Techniques in Signal and Data Processing CSC 508 Frequency Domain Analysis.

Slides:



Advertisements
Similar presentations
Lecture 7: Basis Functions & Fourier Series
Advertisements

The arc length spanned, or cut off, by an angle is shown next:
MEEG 5113 Modal Analysis Set 3.
Engineering Mathematics Class #15 Fourier Series, Integrals, and Transforms (Part 3) Sheng-Fang Huang.
Sinusoidal Waves. Objective of Lecture Discuss the characteristics of a sinusoidal wave. Define the mathematical relationship between the period, frequency,
Copyright © 2005 Pearson Education, Inc. Chapter 4 Graphs of the Circular Functions.
Fourier Integrals For non-periodic applications (or a specialized Fourier series when the period of the function is infinite: L  ) L -L L  -L  - 
Lecture 8 Fourier Series applied to pulses Remember homework 1 for submission 31/10/08 Remember Phils Problems.
DFT/FFT and Wavelets ● Additive Synthesis demonstration (wave addition) ● Standard Definitions ● Computing the DFT and FFT ● Sine and cosine wave multiplication.
3.1 Chapter 3 Data and Signals Computer Communication & Networks.
Complex Power – Background Concepts
Math Review with Matlab:
Quiz Find a positive and negative co-terminal angle with: co-terminal angle with: 2.Find a positive and negative co-terminal angle with: co-terminal.
Techniques in Signal and Data Processing CSC 508 Fourier Analysis.
Properties of continuous Fourier Transforms
Ch 5.1: Review of Power Series
Fourier Analysis D. Gordon E. Robertson, PhD, FCSB School of Human Kinetics University of Ottawa.
A Fourier series can be defined for any function over the interval 0  x  2L where Often easiest to treat n=0 cases separately.
Copyright © 2005 Pearson Education, Inc. Chapter 3 Radian Measure and Circular Functions.
The World’s Largest Ferris Wheel
Boyce/DiPrima 10th ed, Ch 10.2: Fourier Series Elementary Differential Equations and Boundary Value Problems, 10th edition, by William E. Boyce and Richard.
CH#3 Fourier Series and Transform
Chapter 15 Fourier Series and Fourier Transform
Periodic Functions and Fourier Series. Periodic Functions A functionis periodic if it is defined for all real and if there is some positive number, such.
Fourier Series Or How I Spent My Summer Vacation (the 2 weeks after the AP Exam) Kevin Bartkovich Phillips Exeter Academy 1.
Chapter 25 Nonsinusoidal Waveforms. 2 Waveforms Used in electronics except for sinusoidal Any periodic waveform may be expressed as –Sum of a series of.
ELECTRICAL CIRCUIT ET 201 Define and explain characteristics of sinusoidal wave, phase relationships and phase shifting.
Chapter 4 Analytic Trigonometry Section 4.3 Double-Angle, Half-Angle and Product- Sum Formulas.
FOURIER SERIES §Jean-Baptiste Fourier (France, ) proved that almost any period function can be represented as the sum of sinusoids with integrally.
Fourier series. The frequency domain It is sometimes preferable to work in the frequency domain rather than time –Some mathematical operations are easier.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Chapter 19.
Trigonometric Equations M 140 Precalculus V. J. Motto.
Boyce/DiPrima 9 th ed, Ch 5.1: Review of Power Series Elementary Differential Equations and Boundary Value Problems, 9 th edition, by William E. Boyce.
Section 5.7: Additional Techniques of Integration Practice HW from Stewart Textbook (not to hand in) p. 404 # 1-5 odd, 9-27 odd.
 Voltage can be produced such that, over time, it follows the shape of a sine wave  The magnitude of the voltage continually changes.  Polarity may.
STROUD Worked examples and exercises are in the text PROGRAMME F10 FUNCTIONS.
October 29, 2013Computer Vision Lecture 13: Fourier Transform II 1 The Fourier Transform In the previous lecture, we discussed the Hough transform. There.
Fourier series: Eigenfunction Approach
1 ECE 3336 Introduction to Circuits & Electronics Note Set #8 Phasors Spring 2013 TUE&TH 5:30-7:00 pm Dr. Wanda Wosik.
Spatial Frequencies Spatial Frequencies. Why are Spatial Frequencies important? Efficient data representation Provides a means for modeling and removing.
Periodic driving forces
CH#3 Fourier Series and Transform
Module 6Aberration and Doppler Shift of Light1 Module 6 Aberration and Doppler Shift of Light The term aberration used here means deviation. If a light.
Graphs of Sine and Cosine
Fourier series, Discrete Time Fourier Transform and Characteristic functions.
Physics 361 Principles of Modern Physics Lecture 13.
Advanced Engineering Mathematics, 7 th Edition Peter V. O’Neil © 2012 Cengage Learning Engineering. All Rights Reserved. CHAPTER 4 Series Solutions.
Chapter2 : SIGNALS 1st semester King Saud University
Chapter 5 Verifying Trigonometric Identities
1 Copyright © Cengage Learning. All rights reserved. 6. The Trigonometric Functions Graphs of Trigonometric Functions.
Section 1.5 Trigonometric Functions
STROUD Worked examples and exercises are in the text Programme F11: Trigonometric and exponential functions TRIGONOMETRIC AND EXPONENTIAL FUNCTIONS PROGRAMME.
Frequency domain analysis and Fourier Transform
EEE 332 COMMUNICATION Fourier Series Text book: Louis E. Frenzel. Jr. Principles of Electronic Communication Systems, Third Ed. Mc Graw Hill.
2 nd semester nalhareqi King Saud University College of Applied studies and Community Service 1301CT By: Nour Alhariqi.
CH#3 Fourier Series and Transform 1 st semester King Saud University College of Applied studies and Community Service 1301CT By: Nour Alhariqi.
Ch 10.2: Fourier Series We will see that many important problems involving partial differential equations can be solved, provided a given function can.
5.3 Trigonometric Graphs.
Trigonometric Identities
Sinusoidal Excitation of Circuits
ECE 2202 Circuit Analysis II
Graphs of Sine and Cosine Functions
Sinusoidal Waveform Phasor Method.
Trigonometric Identities
Periodic Functions and Fourier Series
Lecture 2: SIGNALS 2nd semester By: Elham Sunbu.
Preparing for the SAT II
Chapter 4. Time Response I may not have gone where I intended to go, but I think I have ended up where I needed to be. Pusan National University Intelligent.
5.4 Sum and Difference Identities
Presentation transcript:

Techniques in Signal and Data Processing CSC 508 Frequency Domain Analysis

In this section we learn how to transform a time domain signal into the frequency domain. We study both periodic and non-periodic signals and the types of transforms that work best with each. First Principles - We have combined sine waves of different amplitudes and frequencies to make other wave shapes. A sine wave is a periodic function described by the y-value of a unit-vector rotated about the origin. In this case the independent variable is the angle between the unit vector and the positive x-axis. We can specify a time-varying periodic function by defining a rotation rate  if the unit- vector in radians per second. The angle  of the unit vector is given by  =  t for time t.

There are three parameters that describe a sine function. These are the amplitude A, the frequency 2  and the phase  0. The amplitude is the peak value obtained by multiplying the value of the sine function by A. The frequency is the number of cycles of the sine wave generated per second and is given in 2  times  cycles per second (Hertz). The period (or wavelength) is the inverse of the frequency. The phase  0 is the amount of shif in angle (or time) of the wave relative to the origin. For a positive phase shift, the wave appears to move backqard because the values of the wave are reached earlier by an amount proportional to the phase angle. So what's the big deal about sine waves? They are only one of an infinitely large number of possible wave shapes. The reason we are concerned with sine waves is that they are a class of waves that can be combined to make any other wave shape. amplitude period = 1/frequency

Combining Sine Waves - Let's add two sine waves together whose amplitudes and frequencies are the same but whose phases are different by  /2. We see that this results in a new sine wave with the same frequency, an amplitude that is  2 times the original functions' amplitudes, and a phase that is half way between 0 and  /2. We can verify this result mathematically. If we were to reduce the amplitude of one of the sine waves in this example, the result would still be a sine wave but the phase of the resulting wave would shift toward the phase of the sine wave with the larger amplitude.

In order to build functions with other shapes we have to combine waves of different frequencies. In the next example with add two sine waves, buth this time we keep the amplitudes and phases equal and give one wave double the frequency of the other. Since the two wave have the same phase, they initially add together constructively (i.e. the amplitude of the resulting wave is greater). However, the higher frequency wave begins to swing back to negative values before the lower frequency wave and the two wave begin to cancel each other out (i.e. they add destructively). You may want to use the program sine_gen.exe to experiement with this effect.

The Fourier Series We are now ready to formalize the notion of a trignonometric series that can generate any function. Consider the following inifinite series, where 1/2 a 0 is a normalized coefficient establishing the baseline or neutral position of the periodic function being represented in the series. This series is called the Fourier series, named after its discoverer. The next pair of terms (a 1 cosx + b 1 sinx) is the fundamental, the next term is called the first harmonic or first overtone and so on. Since the cosine and sine are related by a phase angle p, this series can also be represented with only sine or cosine terms. For example, This is all very exciting, but to be of much value to us, we need to know how to determine the proper coefficients a n and b n to represent an arbitrary function f(x) in a Fourier series.

Suppose a function f(x) is represented by the Fourier series, We wish to solve for an expression of the coefficients a n and b n. To do this we must select one of the term in the infinite series at a time while forcing the other terms to zero. There is usually a trick in mathematical derivations and this case is no exception. Multiply both sides of this expression by cos mx and then integrate both sides with respect to x over one period (-  to  ).

The integrals in the summation can be evaluated with the help of a few trigonometric identities. In particular we have the desired selector operation, And the other integra is always zero. Therefore, for each value of the index m, only the mth term is non-zero, and we have, Solving for the coefficients a n and b n we have,

Any function f(x) that is continuous and integrable over the domain -  to  can be represented as a trigonometric series with the coefficients a n and b n, When f(x) is an even function (i.e. f(-x)=f(x) ) or an odd function (i.e. f(-x)=-f(x) ) then we can simplify the trigonometric series to a Fourier cosine series or a Fourier sine series respectively.  f(x) Evenf(x) Odd

Let's compute the coefficients of the square wave defined by,  We notice that the function is odd since f(-x)=-f(x), so we will compute the coefficients of the Fourier sine series. The limits of integration are between 0 and p so only the +1 portion of the square wave is used. (Don't forget that the -1 portion of the square wave is implied by the fact that we are assuming an odd function.) 0     cosine

So the coefficients are, etc. Using just these terms we get the approximation to the square wave shown below:

Homework: 1. Compute the first 8 coefficients of the following function and use sine_gen.exe to view the resulting approximation of f(x).             2. Define the limits of integration for this odd function and derive the formula for the coefficients of its Fourier sine series.