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京都大学 情報学研究科 知能情報学専攻 2020年8月実施 専門科目 S-5

Author

realball

Description

The Fourier spectrum of a continuous-time signal f(t)f(t) is given by F(ω)=f(t)ejωtdtF(\omega) = \int_{-\infty}^{\infty}f(t)e^{-j\omega t}dt, where jj denotes the imaginary unit. Let X[k]X[k] denote discrete Fourier transform of a finite-length discrete signal of length NN, x[n] (n=0,,N1)x[n] \ (n = 0, \ldots, N-1). Answer the following questions.

Q.1

Suppose that f(t)f(t) is an even function, that is f(x)=f(x).f(x) = f(-x). The Fourier spectrum of f(t)f(t) is given by

F(ω)=20f(t) (A) dt.F(\omega) = 2 \int_0^{\infty} f(t) \boxed{\ (A)\ }dt.

Answer  (A) \boxed{\ (A)\ }. Calculation procedure must also be included in the answer.

Q.2

Let xs[n]x_s[n] be the circular shifted version of x[n]x[n] by ss,

xs[n]={x[N+ns](n<s)x[ns](ns)x_s[n] = \begin{cases} x[N+n-s] &(n<s) \\ x[n-s] &(n \geq s) \end{cases}

The discrete Fourier transform of xs[n]x_s[n] is given by  (B) X[k]\boxed{\ (B)\ }X[k].

Answer  (B) \boxed{\ (B)\ }. Calculation procedure must also be included in the answer.

Q.3

Let y[n]y[n] be a finite-length discrete signal of length 2N2N,

y[n]={x[n](0n<N)x[2N1n](Nn<2N).y[n] = \begin{cases} x[n] &(0 \leq n < N) \\ x[2N-1-n] &(N \leq n < 2N). \end{cases}

The discrete Fourier transform of y[n]y[n] is given by

Y[k]=2 (C) n=0N1x[n]cos( (D) ).Y[k] = 2 \boxed{\ (C)\ } \sum_{n=0}^{N-1} x[n] \cos (\boxed{\ (D)\ }).

Answer  (C) \boxed{\ (C)\ } and  (D) \boxed{\ (D)\ }. Calculation procedure must also be included in the answer.

Q.4

The transform of x[n]x[n],

XDCT[k]=αkn=0N1x[n]cos( (D) ),α0=1/N,αk=2/NX_{DCT}[k] = \alpha_k \sum_{n=0}^{N-1} x[n] \cos (\boxed{\ (D)\ }), \alpha_0 = 1/\sqrt{N}, \alpha_k = \sqrt{2/N}

is known as a discrete cosine tranform (DCT-II), and is used in data compression such as JPEG.

Explain an advantage of the discrete cosine transform compared to the discrete Fourier transform in terms of data compression.

题目描述

连续时间信号 f(t)f(t) 的 Fourier 变换为 F(ω)=f(t)ejωtdtF(\omega)=\int_{-\infty}^{\infty}f(t)e^{-j\omega t}\,dt。长度 NN 的离散信号为 x[n]x[n]n=0,,N1n=0,\ldots,N-1),其 DFT 记为 X[k]X[k]。回答各空并写出推导。

  1. f(t)f(t) 为偶函数,则
    F(ω)=20f(t)(A)dt.F(\omega)=2\int_0^\infty f(t)\boxed{(A)}\,dt.
    (A)(A)
  2. x[n]x[n] 循环右移 ss 位:
    xs[n]={x[N+ns],n<s,x[ns],ns.x_s[n]= \begin{cases} x[N+n-s],&n
    其 DFT 为 (B)X[k]\boxed{(B)}X[k]。求 (B)(B)
  3. 构造长度 2N2N 的偶对称延拓
    y[n]={x[n],0n<N,x[2N1n],Nn<2N.y[n]= \begin{cases} x[n],&0\le n<N,\\ x[2N-1-n],&N\le n<2N. \end{cases}
    其 DFT 写为
    Y[k]=2(C)n=0N1x[n]cos((D)).Y[k]=2\boxed{(C)} \sum_{n=0}^{N-1}x[n]\cos\bigl(\boxed{(D)}\bigr).
    (C),(D)(C),(D)
  4. 定义 DCT-II
    XDCT[k]=αkn=0N1x[n]cos((D)),α0=1N,αk=2N.X_{\mathrm{DCT}}[k]=\alpha_k \sum_{n=0}^{N-1}x[n]\cos\bigl(\boxed{(D)}\bigr), \quad \alpha_0=\frac1{\sqrt N},\quad \alpha_k=\sqrt{\frac2N}.
    从数据压缩角度说明 DCT 相比 DFT 的一个优势,例如其在 JPEG 中的使用。

Kai

Q.1

F(ω)=f(t)ejωtdt=f(t)[cos(ωt)jsin(ωt)dt]=f(t)cos(ωt)dtf(t)jsin(ωt)dt=f(t)cos(ωt)dt\begin{aligned} \mathcal{F}(\omega) &=\int_{-\infty}^{\infty} f(t)e^{-j\omega t}dt\\ &= \int_{-\infty}^{\infty}f(t)\left[\cos(\omega t)-j \sin(\omega t)dt \right]\\ &= \int_{-\infty}^{\infty}f(t)\cos(\omega t)dt - \int_{-\infty}^{\infty}f(t)j \sin(\omega t)dt\\ &= \int_{-\infty}^{\infty}f(t)\cos(\omega t)dt \end{aligned}

Thus, blank (A) is cos(ωt)\cos(\omega t).

Q.2

Xs[k]=n=0N1xs[n]ej2πNkn=n=0S1x[N+ns]ej2πNkn+n=sN1x[ns]ej2πNknassume 1:m=N+ns;n=m+sNassume 2:m=ns;n=m+s=m=NsN1x[m]ej2πNk(m+sN)+m=0NS1x[m]ej2πNk(m+s)=ej2πNksm=0N1x[m]ej2πNkmThus: xs[k]=X[k]ej2πNksThen: (B)=ej2πNks\begin{aligned} X_s[k]&= \sum_{n=0}^{N-1}x_s[n]e^{-j\frac{2\pi}{N}kn}\\ &=\sum_{n=0}^{S-1}x[N+n-s]e^{-j\frac{2\pi}{N}kn}+\sum_{n=s}^{N-1}x[n-s]e^{-j\frac{2\pi}{N}kn}\\ &\text{assume \textcircled{1}:} m=N+n-s;n=m+s-N\\ &\text{assume \textcircled{2}:} m=n-s;n=m+s\\ &=\sum_{m=N-s}^{N-1}x[m]e^{-j\frac{2\pi}{N}k(m+s-N)}+\sum_{m=0}^{N-S-1}x[m]e^{-j\frac{2\pi}{N}k(m+s)}\\ &=e^{-j\frac{2\pi}{N}ks}\sum_{m=0}^{N-1}x[m]e^{-j\frac{2\pi}{N}km}\\ &\text{Thus: } x_s[k]=X[k]e^{-j\frac{2\pi}{N}ks}\\ &\text{Then: } (B)=e^{-j\frac{2\pi}{N}ks} \end{aligned}

Q.3

Y[k]=n=02N1x[n]ej2π2Nkn=n=0N1x[n]ejπNkn+n=N2N1x[2N1n]ejπNkn=n=0N1x[n]ejπNkn+n=0N1x[N1n]ejπNkn=n=0N1x[n]ejπNkn+m=0N1x[m]ejπNk(N1m)=n=0N1x[n]ejπNkn+m=0N1x[m]ejπNk(m+1)=ejπ2Nk(n=0N1x[n]ejπk2n+12N+n=0N1x[n]ejπk2n+12N)=2ejπ2Nk(n=0N1x[n]ejπk2n+12N+ejπk2n+12N2)=2ejπ2Nk(n=0N1x[n]cos2n+12Nπk)\begin{aligned} Y[k] &= \sum_{n=0}^{2N-1}x[n] e^{-j\frac{2\pi}{2N}kn} \\ &= \sum_{n=0}^{N-1}x[n] e^{-j\frac{\pi}{N}kn} + \sum_{n=N}^{2N-1}x[2N-1-n] e^{-j\frac{\pi}{N}kn} \\ &= \sum_{n=0}^{N-1}x[n] e^{-j\frac{\pi}{N}kn} + \sum_{n=0}^{N-1}x[N-1-n] e^{-j\frac{\pi}{N}kn} \\ &= \sum_{n=0}^{N-1}x[n] e^{-j\frac{\pi}{N}kn} + \sum_{m=0}^{N-1}x[m] e^{-j\frac{\pi}{N}k(N-1-m)} \\ &= \sum_{n=0}^{N-1}x[n] e^{-j\frac{\pi}{N}kn} + \sum_{m=0}^{N-1}x[m] e^{j\frac{\pi}{N}k(m+1)} \\ &= e^{j\frac{\pi}{2N}k}\left( \sum_{n=0}^{N-1}x[n] e^{-j\pi k\frac{2n+1}{2N}} + \sum_{n=0}^{N-1}x[n] e^{j\pi k\frac{2n+1}{2N}} \right) \\ &= 2e^{j\frac{\pi}{2N}k}\left( \sum_{n=0}^{N-1}x[n] \frac{e^{-j\pi k\frac{2n+1}{2N}} + e^{j\pi k\frac{2n+1}{2N}}}{2} \right) \\ &= 2e^{j\frac{\pi}{2N}k}\left( \sum_{n=0}^{N-1}x[n] \cos \frac{2n + 1}{2N}\pi k \right) \end{aligned}

Q.4

Better concentrate energy