Fourier Series for Periodic Functions. Lecture #8 5CT3,4,6,7. BME 333 Biomedical Signals and Systems - J.Schesser

Size: px
Start display at page:

Download "Fourier Series for Periodic Functions. Lecture #8 5CT3,4,6,7. BME 333 Biomedical Signals and Systems - J.Schesser"

Transcription

1 Fourier Series for Periodic Functions Lecture #8 5C3,4,6,7

2 Fourier Series for Periodic Functions Up to now we have solved the problem of approximating a function f(t) by f a (t) within an interval. However, if f(t) is periodic with period, i.e., f(t)=f(t+), then the approximation is true for all t. And if we represent a periodic function in terms of an infinite Fourier series, such that the frequencies are all integral multiples of the frequency /, where = corresponds to the fundamental frequency of the function and the remainder are its harmonics. a f t e dt j t () jt j t j t j t a a a a j t j a( ) cos( ), where a and a f() t a [ e * e ] e a Re[ e ] f t a C C e C 3

3 Another Form for the Fourier Series t f() t C C cos( ) t t A A cos( ) B sin( ) where A C cos and B -C sin and A C 4

4 Fourier Series heorem Any periodic function f (t) with period which is integrable ( f (t)dt ) can be represented by an infinite Fourier Series If [f (t)] is also integrable, then the series converges to the value of f (t) at every point where f(t) is continuous and to the average value at any discontinuity. 5

5 Symmetries Properties of Fourier Series If f (t) is even, f (t)=f (-t), then the Fourier Series contains only cosine terms If f (t) is odd, f (t)=-f (-t), then the Fourier Series contains only sine terms If f (t) has half-wave symmetry, f (t) = -f (t+/), then the Fourier Series will only have odd harmonics If f (t) has half-wave symmetry and is even, even quarter-wave, then the Fourier Series will only have odd harmonics and cosine terms If f (t) has half-wave symmetry and is odd, odd quarterwave,then the Fourier Series will only have odd harmonics and sine terms 6

6 Properties of FS Continued Superposition holds, if f(t) and g(t) have coefficients f and g, respectively, then Af(t)+Bg(t)=>Af +Bg j t ime Shifting: f() t ae Differentiation and Integration delayed original jt jt jt j t f( tt ) a e e a e e ( a ) ( a ) e j t j ( a) derivative ( a) ( a) integral ( a) j original original 7

7 FS Coefficients Calculation Example E jt jt jt a [ ] 4 e dt Ee dt e dt j t E 4 e 4 j 4 j j j j [ e ] [ ] E Ee e e j j sin( ) j E 4 4 e, 4 4 E E a dt 4 4 f ( t), t f ( t) f ( t) E, t f ( t), t f ( t 4) Since a C, then sin E E 4 t f() t cos( )

8 Example Continued.5 term terms

9 Frequency Spectrum of the Pulse Function In the preceding example, the coefficients for each of the cosine terms was proportional to sin( 4) ( 4) We call the function Sa( x) sin x x the Sampling Function. If we plot these coefficients along the.8.6 frequency axis we have.4. the frequency spectrum of f(t)

10 Frequency Spectrum Note that the periodic signal in the time domain exhibits a discrete spectrum (i.e., in the frequency domain) Frequency (/4)

11 Another Example V his signal is odd, H/W symmetrical, and mean=; this means no cosine terms, odd harmonics and mean=. jt jt [ ] jt jt e e j j a Ve dt Ve dt V [ V V [ cos ] for odd, j j V 9 for odd. for even. f ( t) odd V odd V sin( t ) cos( t )

12 Fourier Series of an Impulse rain or Sampling Function xt () ( tn) jt a x() t e dt n () te jt dt for all xt () e jt 3

13 4 Fourier Series for Discrete Periodic Functions ] [ ] [ n j n n j n j n j n e x a e a e a e a a n x Notice the similarities to the previous example. Since x[n] is discrete, this becomes a summation

14 Homewor Problem () Compute the Fourier Series for the periodic functions a) f(t) = for <t<π, f(t)= for π<t<π b) f(t) = t for <t<3 Problem () Compute the Fourier series of the following Periodic Functions: f(t)= t, nπ < t < (n+)π for n =, (n+)π < t < (n+)π for n f(t)= e -t/π, nπ < t < (n+)π for n Use Matlab to plot f(t) usinga for maximum number of components, N=5,,, and. Show your code. Problem (3) Problems: 4./3 Find the Fourier series of the following waveforms (choose t c = o /4): o -( o + t c ) - ( o -t c ) - o / -t c t c o / o o -t c o + t c 5

15 Homewor Problem (4) Deduce the Fourier series for the functions shown (hint: deduce the second one using superposition): -π+a -a π+a π-a -π -π/3 -π/3 4π/3 5π/3 -π a π-a π π π/3 π/3 π π 5C.7. 6

SGN-1158 Introduction to Signal Processing Test. Solutions

SGN-1158 Introduction to Signal Processing Test. Solutions SGN-1158 Introduction to Signal Processing Test. Solutions 1. Convolve the function ( ) with itself and show that the Fourier transform of the result is the square of the Fourier transform of ( ). (Hints:

More information

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

Series FOURIER SERIES. Graham S McDonald. A self-contained Tutorial Module for learning the technique of Fourier series analysis Series FOURIER SERIES Graham S McDonald A self-contained Tutorial Module for learning the technique of Fourier series analysis Table of contents Begin Tutorial c 004 g.s.mcdonald@salford.ac.uk 1. Theory.

More information

Frequency Response of FIR Filters

Frequency Response of FIR Filters Frequency Response of FIR Filters Chapter 6 This chapter continues the study of FIR filters from Chapter 5, but the emphasis is frequency response, which relates to how the filter responds to an input

More information

f x a 0 n 1 a 0 a 1 cos x a 2 cos 2x a 3 cos 3x b 1 sin x b 2 sin 2x b 3 sin 3x a n cos nx b n sin nx n 1 f x dx y

f x a 0 n 1 a 0 a 1 cos x a 2 cos 2x a 3 cos 3x b 1 sin x b 2 sin 2x b 3 sin 3x a n cos nx b n sin nx n 1 f x dx y Fourier Series When the French mathematician Joseph Fourier (768 83) was tring to solve a problem in heat conduction, he needed to epress a function f as an infinite series of sine and cosine functions:

More information

Pulsed Fourier Transform NMR The rotating frame of reference. The NMR Experiment. The Rotating Frame of Reference.

Pulsed Fourier Transform NMR The rotating frame of reference. The NMR Experiment. The Rotating Frame of Reference. Pulsed Fourier Transform NR The rotating frame of reference The NR Eperiment. The Rotating Frame of Reference. When we perform a NR eperiment we disturb the equilibrium state of the sstem and then monitor

More information

Graphs of Polar Equations

Graphs of Polar Equations Graphs of Polar Equations In the last section, we learned how to graph a point with polar coordinates (r, θ). We will now look at graphing polar equations. Just as a quick review, the polar coordinate

More information

Analog and Digital Signals, Time and Frequency Representation of Signals

Analog and Digital Signals, Time and Frequency Representation of Signals 1 Analog and Digital Signals, Time and Frequency Representation of Signals Required reading: Garcia 3.1, 3.2 CSE 3213, Fall 2010 Instructor: N. Vlajic 2 Data vs. Signal Analog vs. Digital Analog Signals

More information

10 Discrete-Time Fourier Series

10 Discrete-Time Fourier Series 10 Discrete-Time Fourier Series In this and the next lecture we parallel for discrete time the discussion of the last three lectures for continuous time. Specifically, we consider the representation of

More information

Chapter 8 - Power Density Spectrum

Chapter 8 - Power Density Spectrum EE385 Class Notes 8/8/03 John Stensby Chapter 8 - Power Density Spectrum Let X(t) be a WSS random process. X(t) has an average power, given in watts, of E[X(t) ], a constant. his total average power is

More information

chapter Introduction to Digital Signal Processing and Digital Filtering 1.1 Introduction 1.2 Historical Perspective

chapter Introduction to Digital Signal Processing and Digital Filtering 1.1 Introduction 1.2 Historical Perspective Introduction to Digital Signal Processing and Digital Filtering chapter 1 Introduction to Digital Signal Processing and Digital Filtering 1.1 Introduction Digital signal processing (DSP) refers to anything

More information

9 Fourier Transform Properties

9 Fourier Transform Properties 9 Fourier Transform Properties The Fourier transform is a major cornerstone in the analysis and representation of signals and linear, time-invariant systems, and its elegance and importance cannot be overemphasized.

More information

Review of Fourier series formulas. Representation of nonperiodic functions. ECE 3640 Lecture 5 Fourier Transforms and their properties

Review of Fourier series formulas. Representation of nonperiodic functions. ECE 3640 Lecture 5 Fourier Transforms and their properties ECE 3640 Lecture 5 Fourier Transforms and their properties Objective: To learn about Fourier transforms, which are a representation of nonperiodic functions in terms of trigonometric functions. Also, to

More information

Scientific Programming

Scientific Programming 1 The wave equation Scientific Programming Wave Equation The wave equation describes how waves propagate: light waves, sound waves, oscillating strings, wave in a pond,... Suppose that the function h(x,t)

More information

Lecture 7 ELE 301: Signals and Systems

Lecture 7 ELE 301: Signals and Systems Lecture 7 ELE 3: Signals and Systems Prof. Paul Cuff Princeton University Fall 2-2 Cuff (Lecture 7) ELE 3: Signals and Systems Fall 2-2 / 22 Introduction to Fourier Transforms Fourier transform as a limit

More information

Appendix D Digital Modulation and GMSK

Appendix D Digital Modulation and GMSK D1 Appendix D Digital Modulation and GMSK A brief introduction to digital modulation schemes is given, showing the logical development of GMSK from simpler schemes. GMSK is of interest since it is used

More information

Sampling and Interpolation. Yao Wang Polytechnic University, Brooklyn, NY11201

Sampling and Interpolation. Yao Wang Polytechnic University, Brooklyn, NY11201 Sampling and Interpolation Yao Wang Polytechnic University, Brooklyn, NY1121 http://eeweb.poly.edu/~yao Outline Basics of sampling and quantization A/D and D/A converters Sampling Nyquist sampling theorem

More information

Application of Fourier Transform to PDE (I) Fourier Sine Transform (application to PDEs defined on a semi-infinite domain)

Application of Fourier Transform to PDE (I) Fourier Sine Transform (application to PDEs defined on a semi-infinite domain) Application of Fourier Transform to PDE (I) Fourier Sine Transform (application to PDEs defined on a semi-infinite domain) The Fourier Sine Transform pair are F. T. : U = 2/ u x sin x dx, denoted as U

More information

Department of Electrical and Computer Engineering Ben-Gurion University of the Negev. LAB 1 - Introduction to USRP

Department of Electrical and Computer Engineering Ben-Gurion University of the Negev. LAB 1 - Introduction to USRP Department of Electrical and Computer Engineering Ben-Gurion University of the Negev LAB 1 - Introduction to USRP - 1-1 Introduction In this lab you will use software reconfigurable RF hardware from National

More information

The Heat Equation. Lectures INF2320 p. 1/88

The Heat Equation. Lectures INF2320 p. 1/88 The Heat Equation Lectures INF232 p. 1/88 Lectures INF232 p. 2/88 The Heat Equation We study the heat equation: u t = u xx for x (,1), t >, (1) u(,t) = u(1,t) = for t >, (2) u(x,) = f(x) for x (,1), (3)

More information

5 Signal Design for Bandlimited Channels

5 Signal Design for Bandlimited Channels 225 5 Signal Design for Bandlimited Channels So far, we have not imposed any bandwidth constraints on the transmitted passband signal, or equivalently, on the transmitted baseband signal s b (t) I[k]g

More information

Introduction to Frequency Domain Processing 1

Introduction to Frequency Domain Processing 1 MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MECHANICAL ENGINEERING 2.151 Advanced System Dynamics and Control Introduction to Frequency Domain Processing 1 1 Introduction - Superposition In this

More information

Lecture 8 ELE 301: Signals and Systems

Lecture 8 ELE 301: Signals and Systems Lecture 8 ELE 3: Signals and Systems Prof. Paul Cuff Princeton University Fall 2-2 Cuff (Lecture 7) ELE 3: Signals and Systems Fall 2-2 / 37 Properties of the Fourier Transform Properties of the Fourier

More information

y cos 3 x dx y cos 2 x cos x dx y 1 sin 2 x cos x dx

y cos 3 x dx y cos 2 x cos x dx y 1 sin 2 x cos x dx Trigonometric Integrals In this section we use trigonometric identities to integrate certain combinations of trigonometric functions. We start with powers of sine and cosine. EXAMPLE Evaluate cos 3 x dx.

More information

Period of Trigonometric Functions

Period of Trigonometric Functions Period of Trigonometric Functions In previous lessons we have learned how to translate any primary trigonometric function horizontally or vertically, and how to Stretch Vertically (change Amplitude). In

More information

CALCULATING THE RESPONSE OF AN OSCILLATOR TO ARBITRARY GROUND MOTION* By G~ORGE W. I-IouSl~ER

CALCULATING THE RESPONSE OF AN OSCILLATOR TO ARBITRARY GROUND MOTION* By G~ORGE W. I-IouSl~ER CALCULATING THE RESONSE OF AN OSCILLATOR TO ARBITRARY GROUND MOTION* By G~ORGE W. I-IouSl~ER IN COMUTING the response of a simple oscillator to arbitrary ground motion it is necessary to use arithmetical

More information

The Algorithms of Speech Recognition, Programming and Simulating in MATLAB

The Algorithms of Speech Recognition, Programming and Simulating in MATLAB FACULTY OF ENGINEERING AND SUSTAINABLE DEVELOPMENT. The Algorithms of Speech Recognition, Programming and Simulating in MATLAB Tingxiao Yang January 2012 Bachelor s Thesis in Electronics Bachelor s Program

More information

GRAPHING IN POLAR COORDINATES SYMMETRY

GRAPHING IN POLAR COORDINATES SYMMETRY GRAPHING IN POLAR COORDINATES SYMMETRY Recall from Algebra and Calculus I that the concept of symmetry was discussed using Cartesian equations. Also remember that there are three types of symmetry - y-axis,

More information

7. Beats. sin( + λ) + sin( λ) = 2 cos(λ) sin( )

7. Beats. sin( + λ) + sin( λ) = 2 cos(λ) sin( ) 34 7. Beats 7.1. What beats are. Musicians tune their instruments using beats. Beats occur when two very nearby pitches are sounded simultaneously. We ll make a mathematical study of this effect, using

More information

How To Understand The Discrete Fourier Transform

How To Understand The Discrete Fourier Transform The Fast Fourier Transform (FFT) and MATLAB Examples Learning Objectives Discrete Fourier transforms (DFTs) and their relationship to the Fourier transforms Implementation issues with the DFT via the FFT

More information

T = 10-' s. p(t)= ( (t-nt), T= 3. n=-oo. Figure P16.2

T = 10-' s. p(t)= ( (t-nt), T= 3. n=-oo. Figure P16.2 16 Sampling Recommended Problems P16.1 The sequence x[n] = (-1)' is obtained by sampling the continuous-time sinusoidal signal x(t) = cos oot at 1-ms intervals, i.e., cos(oont) = (-1)", Determine three

More information

FFT Algorithms. Chapter 6. Contents 6.1

FFT Algorithms. Chapter 6. Contents 6.1 Chapter 6 FFT Algorithms Contents Efficient computation of the DFT............................................ 6.2 Applications of FFT................................................... 6.6 Computing DFT

More information

S. Boyd EE102. Lecture 1 Signals. notation and meaning. common signals. size of a signal. qualitative properties of signals.

S. Boyd EE102. Lecture 1 Signals. notation and meaning. common signals. size of a signal. qualitative properties of signals. S. Boyd EE102 Lecture 1 Signals notation and meaning common signals size of a signal qualitative properties of signals impulsive signals 1 1 Signals a signal is a function of time, e.g., f is the force

More information

ENCOURAGING THE INTEGRATION OF COMPLEX NUMBERS IN UNDERGRADUATE ORDINARY DIFFERENTIAL EQUATIONS

ENCOURAGING THE INTEGRATION OF COMPLEX NUMBERS IN UNDERGRADUATE ORDINARY DIFFERENTIAL EQUATIONS Texas College Mathematics Journal Volume 6, Number 2, Pages 18 24 S applied for(xx)0000-0 Article electronically published on September 23, 2009 ENCOURAGING THE INTEGRATION OF COMPLEX NUMBERS IN UNDERGRADUATE

More information

TMA4213/4215 Matematikk 4M/N Vår 2013

TMA4213/4215 Matematikk 4M/N Vår 2013 Norges teknisk naturvitenskapelige universitet Institutt for matematiske fag TMA43/45 Matematikk 4M/N Vår 3 Løsningsforslag Øving a) The Fourier series of the signal is f(x) =.4 cos ( 4 L x) +cos ( 5 L

More information

CITY UNIVERSITY LONDON. BEng Degree in Computer Systems Engineering Part II BSc Degree in Computer Systems Engineering Part III PART 2 EXAMINATION

CITY UNIVERSITY LONDON. BEng Degree in Computer Systems Engineering Part II BSc Degree in Computer Systems Engineering Part III PART 2 EXAMINATION No: CITY UNIVERSITY LONDON BEng Degree in Computer Systems Engineering Part II BSc Degree in Computer Systems Engineering Part III PART 2 EXAMINATION ENGINEERING MATHEMATICS 2 (resit) EX2005 Date: August

More information

Lecture 27: Mixers. Gilbert Cell

Lecture 27: Mixers. Gilbert Cell Whites, EE 322 Lecture 27 Page 1 of 9 Lecture 27: Mixers. Gilbert Cell Mixers shift the frequency spectrum of an input signal. This is an essential component in electrical communications (wireless or otherwise)

More information

Lab 1. The Fourier Transform

Lab 1. The Fourier Transform Lab 1. The Fourier Transform Introduction In the Communication Labs you will be given the opportunity to apply the theory learned in Communication Systems. Since this is your first time to work in the

More information

y cos 3 x dx y cos 2 x cos x dx y 1 sin 2 x cos x dx y 1 u 2 du u 1 3u 3 C

y cos 3 x dx y cos 2 x cos x dx y 1 sin 2 x cos x dx y 1 u 2 du u 1 3u 3 C Trigonometric Integrals In this section we use trigonometric identities to integrate certain combinations of trigonometric functions. We start with powers of sine and cosine. EXAMPLE Evaluate cos 3 x dx.

More information

UNIVERSITY OF CALIFORNIA, SAN DIEGO Electrical & Computer Engineering Department ECE 101 - Fall 2009 Linear Systems Fundamentals

UNIVERSITY OF CALIFORNIA, SAN DIEGO Electrical & Computer Engineering Department ECE 101 - Fall 2009 Linear Systems Fundamentals UNIVERSITY OF CALIFORNIA, SAN DIEGO Electrical & Computer Engineering Department ECE 101 - Fall 2009 Linear Systems Fundamentals MIDTERM EXAM You are allowed one 2-sided sheet of notes. No books, no other

More information

1 The 1-D Heat Equation

1 The 1-D Heat Equation The 1-D Heat Equation 18.303 Linear Partial Differential Equations Matthew J. Hancock Fall 006 1 The 1-D Heat Equation 1.1 Physical derivation Reference: Guenther & Lee 1.3-1.4, Myint-U & Debnath.1 and.5

More information

L9: Cepstral analysis

L9: Cepstral analysis L9: Cepstral analysis The cepstrum Homomorphic filtering The cepstrum and voicing/pitch detection Linear prediction cepstral coefficients Mel frequency cepstral coefficients This lecture is based on [Taylor,

More information

SIGNAL PROCESSING & SIMULATION NEWSLETTER

SIGNAL PROCESSING & SIMULATION NEWSLETTER 1 of 10 1/25/2008 3:38 AM SIGNAL PROCESSING & SIMULATION NEWSLETTER Note: This is not a particularly interesting topic for anyone other than those who ar e involved in simulation. So if you have difficulty

More information

Computing Fourier Series and Power Spectrum with MATLAB

Computing Fourier Series and Power Spectrum with MATLAB Computing Fourier Series and Power Spectrum with MATLAB By Brian D. Storey. Introduction Fourier series provides an alternate way of representing data: instead of representing the signal amplitude as a

More information

2.6 The driven oscillator

2.6 The driven oscillator 2.6. THE DRIVEN OSCILLATOR 131 2.6 The driven oscillator We would like to understand what happens when we apply forces to the harmonic oscillator. That is, we want to solve the equation M d2 x(t) 2 + γ

More information

The Fourier Analysis Tool in Microsoft Excel

The Fourier Analysis Tool in Microsoft Excel The Fourier Analysis Tool in Microsoft Excel Douglas A. Kerr Issue March 4, 2009 ABSTRACT AD ITRODUCTIO The spreadsheet application Microsoft Excel includes a tool that will calculate the discrete Fourier

More information

PYKC Jan-7-10. Lecture 1 Slide 1

PYKC Jan-7-10. Lecture 1 Slide 1 Aims and Objectives E 2.5 Signals & Linear Systems Peter Cheung Department of Electrical & Electronic Engineering Imperial College London! By the end of the course, you would have understood: Basic signal

More information

B3. Short Time Fourier Transform (STFT)

B3. Short Time Fourier Transform (STFT) B3. Short Time Fourier Transform (STFT) Objectives: Understand the concept of a time varying frequency spectrum and the spectrogram Understand the effect of different windows on the spectrogram; Understand

More information

Second Order Linear Nonhomogeneous Differential Equations; Method of Undetermined Coefficients. y + p(t) y + q(t) y = g(t), g(t) 0.

Second Order Linear Nonhomogeneous Differential Equations; Method of Undetermined Coefficients. y + p(t) y + q(t) y = g(t), g(t) 0. Second Order Linear Nonhomogeneous Differential Equations; Method of Undetermined Coefficients We will now turn our attention to nonhomogeneous second order linear equations, equations with the standard

More information

Convolution, Correlation, & Fourier Transforms. James R. Graham 10/25/2005

Convolution, Correlation, & Fourier Transforms. James R. Graham 10/25/2005 Convolution, Correlation, & Fourier Transforms James R. Graham 10/25/2005 Introduction A large class of signal processing techniques fall under the category of Fourier transform methods These methods fall

More information

The one dimensional heat equation: Neumann and Robin boundary conditions

The one dimensional heat equation: Neumann and Robin boundary conditions The one dimensional heat equation: Neumann and Robin boundary conditions Ryan C. Trinity University Partial Differential Equations February 28, 2012 with Neumann boundary conditions Our goal is to solve:

More information

1.1 Discrete-Time Fourier Transform

1.1 Discrete-Time Fourier Transform 1.1 Discrete-Time Fourier Transform The discrete-time Fourier transform has essentially the same properties as the continuous-time Fourier transform, and these properties play parallel roles in continuous

More information

Adding Sinusoids of the Same Frequency. Additive Synthesis. Spectrum. Music 270a: Modulation

Adding Sinusoids of the Same Frequency. Additive Synthesis. Spectrum. Music 270a: Modulation Adding Sinusoids of the Same Frequency Music 7a: Modulation Tamara Smyth, trsmyth@ucsd.edu Department of Music, University of California, San Diego (UCSD) February 9, 5 Recall, that adding sinusoids of

More information

The continuous and discrete Fourier transforms

The continuous and discrete Fourier transforms FYSA21 Mathematical Tools in Science The continuous and discrete Fourier transforms Lennart Lindegren Lund Observatory (Department of Astronomy, Lund University) 1 The continuous Fourier transform 1.1

More information

L and C connected together. To be able: To analyse some basic circuits.

L and C connected together. To be able: To analyse some basic circuits. circuits: Sinusoidal Voltages and urrents Aims: To appreciate: Similarities between oscillation in circuit and mechanical pendulum. Role of energy loss mechanisms in damping. Why we study sinusoidal signals

More information

Laboratory Manual and Supplementary Notes. CoE 494: Communication Laboratory. Version 1.2

Laboratory Manual and Supplementary Notes. CoE 494: Communication Laboratory. Version 1.2 Laboratory Manual and Supplementary Notes CoE 494: Communication Laboratory Version 1.2 Dr. Joseph Frank Dr. Sol Rosenstark Department of Electrical and Computer Engineering New Jersey Institute of Technology

More information

So far, we have looked at homogeneous equations

So far, we have looked at homogeneous equations Chapter 3.6: equations Non-homogeneous So far, we have looked at homogeneous equations L[y] = y + p(t)y + q(t)y = 0. Homogeneous means that the right side is zero. Linear homogeneous equations satisfy

More information

EE 179 April 21, 2014 Digital and Analog Communication Systems Handout #16 Homework #2 Solutions

EE 179 April 21, 2014 Digital and Analog Communication Systems Handout #16 Homework #2 Solutions EE 79 April, 04 Digital and Analog Communication Systems Handout #6 Homework # Solutions. Operations on signals (Lathi& Ding.3-3). For the signal g(t) shown below, sketch: a. g(t 4); b. g(t/.5); c. g(t

More information

Network Theory Question Bank

Network Theory Question Bank Network Theory Question Bank Unit-I JNTU SYLLABUS: Three Phase Circuits Three phase circuits: Phase sequence Star and delta connection Relation between line and phase voltages and currents in balanced

More information

The Fourier Series of a Periodic Function

The Fourier Series of a Periodic Function 1 Chapter 1 he Fourier Series of a Periodic Function 1.1 Introduction Notation 1.1. We use the letter with a double meaning: a) [, 1) b) In the notations L p (), C(), C n () and C () we use the letter

More information

How To Understand The Nyquist Sampling Theorem

How To Understand The Nyquist Sampling Theorem Nyquist Sampling Theorem By: Arnold Evia Table of Contents What is the Nyquist Sampling Theorem? Bandwidth Sampling Impulse Response Train Fourier Transform of Impulse Response Train Sampling in the Fourier

More information

POWER SYSTEM HARMONICS. A Reference Guide to Causes, Effects and Corrective Measures AN ALLEN-BRADLEY SERIES OF ISSUES AND ANSWERS

POWER SYSTEM HARMONICS. A Reference Guide to Causes, Effects and Corrective Measures AN ALLEN-BRADLEY SERIES OF ISSUES AND ANSWERS A Reference Guide to Causes, Effects and Corrective Measures AN ALLEN-BRADLEY SERIES OF ISSUES AND ANSWERS By: Robert G. Ellis, P. Eng., Rockwell Automation Medium Voltage Business CONTENTS INTRODUCTION...

More information

T = 1 f. Phase. Measure of relative position in time within a single period of a signal For a periodic signal f(t), phase is fractional part t p

T = 1 f. Phase. Measure of relative position in time within a single period of a signal For a periodic signal f(t), phase is fractional part t p Data Transmission Concepts and terminology Transmission terminology Transmission from transmitter to receiver goes over some transmission medium using electromagnetic waves Guided media. Waves are guided

More information

14.1. Basic Concepts of Integration. Introduction. Prerequisites. Learning Outcomes. Learning Style

14.1. Basic Concepts of Integration. Introduction. Prerequisites. Learning Outcomes. Learning Style Basic Concepts of Integration 14.1 Introduction When a function f(x) is known we can differentiate it to obtain its derivative df. The reverse dx process is to obtain the function f(x) from knowledge of

More information

WAVEFORM DICTIONARIES AS APPLIED TO THE AUSTRALIAN EXCHANGE RATE

WAVEFORM DICTIONARIES AS APPLIED TO THE AUSTRALIAN EXCHANGE RATE Sunway Academic Journal 3, 87 98 (26) WAVEFORM DICTIONARIES AS APPLIED TO THE AUSTRALIAN EXCHANGE RATE SHIRLEY WONG a RAY ANDERSON Victoria University, Footscray Park Campus, Australia ABSTRACT This paper

More information

Lecture 18: The Time-Bandwidth Product

Lecture 18: The Time-Bandwidth Product WAVELETS AND MULTIRATE DIGITAL SIGNAL PROCESSING Lecture 18: The Time-Bandwih Product Prof.Prof.V.M.Gadre, EE, IIT Bombay 1 Introduction In this lecture, our aim is to define the time Bandwih Product,

More information

Lectures 5-6: Taylor Series

Lectures 5-6: Taylor Series Math 1d Instructor: Padraic Bartlett Lectures 5-: Taylor Series Weeks 5- Caltech 213 1 Taylor Polynomials and Series As we saw in week 4, power series are remarkably nice objects to work with. In particular,

More information

ALFFT FAST FOURIER Transform Core Application Notes

ALFFT FAST FOURIER Transform Core Application Notes ALFFT FAST FOURIER Transform Core Application Notes 6-20-2012 Table of Contents General Information... 3 Features... 3 Key features... 3 Design features... 3 Interface... 6 Symbol... 6 Signal description...

More information

Finding Equations of Sinusoidal Functions From Real-World Data

Finding Equations of Sinusoidal Functions From Real-World Data Finding Equations of Sinusoidal Functions From Real-World Data **Note: Throughout this handout you will be asked to use your graphing calculator to verify certain results, but be aware that you will NOT

More information

UNIVERSITY OF CALIFORNIA, SAN DIEGO Electrical & Computer Engineering Department ECE 101 - Fall 2010 Linear Systems Fundamentals

UNIVERSITY OF CALIFORNIA, SAN DIEGO Electrical & Computer Engineering Department ECE 101 - Fall 2010 Linear Systems Fundamentals UNIVERSITY OF CALIFORNIA, SAN DIEGO Electrical & Computer Engineering Department ECE 101 - Fall 2010 Linear Systems Fundamentals FINAL EXAM WITH SOLUTIONS (YOURS!) You are allowed one 2-sided sheet of

More information

2 Signals and Systems: Part I

2 Signals and Systems: Part I 2 Signals and Systems: Part I In this lecture, we consider a number of basic signals that will be important building blocks later in the course. Specifically, we discuss both continuoustime and discrete-time

More information

CONVOLUTION Digital Signal Processing

CONVOLUTION Digital Signal Processing CONVOLUTION Digital Signal Processing Introduction As digital signal processing continues to emerge as a major discipline in the field of electrical engineering an even greater demand has evolved to understand

More information

Fourier Analysis. u m, a n u n = am um, u m

Fourier Analysis. u m, a n u n = am um, u m Fourier Analysis Fourier series allow you to expand a function on a finite interval as an infinite series of trigonometric functions. What if the interval is infinite? That s the subject of this chapter.

More information

1995 Mixed-Signal Products SLAA013

1995 Mixed-Signal Products SLAA013 Application Report 995 Mixed-Signal Products SLAA03 IMPORTANT NOTICE Texas Instruments and its subsidiaries (TI) reserve the right to make changes to their products or to discontinue any product or service

More information

Aliasing, Image Sampling and Reconstruction

Aliasing, Image Sampling and Reconstruction Aliasing, Image Sampling and Reconstruction Recall: a pixel is a point It is NOT a box, disc or teeny wee light It has no dimension It occupies no area It can have a coordinate More than a point, it is

More information

Time domain modeling

Time domain modeling Time domain modeling Equationof motion of a WEC Frequency domain: Ok if all effects/forces are linear M+ A ω X && % ω = F% ω K + K X% ω B ω + B X% & ω ( ) H PTO PTO + others Time domain: Must be linear

More information

Experiment 3: Double Sideband Modulation (DSB)

Experiment 3: Double Sideband Modulation (DSB) Experiment 3: Double Sideband Modulation (DSB) This experiment examines the characteristics of the double-sideband (DSB) linear modulation process. The demodulation is performed coherently and its strict

More information

Fourier Transforms. Figure 7-1:Fourier transform from time domain to frequency domain

Fourier Transforms. Figure 7-1:Fourier transform from time domain to frequency domain Fourier Transforms The whole is the sum of the parts Euclid Fourier Series As nmr spectroscopists working with modern digital equipment we routinely acquire data in the form of the free induction decay.

More information

PHY3128 / PHYM203 (Electronics / Instrumentation) Transmission Lines. Repeated n times I L

PHY3128 / PHYM203 (Electronics / Instrumentation) Transmission Lines. Repeated n times I L Transmission Lines Introduction A transmission line guides energy from one place to another. Optical fibres, waveguides, telephone lines and power cables are all electromagnetic transmission lines. are

More information

Networks, instruments and data centres: what does your seismic (meta-) data really mean? Joachim Wassermann (LMU Munich)

Networks, instruments and data centres: what does your seismic (meta-) data really mean? Joachim Wassermann (LMU Munich) Networks, instruments and data centres: what does your seismic (meta-) data really mean? Joachim Wassermann (LMU Munich) Quest Workshop, Hveragerdi 2011 Why an issue? Seismic Networks: Data Exchange many

More information

7. DYNAMIC LIGHT SCATTERING 7.1 First order temporal autocorrelation function.

7. DYNAMIC LIGHT SCATTERING 7.1 First order temporal autocorrelation function. 7. DYNAMIC LIGHT SCATTERING 7. First order temporal autocorrelation function. Dynamic light scattering (DLS) studies the properties of inhomogeneous and dynamic media. A generic situation is illustrated

More information

First, we show how to use known design specifications to determine filter order and 3dB cut-off

First, we show how to use known design specifications to determine filter order and 3dB cut-off Butterworth Low-Pass Filters In this article, we describe the commonly-used, n th -order Butterworth low-pass filter. First, we show how to use known design specifications to determine filter order and

More information

SAMPLE SOLUTIONS DIGITAL SIGNAL PROCESSING: Signals, Systems, and Filters Andreas Antoniou

SAMPLE SOLUTIONS DIGITAL SIGNAL PROCESSING: Signals, Systems, and Filters Andreas Antoniou SAMPLE SOLUTIONS DIGITAL SIGNAL PROCESSING: Signals, Systems, and Filters Andreas Antoniou (Revision date: February 7, 7) SA. A periodic signal can be represented by the equation x(t) k A k sin(ω k t +

More information

Methods for Estimation of Voltage Harmonic Components

Methods for Estimation of Voltage Harmonic Components 13 Methods for Estimation of Voltage Harmonic Components Tomáš Radil 1 and Pedro M. Ramos 2 1 Instituto de Telecomunições, Lisbon 2 Instituto de Telecomunições and Department of Electrical and Computer

More information

Digital Transmission (Line Coding)

Digital Transmission (Line Coding) Digital Transmission (Line Coding) Pulse Transmission Source Multiplexer Line Coder Line Coding: Output of the multiplexer (TDM) is coded into electrical pulses or waveforms for the purpose of transmission

More information

Contents. A Error probability for PAM Signals in AWGN 17. B Error probability for PSK Signals in AWGN 18

Contents. A Error probability for PAM Signals in AWGN 17. B Error probability for PSK Signals in AWGN 18 Contents 5 Signal Constellations 3 5.1 Pulse-amplitude Modulation (PAM).................. 3 5.1.1 Performance of PAM in Additive White Gaussian Noise... 4 5.2 Phase-shift Keying............................

More information

MCR3U - Practice Test - Periodic Functions - W2012

MCR3U - Practice Test - Periodic Functions - W2012 Name: Date: May 25, 2012 ID: A MCR3U - Practice Test - Periodic Functions - W2012 1. One cycle of the graph of a periodic function is shown below. State the period and amplitude. 2. One cycle of the graph

More information

Design of FIR Filters

Design of FIR Filters Design of FIR Filters Elena Punskaya www-sigproc.eng.cam.ac.uk/~op205 Some material adapted from courses by Prof. Simon Godsill, Dr. Arnaud Doucet, Dr. Malcolm Macleod and Prof. Peter Rayner 68 FIR as

More information

Em bedded DSP : I ntroduction to Digital Filters

Em bedded DSP : I ntroduction to Digital Filters Embedded DSP : Introduction to Digital Filters 1 Em bedded DSP : I ntroduction to Digital Filters Digital filters are a important part of DSP. In fact their extraordinary performance is one of the keys

More information

Homework 2 Solutions

Homework 2 Solutions Homework Solutions Igor Yanovsky Math 5B TA Section 5.3, Problem b: Use Taylor s method of order two to approximate the solution for the following initial-value problem: y = + t y, t 3, y =, with h = 0.5.

More information

LS.6 Solution Matrices

LS.6 Solution Matrices LS.6 Solution Matrices In the literature, solutions to linear systems often are expressed using square matrices rather than vectors. You need to get used to the terminology. As before, we state the definitions

More information

8 Filtering. 8.1 Mathematical operation

8 Filtering. 8.1 Mathematical operation 8 Filtering The estimated spectrum of a time series gives the distribution of variance as a function of frequency. Depending on the purpose of analysis, some frequencies may be of greater interest than

More information

Design of Efficient Digital Interpolation Filters for Integer Upsampling. Daniel B. Turek

Design of Efficient Digital Interpolation Filters for Integer Upsampling. Daniel B. Turek Design of Efficient Digital Interpolation Filters for Integer Upsampling by Daniel B. Turek Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements

More information

Generation and Detection of NMR Signals

Generation and Detection of NMR Signals Generation and Detection of NMR Signals Hanudatta S. Atreya NMR Research Centre Indian Institute of Science NMR Spectroscopy Spin (I)=1/2h B 0 Energy 0 = B 0 Classical picture (B 0 ) Quantum Mechanical

More information

Let s examine the response of the circuit shown on Figure 1. The form of the source voltage Vs is shown on Figure 2. R. Figure 1.

Let s examine the response of the circuit shown on Figure 1. The form of the source voltage Vs is shown on Figure 2. R. Figure 1. Examples of Transient and RL Circuits. The Series RLC Circuit Impulse response of Circuit. Let s examine the response of the circuit shown on Figure 1. The form of the source voltage Vs is shown on Figure.

More information

Notice numbers may change randomly in your assignments and you may have to recalculate solutions for your specific case.

Notice numbers may change randomly in your assignments and you may have to recalculate solutions for your specific case. HW1 Possible Solutions Notice numbers may change randomly in your assignments and you may have to recalculate solutions for your specific case. Tipler 14.P.003 An object attached to a spring has simple

More information

054414 PROCESS CONTROL SYSTEM DESIGN. 054414 Process Control System Design LECTURE 2: FREQUENCY DOMAIN ANALYSIS

054414 PROCESS CONTROL SYSTEM DESIGN. 054414 Process Control System Design LECTURE 2: FREQUENCY DOMAIN ANALYSIS 05444 PROCESS CONTROL SYSTEM DESIGN 05444 Process Control System Design LECTURE : FREQUENCY DOMAIN ANALYSIS Daniel R. Lewin Department of Chemical Engineering Technion, Haifa, Israel - Objectives On completing

More information

ANALYTICAL METHODS FOR ENGINEERS

ANALYTICAL METHODS FOR ENGINEERS UNIT 1: Unit code: QCF Level: 4 Credit value: 15 ANALYTICAL METHODS FOR ENGINEERS A/601/1401 OUTCOME - TRIGONOMETRIC METHODS TUTORIAL 1 SINUSOIDAL FUNCTION Be able to analyse and model engineering situations

More information

N 1. (q k+1 q k ) 2 + α 3. k=0

N 1. (q k+1 q k ) 2 + α 3. k=0 Teoretisk Fysik Hand-in problem B, SI1142, Spring 2010 In 1955 Fermi, Pasta and Ulam 1 numerically studied a simple model for a one dimensional chain of non-linear oscillators to see how the energy distribution

More information

1 TRIGONOMETRY. 1.0 Introduction. 1.1 Sum and product formulae. Objectives

1 TRIGONOMETRY. 1.0 Introduction. 1.1 Sum and product formulae. Objectives TRIGONOMETRY Chapter Trigonometry Objectives After studying this chapter you should be able to handle with confidence a wide range of trigonometric identities; be able to express linear combinations of

More information