signals & Systems. Lecture 4 Fourier Series Properties & Discrete-Time Fourier Series. Alp Ertürk

Homework: 4.50 & 4.51 of the attachment Tutorial Problems: 7.41, 7.44, 7.47, Signals & Systems Sampling P1

Lecture 3 January 23

ECE 301: Signals and Systems Homework Assignment #3

EE Homework 13 - Solutions

Problem Value

Signals and Systems Profs. Byron Yu and Pulkit Grover Fall Midterm 2 Solutions

Lecture 8: Signal Reconstruction, DT vs CT Processing. 8.1 Reconstruction of a Band-limited Signal from its Samples

4.1 Introduction. 2πδ ω (4.2) Applications of Fourier Representations to Mixed Signal Classes = (4.1)

Chapter 12 Variable Phase Interpolation

ECE 301 Division 1 Final Exam Solutions, 12/12/2011, 3:20-5:20pm in PHYS 114.

Multirate signal processing

Chap 4. Sampling of Continuous-Time Signals

Bridge between continuous time and discrete time signals

Fourier Series Representation of

GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL and COMPUTER ENGINEERING

Cubic Splines; Bézier Curves

Digital Signal Processing

Signals and Systems Spring 2004 Lecture #9

Shift-Variance and Nonstationarity of Generalized Sampling-Reconstruction Processes

5. THE CLASSES OF FOURIER TRANSFORMS

16.362: Signals and Systems: 1.0

Series FOURIER SERIES. Graham S McDonald. A self-contained Tutorial Module for learning the technique of Fourier series analysis

Final Exam ECE301 Signals and Systems Friday, May 3, Cover Sheet

Sinc Functions. Continuous-Time Rectangular Pulse

Problem Value

Question Paper Code : AEC11T02

6.003: Signals and Systems. Sampling and Quantization

Lecture 18: Stability

CH.4 Continuous-Time Fourier Series

Solution 10 July 2015 ECE301 Signals and Systems: Midterm. Cover Sheet

Chapter 2: The Fourier Transform

Ver 3808 E1.10 Fourier Series and Transforms (2014) E1.10 Fourier Series and Transforms. Problem Sheet 1 (Lecture 1)

Solutions. Number of Problems: 10

Discrete Fourier Transform

Continuous-Time Frequency Response (II) Lecture 28: EECS 20 N April 2, Laurent El Ghaoui

EE301 Signals and Systems In-Class Exam Exam 3 Thursday, Apr. 20, Cover Sheet

EE301 Signals and Systems In-Class Exam Exam 3 Thursday, Apr. 19, Cover Sheet

Chapter 5 Frequency Domain Analysis of Systems

Up-Sampling (5B) Young Won Lim 11/15/12

11.6. Parametric Differentiation. Introduction. Prerequisites. Learning Outcomes

Homework 3 Solutions

Lecture 5. The Digital Fourier Transform. (Based, in part, on The Scientist and Engineer's Guide to Digital Signal Processing by Steven Smith)

OSE801 Engineering System Identification. Lecture 05: Fourier Analysis

Assignment #09 - Solution Manual

ELEN 4810 Midterm Exam

Plane Curves and Parametric Equations

GATE EE Topic wise Questions SIGNALS & SYSTEMS

x(t) = t[u(t 1) u(t 2)] + 1[u(t 2) u(t 3)]

Chapter 5. Fourier Analysis for Discrete-Time Signals and Systems Chapter

11.6. Parametric Differentiation. Introduction. Prerequisites. Learning Outcomes

EE 16B Final, December 13, Name: SID #:

EE123 Digital Signal Processing

SIGNALS AND SYSTEMS: PAPER 3C1 HANDOUT 6a. Dr David Corrigan 1. Electronic and Electrical Engineering Dept.

Problem Score Total

Electronics and Communication Exercise 1

Final Exam January 31, Solutions

Grades will be determined by the correctness of your answers (explanations are not required).

Homework 9 Solutions

Partial Differential Equations

4.5. Applications of Trigonometry to Waves. Introduction. Prerequisites. Learning Outcomes

Final Exam of ECE301, Prof. Wang s section 8 10am Tuesday, May 6, 2014, EE 129.

Math 333 Qualitative Results: Forced Harmonic Oscillators

6.003: Signals and Systems Lecture 18 April 13, 2010

We start with a simple result from Fourier analysis. Given a function f : [0, 1] C, we define the Fourier coefficients of f by

Problem Value

Lecture 4: Oscillators to Waves

ENGI 9420 Lecture Notes 8 - PDEs Page 8.01

Assignment 3 Solutions

Review of Frequency Domain Fourier Series: Continuous periodic frequency components

ESE 531: Digital Signal Processing

Sampling Signals of Finite Rate of Innovation*

Signals and Systems: Introduction

ESE 531: Digital Signal Processing

New Mexico State University Klipsch School of Electrical Engineering EE312 - Signals and Systems I Fall 2015 Final Exam

! Downsampling/Upsampling. ! Practical Interpolation. ! Non-integer Resampling. ! Multi-Rate Processing. " Interchanging Operations

LAB 6: FIR Filter Design Summer 2011

Variational construction of periodic and connecting orbits in the planar Sitnikov problem. Mitsuru Shibayama(Kyoto University)

EE 3054: Signals, Systems, and Transforms Summer It is observed of some continuous-time LTI system that the input signal.

ESE 531: Digital Signal Processing

ECE 301 Fall 2011 Division 1. Homework 1 Solutions.

Discrete Time Fourier Transform

Topic 3: Fourier Series (FS)

The Johns Hopkins University Department of Electrical and Computer Engineering Introduction to Linear Systems Fall 2002.

Final Exam of ECE301, Section 1 (Prof. Chih-Chun Wang) 1 3pm, Friday, December 13, 2016, EE 129.

Fourier Analysis Overview (0A)

ENGI 9420 Lecture Notes 8 - PDEs Page 8.01

Homework 4. May An LTI system has an input, x(t) and output y(t) related through the equation y(t) = t e (t t ) x(t 2)dt

Signal Processing Signal and System Classifications. Chapter 13

EE 313 Linear Systems and Signals The University of Texas at Austin. Solution Set for Homework #1 on Sinusoidal Signals

Lecture 11: Spectral Analysis

Practice Problems For Test 3

Ordinary Differential Equations (ODEs) Background. Video 17

Detailed Solutions to Exercises

Transcription:

Lecture 7: Interpolation ECE 401: Signal and Image Analysis University of Illinois 2/9/2017

1 Sampling Review 2 Interpolation and Upsampling 3 Spectrum of Interpolated Signals

Outline 1 Sampling Review 2 Interpolation and Upsampling 3 Spectrum of Interpolated Signals

On-Board Practice x(t) is sampled at F s,1 = 16, 000 samples/second, creating a signal x[n]. x[n] is then played back through an ideal D/A at a different sampling rate, F s,2 = 8, 000 samples/second, to create a signal y(t). What is y(t)? x(t) = 2 + 3 cos (2000πt) + sin (20, 000πt)

Outline 1 Sampling Review 2 Interpolation and Upsampling 3 Spectrum of Interpolated Signals

Interpolation and Upsampling Today we ll learn upsampling, and four types of interpolation. 1 Upsampling: put zeros between the samples. 2 Piece-wise constant interpolation 3 Piece-wise linear interpolation 4 Piece-wise cubic spline interpolation 5 Sinc interpolation

Upsampling Upsampling changes the sampling rate by inserting zeros. Suppose x[n] is sampled at F s,1, and we want to change the sampling rate to F s,2 = MF s,1 for some integer M. Upsampling creates the signal y[n]: { x[m] n = mm y ups [n] = 0 otherwise

Piece-Wise Constant Piece-wise constant interpolation creates ( n ) y PWC [n] = x[m], m = int M where the int operator takes the integer part. PWC interpolation can also be used as a kind of D/A, to create a continuous-time signal: ( t ) y PWC (t) = x[m], m = int T where T = 1 F s is the sampling period of x[m]. A PWC signal is discontinuous once every M samples.

Piece-Wise Linear Piece-wise linear interpolation creates ( ) ( n mm n (m + 1)M y PWC [n] = g x[m] + g M M PWL can also create a continuous-time signal: ( ) ( t mt t (m + 1)T y PWC (t) = g x[m] + g T T PWL creates a continuous signal by using a continuous interpolation kernel: g(t) = max(0, 1 t ) ) x[m + 1] ) x[m + 1]

Piece-Wise Cubic Spline Piece-wise cubic spline interpolation creates y PWCS [n] = n/m+2 m=n/m 2 ( n mm g M PWCS can also create a continuous-time signal: y PWCS (t) = n/m+2 m=n/m 2 ( t mt g T ) x[m] ) x[m] PWCS creates a continuous signal with continuous first derivatives. This is done by using an interpolation function that has continuous first derivatives: g(t) = 1 t 2 0 t 1 2(2 t ) 3 2(2 t ) 2 1 t 2 0 otherwise

Sinc Interpolation Sinc interpolation creates y SINC [n] = m= ( n mm g M ) x[m] Sinc interpolation can also create a continuous-time signal: y SINC (t) = m= ( t mt g T ) x[m] Sinc interpolation creates a continuous signal with all of its derivatives continuous. It does this by using an interpolation function that has all continuous derivatives: { sin(πt) g(t) = sinc(πt) πt t 0 1 t = 0

Outline 1 Sampling Review 2 Interpolation and Upsampling 3 Spectrum of Interpolated Signals

Upsampling Suppose a cosine with period T 0 is upsampled by a factor of M: x[n] = cos (2πn/T 0 ) { x[m] n = mm y[n] = 0 otherwise Then y[n] is periodic with period MT 0.

Fourier Series of an Upsampled Cosine Since y[n] has period MT 0, it can be written with a Fourier series: y[n] = MT 0 1 k=0 Y k e jkω 0n/M, ω 0 = 2π T 0 The coefficients Y k can be derived using Fourier series formula: Y k = 1 MT 0 1 MT 0 n=0 y[n]e jkω 0n/M Since y[n] is zero except at n = mm, we can write this as: = { 1 2M Y k = 1 T 0 1 MT 0 m=0 kω 0 = ± 2π T 0 + l2π, 0 otherwise x[m]e jkω 0m any integer l

Spectrum of an Upsampled Cosine So Then y[n] has the spectrum x[n] = cos (2πn/T 0 ) { x[m] n = mm y[n] = 0 otherwise Y ω = { 1 2M ω = ± 2π MT 0 + l 2π M, 0 otherwise for any integer l

Spectrum of an Interpolated Cosine An interpolated cosine (PWC, PWL, or PWCS) has energy only at the frequencies where the upsampled cosine has energy, that is, at ω = ± 2π MT 0 + l 2π M The energy at the lowest harmonics (±2π/MT 0 ) is nearly the same for interpolation as for upsampling. The better the interpolation, the more it damps out the high-frequency harmonics: Y PWCS,ω 2 < Y PWL,ω 2 < Y PWC,ω 2 < Y UPS,ω 2, ω > 2π MT 0

Spectrum of Sinc Interpolation Sinc interpolation completely eliminates the higher harmonics. ( ) 2πm x[m] = cos y[n] = m= T 0 ( ) π(n mm) sinc x[m] M Gives the following result exactly: ( ) 2πn y[n] = cos MT 0 It works in continuous time, too: ( ) ( ) π(t mt ) 2πt y(t) = sinc x[m] = cos T TT 0 m=