Complex Form of Fourier Series For a real periodic function f(t) with period T, fundamental frequency where is the “complex amplitude spectrum”.

Slides:



Advertisements
Similar presentations
For more ppt’s, visit Fourier Series For more ppt’s, visit
Advertisements

Fourier Series & Transforms
Mike Doggett Staffordshire University
Chapter 5 The Fourier Transform. Basic Idea We covered the Fourier Transform which to represent periodic signals We assumed periodic continuous signals.
Fourier Series 主講者:虞台文.
1 Chapter 16 Fourier Analysis with MATLAB Fourier analysis is the process of representing a function in terms of sinusoidal components. It is widely employed.
Signals and Signal Space
Properties of continuous Fourier Transforms
Dr. Jie ZouPHY Chapter 8 (Hall) Sound Spectra.
Fourier Series.
S. Mandayam/ ECOMMS/ECE Dept./Rowan University Electrical Communication Systems ECE Spring 2008 Shreekanth Mandayam ECE Department Rowan University.
Signals Processing Second Meeting. Fourier's theorem: Analysis Fourier analysis is the process of analyzing periodic non-sinusoidal waveforms in order.
Signals, Fourier Series
S. Mandayam/ ECOMMS/ECE Dept./Rowan University Electrical Communication Systems ECE Spring 2009 Shreekanth Mandayam ECE Department Rowan University.
Fourier Series. is the “fundamental frequency” Fourier Series is the “fundamental frequency”
S. Mandayam/ ECOMMS/ECE Dept./Rowan University Electrical Communication Systems ECE Spring 2008 Shreekanth Mandayam ECE Department Rowan University.
Chapter 4 The Fourier Series and Fourier Transform
Continuous Time Signals All signals in nature are in continuous time.
CH#3 Fourier Series and Transform
Chapter 4 The Fourier Series and Fourier Transform.
Chapter 15 Fourier Series and Fourier Transform
Systems: Definition Filter
Chapter 25 Nonsinusoidal Waveforms. 2 Waveforms Used in electronics except for sinusoidal Any periodic waveform may be expressed as –Sum of a series of.
Continuous Time Signals
ELEC ENG 4035 Communications IV1 School of Electrical & Electronic Engineering 1 Section 2: Frequency Domain Analysis Contents 2.1 Fourier Series 2.2 Fourier.
DTFT And Fourier Transform
Where we’re going Speed, Storage Issues Frequency Space.
Lecture 1 Signals in the Time and Frequency Domains
1 Let g(t) be periodic; period = T o. Fundamental frequency = f o = 1/ T o Hz or  o = 2  / T o rad/sec. Harmonics =n f o, n =2,3 4,... Trigonometric.
Fundamentals of Electric Circuits Chapter 17
1 The Fourier Series for Discrete- Time Signals Suppose that we are given a periodic sequence with period N. The Fourier series representation for x[n]
Module 2 SPECTRAL ANALYSIS OF COMMUNICATION SIGNAL.
Fourier Series. Introduction Decompose a periodic input signal into primitive periodic components. A periodic sequence T2T3T t f(t)f(t)
ECE 4710: Lecture #6 1 Bandlimited Signals  Bandlimited waveforms have non-zero spectral components only within a finite frequency range  Waveform is.
Fundamentals of Electric Circuits Chapter 18 Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
Fourier Series Kamen and Heck.
1 ECE 3336 Introduction to Circuits & Electronics Note Set #8 Phasors Spring 2013 TUE&TH 5:30-7:00 pm Dr. Wanda Wosik.
Chapter 2. Signals and Linear Systems
Fourrier example.
CH#3 Fourier Series and Transform
1 CHAPTER 5 : FOURIER SERIES  Introduction  Periodic Functions  Fourier Series of a function  Dirichlet Conditions  Odd and Even Functions  Relationship.
ES97H Biomedical Signal Processing
Fourier series, Discrete Time Fourier Transform and Characteristic functions.
INTRODUCTION TO SIGNALS
1 Signals Signal  a transmitted effect conveying a message  A essential characteristic of a signal is that of change, since it must be capable of carrying.
1 “Figures and images used in these lecture notes by permission, copyright 1997 by Alan V. Oppenheim and Alan S. Willsky” Signals and Systems Spring 2003.
Quantum Two 1. 2 Time-Dependent Perturbations 3.
The Spectrum n Jean Baptiste Fourier ( ) discovered a fundamental tenet of wave theory.
Fourier Series & Transforms
CH#3 Fourier Series and Transform 1 st semester King Saud University College of Applied studies and Community Service 1301CT By: Nour Alhariqi.
Convergence of Fourier series It is known that a periodic signal x(t) has a Fourier series representation if it satisfies the following Dirichlet conditions:
ELECTRIC CIRCUITS EIGHTH EDITION JAMES W. NILSSON & SUSAN A. RIEDEL.
EE422G Signals and Systems Laboratory Fourier Series and the DFT Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
EEE422 Signals and Systems Laboratory
MECH 373 Instrumentation and Measurements
Continuous-Time Signal Analysis
UNIT II Analysis of Continuous Time signal
Chapter4 Bandpass Signalling Bandpass Filtering and Linear Distortion
Lecture 3 From Delta Functions to Convolution.
UNIT V Linear Time Invariant Discrete-Time Systems
Fourier transforms and
Fundamentals of Electric Circuits Chapter 18
Example I: F.T. Integration Property
7.2 Even and Odd Fourier Transforms phase of signal frequencies
The Fourier Series for Continuous-Time Periodic Signals
Signals and Systems EE235 Leo Lam ©
Lecture 7C Fourier Series Examples: Common Periodic Signals
7.7 Fourier Transform Theorems, Part II
Signals and Systems EE235 Lecture 23 Leo Lam ©
Lecture 5A: Operations on the Spectrum
Presentation transcript:

Complex Form of Fourier Series For a real periodic function f(t) with period T, fundamental frequency where is the “complex amplitude spectrum”.

Amplitude spectrum: Phase spectrum: The coefficients are related to those in the other forms of the series by

Example: Derive complex Fouries Series for the rectangular form in the Figure below, and the amplitude and phase spectrum.

Note that the plot is more complex than previous examples of purely odd, or even functions.

Where the sinc function is given by

The harmonics are placed at intervals of 1/T, their envelop following the (modulus) of the sinc function. A zero amplitude occurs whenever is integral so with the fourth, eighth, twelfth lines etc. are zero. These zeros occurs at frequencies 1/τ, 2/τ, 3/τ etc.. The repetition of the waveform produces lines every 1/T Hz and the envelope of the spectrum is determined by the shape of the waveform. The term is a phase term dependent on the choice of origin and vanishes if the origin is in chosen in the center of a pulse. In general a shift of origin of θ in time produces a phase term of in the corresponding spectrum.

Useful deductions: (i) For a given period T, the value of τ determines the distribution of power in the spectrum. large τ small τ 1/τ 1/T

(ii) For a given value of pulse width τ, the period T similarly determines determines the power distribution. large T small T

(iii) If we put T=τ, we get a constant (d.c) level. is then given by A sinc(n), so a single spectral line of height A occurs at zero frequency.

(iv) If we let the repetition period T become very large, the line spacing 1/T become very small. As T tends to infinity, the spacing tends to zero and we get a continuous spectrum. This is because f(t) becomes a finite energy signal if T is infinite, and such signal have continuous spectra.

(v) Suppose we make τ small but keep the pulse area A τ constant. In the limit we get an impulse of strength A τ, and the spectrum will simply be a set of lines of constants heights A/T.

(vi) Finally, it is clear tha a single impulse will have a constant but continuous spectrum.