山东大学：《生物医学信号处理 Biomedical Signal Processing》精品课程教学资源（PPT课件讲稿）Chapter 08 The Discrete Fourier Transform

Chapter 8 The Discrete Fourier Transform ◆8.0 Introduction 8.1 Representation of Periodic sequence: the Discrete Fourier Series(DFS) 8.2 Properties of the dFs 8. 3 The Fourier Transform of Periodic signal 98.4 Sampling the Fourier Transform 98.5 Fourier Representation of Finite-Duration Sequence: the Discrete Fourier Transform FT 8.6 Properties of the dt 98.7 Linear Convolution using the DFT 8. 8 the discrete cosine transform DCT

2 Chapter 8 The Discrete Fourier Transform ◆8.0 Introduction ◆8.1 Representation of Periodic Sequence: the Discrete Fourier Series (DFS) ◆8.2 Properties of the DFS ◆8.3 The Fourier Transform of Periodic Signal ◆8.4 Sampling the Fourier Transform ◆8.5 Fourier Representation of Finite-Duration Sequence: the Discrete Fourier Transform(DFT) ◆8.6 Properties of the DFT ◆8.7 Linear Convolution using the DFT ◆8.8 the discrete cosine transform (DCT)

Chapter 8 The Discrete Fourier transform 8.0 Introduction

3 Chapter 8 The Discrete Fourier Transform 8.0 Introduction

8.0 Introduction Discrete Fourier Transform(DFT) is Transform of finite duration sequence DFT corresponds to samples equally spaced in frequency, of the Discrete-time Fourier transform(DTFT) of the signal DFT is a sequence rather than a function of a continuous variable o

4 8.0 Introduction ◆Discrete Fourier Transform (DFT) is Transform of finite duration sequence. ◆DFT corresponds to samples, equally spaced in frequency, of the Discrete-time Fourier transform (DTFT) of the signal. ◆DFT is a sequence rather than a function of a continuous variable ω

8.0 Introduction Derivation and interpretation of dft is based on relationship between periodic sequence and finite-length sequences The Fourier series representation of the periodic sequence corresponds to the DFT of the finite-length sequence

5 8.0 Introduction ◆Derivation and interpretation of DFT is based on relationship between periodic sequence and finite-length sequences： ◆The Fourier series representation of the periodic sequence corresponds to the DFT of the finite-length sequence

8.1 Representation of Periodic Sequence: the discrete fourier series Given a periodic sequence x[n] with period N so that xn]=xntrN The fourier series representation can be written as x[n]= Xk]ej(2T/N)kn Fourier series representation of continuous-time periodic signals require infinitely many complex exponentials, for discrete-time periodic signals 2丌 (k+mn)n ,(2zm) k=0,1,2,…,N-1

6 ◆Fourier series representation of continuous-time periodic signals require infinitely many complex exponentials, ◆for discrete-time periodic signals: 8.1 Representation of Periodic Sequence: the Discrete Fourier Series ( ) 2 j k N N m n e  + ◆The Fourier series representation can be written as 2 j kn N e  = ( ) 2 2 j kn N j mn e e   =

8.1 Representation of Periodic Sequence: the discrete Fourier series 2丌 (k+mn)n 2丌m ,k=0,1,2,…,N-1 Due to periodicity, we only need N complex exponentials for Fourier series representation of x[n] 2兀k 刘n=∑X[eN k Ankle N k=0 The Fourier series representation of periodic sequence x[n]

8 8.1 Representation of Periodic Sequence: the Discrete Fourier Series ◆Due to periodicity,we only need N complex exponentials   1 0 2 1 [ ] j N N k kn x n X k e N −  = =    2 1 [ ] N k j kn x n X k e N  =  ( ) 2 j k N N m n e  + ( ) 2 2 j kn N j mn e e   = 2 j kn N e  = , 0,1,2, , 1 k N = −

Discrete Fourier series pair 2丌 ◆ The fourier series:n=1∑[k]eN k=0 To obtain the Fourier series coefficients we multiply both sides(2m/ N)rn for0≤n≤N-1 and then sum both the sides we obtain ∑ x(n)en"=∑1∑x(k(krm 2丌 0 =0 k=0 2丌 ∑x(21e (k-r)n n=0

9 Discrete Fourier Series Pair ◆The Fourier series: 1 0 2 ( ) N n j n N r x n e −  = −  0 1 1 0 2 ( ) ( ) 1 N n N k j k r n N N X k e − −  = = − =  ◆To obtain the Fourier series coefficients we multiply both sides by for 0nN-1 and then sum both the sides , we obtain j n (2 / ) N r e −  1 1 0 0 2 ( ) ( ) 1 n N k N j n N k r N X k e − −  = = − =    1 0 2 1 [ ] j N N k kn x n e X k N −  = = 

Discrete Fourier series pair e点m(,kr=mN, n an intege 0. otherwise n=0 orthogonality of the complex exponentials Problem x(ne =∑(k)1 8.51 (k-r) 0 石N三X(+mN) (4)=∑(n)eNk=x() Periodic DFS N coefficients[n]=>X[k]e2r/n) k=0 The discrete Fourier series 10

10 Discrete Fourier Series Pair 1 0 2 1 ( ) N n j k r n N N e −  = −  = X( )r Problem 8.51 1 0 2 ( ) N n j n N r x n e −  = −  0 1 1 0 2 ( ) ( ) 1 N n N k j k r n N N X k e − −  = = − =  1, - , 0, k r mN m an integer otherwise  = =     (2 ) 1 / 0 1 [ ] j N N k kn x n e X N k  − = =  1 0 2 ( ) N n j n N k x n e −  = − X ( ) k = = X( ) r + mN The Discrete Fourier Series coefficients Periodic orthogonality of the complex exponentials. DFS r→k

8.1 Representation of Periodic equence: the Discrete Fourier Series a periodic sequence x[] with period N, [n]=x[n+rN] for any integer The Discrete Fourier series: =1 2丌 kn ∑X[k]e Synthesis equation k=0 ◆ Coefficients: 2丌 X()= Analysis r(n)eN equation n=0 12

12 8.1 Representation of Periodic Sequence: the Discrete Fourier Series ◆a periodic sequence x n  with period N, x n x n rN for any integer r   = +   ◆The Discrete Fourier Series: 1 0 2 ( ) ( ) , N n j kn X k x n N e −  = − =   1 0 2 1 [ ] , N k j kn N x n X k N e −  = =  Synthesis equation Analysis equation ◆Coefficients:

8. 1 Representation of periodic Sequence: the Discrete Fourier Series X[小]=∑川e j(2T/N) kn CThe sequence X k] is periodic with period N ￥0=X[M,X[=X[N+1 对k+N=∑小]e n=0 ∑划小e|e2=] n=0

13 8.1 Representation of Periodic Sequence: the Discrete Fourier Series ◆The sequence is X k  periodic with period N X X X X 0 , 1 1  = = + N N          ( ) 1 0 N 2 n j k n N N X k x n N e −  = − + + =      1 0 2 N 2 n j kn N j n x n X k e e  −  = −   − = =          ( ) 1 0 2 N n j N kn X k x n e  − = − = 