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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, 祭音Myyura

Description​

大学公表の原題 The Fourier spectrum of a continuous-time signal f(t)f(t) is given by F(ω)=∫−∞∞f(t)e−jωtdtF(\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,…,N−1)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)=2∫0∞f(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+n−s](n<s)x[n−s](n≥s)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](0≤n<N)x[2N−1−n](N≤n<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=0N−1x[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]=αk∑n=0N−1x[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)e−jωt dtF(\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,…,N−1n=0,\ldots,N-1),其 DFT 记为 X[k]X[k]。回答各空并写出推导。

  1. 若 f(t)f(t) 为偶函数,则

    F(ω)=2∫0∞f(t)(A) dt.F(\omega)=2\int_0^\infty f(t)\boxed{(A)}\,dt.

    求 (A)(A)。

  2. 将 x[n]x[n] 循环右移 ss 位:

    xs[n]={x[N+n−s],n<s,x[n−s],n≥s.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],0≤n<N,x[2N−1−n],N≤n<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=0N−1x[n]cos⁡((D)).Y[k]=2\boxed{(C)} \sum_{n=0}^{N-1}x[n]\cos\bigl(\boxed{(D)}\bigr).

    求 (C),(D)(C),(D)。

  4. 定义 DCT-II

    XDCT[k]=αk∑n=0N−1x[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)e−jωtdt=∫−∞∞f(t)[cos⁡(ωt)−jsin⁡(ωt)]dt=∫−∞∞f(t)cos⁡(ωt)dt−j∫−∞∞f(t)sin⁡(ωt)dt=∫−∞∞f(t)cos⁡(ωt)dt=2∫0∞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)\right]dt\\ &= \int_{-\infty}^{\infty}f(t)\cos(\omega t)dt-j\int_{-\infty}^{\infty}f(t)\sin(\omega t)dt\\ &= \int_{-\infty}^{\infty}f(t)\cos(\omega t)dt =2\int_0^\infty f(t)\cos(\omega t)dt \end{aligned}

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

Q.2​

Xs[k]=∑n=0N−1xs[n]e−j2πNkn=∑n=0s−1x[N+n−s]e−j2πNkn+∑n=sN−1x[n−s]e−j2πNknassume 1◯:m=N+n−s;n=m+s−Nassume 2◯:m=n−s;n=m+s=∑m=N−sN−1x[m]e−j2πNk(m+s−N)+∑m=0N−s−1x[m]e−j2πNk(m+s)=e−j2πNks∑m=0N−1x[m]e−j2πNkmThus: Xs[k]=X[k]e−j2πNksThen: (B)=e−j2πNks\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=02N−1y[n]e−j2π2Nkn=∑n=0N−1x[n]e−jπNkn+∑n=N2N−1x[2N−1−n]e−jπNkn=∑n=0N−1x[n]e−jπNkn+∑m=0N−1x[m]e−jπNk(2N−1−m)=∑n=0N−1x[n]e−jπNkn+∑m=0N−1x[m]ejπNk(m+1)=ejπ2Nk(∑n=0N−1x[n]e−jπk2n+12N+∑n=0N−1x[n]ejπk2n+12N)=2ejπ2Nk(∑n=0N−1x[n]e−jπk2n+12N+ejπk2n+12N2)=2ejπ2Nk(∑n=0N−1x[n]cos⁡2n+12Nπk)\begin{aligned} Y[k] &= \sum_{n=0}^{2N-1}y[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_{m=0}^{N-1}x[m] e^{-j\frac{\pi}{N}k(2N-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}

Thus, (C)=ejπk/(2N)(C)=e^{j\pi k/(2N)} and (D)=(2n+1)πk/(2N)(D)=(2n+1)\pi k/(2N).

Q.4​

The even extension reduces the boundary discontinuity. For correlated data, the DCT therefore concentrates most energy into a few real, low-frequency coefficients, so the remaining coefficients can be coarsely quantized or discarded.