京都大学 情報学研究科 知能情報学専攻 2024年8月実施 専門科目 S-4

标签：

• Kyoto-University
• Convolution-Theorem
• Sampling-Theorem-and-Aliasing
• Fourier-Analysis

Author

itsuitsuki

Description (English)

Let us define the Fourier transform $\mathcal{F}[x(t)]$ of a real function $x(t)$ and the inverse Fourier transform $\mathcal{F}^{-1}[X(\omega)]$ of a function $X(\omega)$ with the following formulas, where $t$ and $\omega$ denote real numbers, and $j = \sqrt{-1}$.

$$\mathcal{F}[x(t)] = \int_{-\infty}^{\infty} x(t)e^{-j\omega t} \mathrm d t \\ \mathcal{F}^{-1}[X(\omega)] = \frac{1}{2\pi} \int_{-\infty}^{\infty} X(\omega)e^{j\omega t} \mathrm d\omega$$

Answer the following questions, where $T_0, \omega_0$, and $T$ denote positive constants.

Q.1

Prove that the following equation holds for real functions $f(t)$ and $g(t)$, where $*$ denotes convolution.

$$\mathcal{F}[f(t)g(t)] = \frac{1}{2\pi} \mathcal{F}[f(t)] * \mathcal{F}[g(t)]$$

Q.2

Compute the Fourier transform of the functions given below. (1) $x_1(t) = \frac{1}{2}(\text{sgn}(T_0 - t) + \text{sgn}(T_0 + t))$,

$$\text{where } \text{sgn}(t) = \begin{cases} -1 & (t < 0) \\ 0 & (t = 0) \\ 1 & (t > 0) \end{cases}$$

(2) $x_2(t) = \begin{cases} \frac{\sin \omega_0 t}{\pi t} & (t \neq 0) \\ \frac{\omega_0}{\pi} & (t = 0) \end{cases}$

Q.3

Let $x_s(t, T) = x_2(t)\delta_T(t)$ be a signal sampled from $x_2(t)$ in Q.2 using a comb function $\delta_T(t) = \sum_{k=-\infty}^{\infty} \delta(t - kT)$, where $\delta(t)$ denotes the Dirac delta function. Answer the following questions. You may use that $\mathcal{F}[\delta_T(t)] = \frac{1}{T} \sum_{k=-\infty}^{\infty} \delta(\omega - \frac{k}{T})$ holds.

(1) Draw the graph of $\mathcal{F}[x_s(t, \frac{1}{3\omega_0})]$ in the range of $|\omega| \leq 3\omega_0$.

(2) Show the condition for $T$ to satisfy $\mathcal{F}[x_2(t)] = \mathcal{F}[x_s(t, T)]$ in the range of $|\omega| \leq \omega_0$.

(3) Draw the graph of $\mathcal{F}[x_s(t, \frac{2}{3\omega_0})]$ in the range of $|\omega| \leq 3\omega_0$.

(4) Draw the graph of $\mathcal{F}^{-1}[X_s(\omega)]$ in the range of $|t| \leq \frac{\pi}{\omega_0}$. $X_s(\omega)$ is given below.

$$X_s(\omega) = \begin{cases} \mathcal{F}[x_s(t, \frac{2}{3\omega_0})] & (|\omega| \leq \omega_0) \\ 0 & (|\omega| > \omega_0) \end{cases}$$