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Fast Fourier Transform and MATLAB Implementation Fast Fourier Transform and MATLAB Implementation by Wanjun Huang for Dr. Duncan L. MacFarlane 1 Signals In the fields of communications, signal processing, and in electrical engineering more generally, a signal is any time‐varying or spatial‐varying quantity This variable(quantity) changes in time • Speech or audio signal: A sound amplitude that varies in time • Temperature readings at different hours of a day • Stock price changes over days • Etc. Signals can be classified by continues‐time signal and discrete‐time signal: • A discrete signal or discrete‐time signal is a time series, perhaps a signal that has been sampldled from a continuous‐time silignal • A digital signal is a discrete‐time signal that takes on only a discrete set of values Continuous Time Signal Discrete Time Signal 1 1 0.5 0.5 0 0 f(t) f[n] -0.5 -0.5 -1 -1 0 10 20 30 40 0 10 20 30 40 Time (sec) n 2 Periodic Signal periodic signal and non‐periodic signal: Periodic Signal Non-Periodic Signal 1 1 0 f(t) 0 f[n] -1 -1 0 10 20 30 40 0 10 20 30 40 Time (sec) n • Period T: The minimum interval on which a signal repeats • Fundamental frequency: f0=1/T • Harmonic frequencies: kf0 • Any periodic signal can be approximated by a sum of many sinusoids at harmonic frequencies of the sig(gnal(kf0) with appropriate amplitude and phase • Instead of using sinusoid signals, mathematically, we can use the complex exponential functions with both positive and negative harmonic frequencies Euler formula: exp( j t ) sin( t ) j cos( t ) 3 Time‐Frequency Analysis • A signal has one or more frequencies in it, and can be viewed from two different standpoints: Time domain and Frequency domain Time Domian (Banded Wren Song) Frequency Domain 1 2 0 1 mplitude Power AA -1 0 0 2 4 6 8 0 200 400 600 800 1000 1200 Sample Number 4 x 10 Frequency (Hz) Time‐domain figure: how a signal changes over time Frequency‐domain figure: how much of the signal lies within each given frequency band over a range of frequencies Why frequency domain analysis? • To decompose a complex signal into simpler parts to facilitate analysis • Differential and difference equations and convolution operations in the time domain become algebraic operations in the frequency domain • Fast Algorithm (FFT) 4 Fourier Transform We can go between the time domain and the frequency domain by using a tool called Fourier transform • A Fourier transform converts a signal in the time domain to the frequency domain(spectrum) • An inverse FiFourier transform converts the frequency didomain components back into the original time domain signal Continuous‐Time Fourier Transform: j t 1 j t F ( j ) f (t )e dt f (t ) F ( j )e d 2 Discrete‐Time Fourier Transform(DTFT): 1 j j n X (e j ) x[ n ]e j n x[ n ] X (e )e d n 2 2 5 Fourier Representation For Four Types of Signals The signal with different time‐domain characteristics has different freqqyuency‐domain characteristics 1Continues‐time periodic signal ‐‐‐> discrete non‐periodic spectrum 2Continues‐time non‐periodic signal ‐‐‐> continues non‐periodic spectrum 3 Discrete non‐periodic signal ‐‐‐> continues periodic spectrum 4Discrete periodic signal ‐‐‐> discrete periodic spectrum The last transformation between time‐domain and freqqyuency is most useful The reason that discrete is associated with both time‐domain and frequency domain is because computers can only take finite discrete time signals 6 Periodic Sequence A periodic sequence with period N is defined as: ~x (n ) ~x (n kN ) , where k is integer 2 j kn kn N For example: W N e (it is called Twiddle Factor) kn ( k N ) n k ( n N ) Properties: Periodic W N W N W N kn kn * ( N k ) n k ( N n ) Symmetric W N (W N ) W N W N N 1 kn N n rN Orthogonal W N k 0 0 other For a periodic sequence x(n) with period N, only N samples are independent. So that N sample in one period is enough to represent the whole sequence 7 Discrete Fourier Series(DFS) Periodic signals may be expanded into a series of sine and cosine functions N 1 ~ ~ kn X ( k ) x ( n )W N ~ ~ n 0 X (k ) DFS ( x (n )) N 1 ~ ~ ~ 1 ~ kn x (n ) IDFS ( X (k )) x ( n ) X ( k )W N N n 0 ~ X ( k ) is still a periodic sequence with period N in frequency domain The Fourier series for the discrete‐time periodic wave shown below: Sequence x (in time domain) Fourier Coeffients 1 0.2 0.5 0 X -0.2 Amplitude 0 -040.4 010203040 0 10 20 30 40 time 8 Finite Length Sequence Real lift signal is generally a x ( n ) 0 n N 1 fin ite lhlength sequence x ( n ) 0 others If we periodic extend it by the period N, then ~x (n) x(n rN ) r x(n) ~x(n) 9 Relationship Between Finite Length Sequence and PidiPeriodic Sequence A periodic sequence is the periodic extension of a finite length sequence ~ x ( n ) x ( n rN ) x (( n )) N m A finite length sequence is the principal value interval of the periodic sequence ~ 1 0 n N 1 x(n) x(n)RN (n) Where RN (n) 0 others So that: ~ ~ x(n) x (n)RN (n) IDFS[X (k)]RN (n) ~ ~ X (k) X (k)RN (k) DFS[x (n)]RN (n) 10 Discrete Fourier Transform(DFT) • Using the Fourier series representation we have Discrete Fourier Transform(DFT) for finite length signal • DFT can convert time‐domain discrete signal into frequency‐ domain discrete spectrum N 1 Assume that we have a signal { x[ n ]} n 0 . Then the DFT of the signal is a sequence X [ k ] for k 0, , N 1 N 1 X [ k ] x[ n ]e 2 jnk / N n 0 The Inverse Discrete Fourier Transform(IDFT): N 1 1 2 jnk / N x[ n ] X [ k ]e , n 0,2, , N 1 . N k 0 Note that because MATLAB cannot use a zero or negative indices, the index starts from 1 in MATLAB 11 DFT Example The DFT is widely used in the fields of spectral analysis, acoustics, medical imaging, and telecommunications. Time domain signal 6 For example: 5 4 ee x[ n ] [ 2 4 1 6 ], N 4, ( n 0,1,2,3) 3 2 Amplitud 1 3 j nk 3 nk X [ k ] x[ n ]e 2 x[ n ]( j ) 0 n 0 n 0 -1 0 0.5 1 1.5 2 2.5 3 Time X [0 ] 2 4 ( 1) 6 11 Frequency domain signal 12 X [1] 2 ( 4 j ) 1 6 j 3 2 j 10 X [ 2 ] 2 ( 4 ) ( 1) 6 9 8 X [3] 2 ( 4 j ) 1 6 j 3 2 j 6 |X[k]| 4 2 0 0 0.5 1 1.5 2 2.5 3 Frequency 12 Fast Fourier Transform(FFT) • The Fast Fourier Transform does not refer to a new or different type of Fourier transform. It refers to a very efficient algorithm for computing the DFT • The time taken to evaluate a DFT on a computer depends principally on the number of multiplications involved. DFT needs 2 N multiplications. FFT only needs Nlog2(N) • The central insight which leads to this algorithm is the realization that a discrete Fourier transform of a sequence of N points can be written in terms of two discrete Fourier transforms of length N/2 • Thus if N is a power of two, it is possible to recursively apply this decomposition until we are left with discrete Fourier transforms of single points 13 Fast Fourier Transform(cont.) N 1 N 1 2 jnk / N nk Re‐writing X [ k ] x[ n ]e as X [ k ] x[ n ]W N n 0 n 0 nk It is easy to realize that the same values of W N are calculated many times as the computation proceeds Using the symmetric property of the twiddle factor, we can save lots of computations N 1 N 1 N 1 nk kn kn X [ k ] x[ n ]W N x ( n )W N x ( n )W N n 0 n 0 n 0 even n odd n N 2 1 N 2 1 2 kr k ( 2 r 1) x ( 2 r )W N x ( 2 r 1)W N r 0 r 0 N 2 1 N 2 1 kr k kr x1 ( r )W N 2 W N x 2 ( r )W N 2 r 0 r 0 k X 1 ( k ) W N X 2 ( k ) Thus the N‐point DFT can be obtained from two N/2‐point transforms, one on even input data, and one on odd input data. 14 Introduction for MATLAB MATLAB is a numerical computing environment developed by MathWorks. MATLAB allows matrix manipp,ulations, ppglotting of functions and data, and implementation of algorithms Getting help You can get help by typing the commands help or lookfor at the >> prompt, e.g. >> help fft Arithmetic operators Symbol Operation Example + Addition 3.1 + 9 ‐ Subtraction 6.2 – 5 * Multiplication 2 * 3 / Division 5 / 2 ^ Power 3^2 15 Data Representations in MATLAB Variables:Variablesaredefinedastheassignmentoperator“=”.Thesyntaxof variable assignment is varibliable name = a value (or an expressi)ion) For example, >> x = 5 x= 5 >> y = [3*7, pi/3]; % pi is in MATLAB Vectors/Matrices: MATLAB can create and manipu late arrays of 1 (ectors)(vectors), 2 (matrices), or more dimensions row vectors: a = [1, 2, 3, 4] is a 1X4 matrix column vectors: b = [5; 6; 7; 8; 9] is a 5X1 matrix, e.g.
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