# Advanced Signals and Systems - Fourier Transformation

### 9. Fourier transform of a rectangular function.

Find the spectrum $$R_N(e^{j\Omega})$$ of the discrete rectangular function $$r_N(n)$$ given in the figure below.

## Amount and difficulty

• Working time: approx. xx minutes
• Difficulty: ?

Sketches for $$T=1$$, $$T_S = 0.1$$ and $$N=10$$:

## Solution

The spectrum of the discrete rectangular function \begin{equation*} r_N(n) = \begin{cases} 1 &\text{, for } |n|\leq N\\ 0 &\text{, otherwise} \end{cases} \ \ \ \circ-\bullet \ \ \ R_N(e^{j\Omega}) = \sum \limits_{n=-\infty}^\infty r_N(n)e^{-j\Omega n} \end{equation*} Using the following geometric series \begin{equation*} \sum \limits_{n=0}^N q^n = \frac{q^{N+1}-1}{q-1} \ \ \ q \neq 1 \label{eq:geometricseries} \end{equation*} it can be concluded that \begin{align*} R_N(e^{j\Omega}) &= \frac{\sin\left(\frac{\Omega}{2} (2N+1)\right)}{\sin\left(\frac{\Omega}{2}\right)} \ \ \ \text{for } \Omega \neq \lambda \cdot 2\pi \ \ \lambda\in \mathbb{Z} \end{align*} Since the calculation of the Fourier transform at $$\Omega = 0 ( = \lambda \cdot 2 \pi)$$ leads to \begin{align*}R_N(e^{j 0 }) &= \sum \limits_{n=-\infty}^\infty r_N(n)e^{-j 0 n} = 2N+1 \text { .} \end{align*} It follows that \begin{align*}R_N(e^{j \Omega }) = 2N+1 \ \ \ \text{for } \Omega \neq \lambda \cdot 2\pi \ \ \lambda\in \mathbb{Z} \text{ .} \end{align*}

### 10. Fourier transform of a right-sided exponential function.

Sample the signal \begin{eqnarray*} v_0(t) = e^{-\alpha t}\;\delta_{-1}(t), \hspace{1cm} \alpha>0. \end{eqnarray*} with sampling period $$T_S$$.

1. Determine the spectrum $$V(e^{j\Omega})$$ of the sample values $$v(n)=v_0(nT_S)$$.
2. Show that $$V(e^{j\Omega})$$ is the periodically repeated spectrum $$V(j\omega)$$ (challenging!).
3. Does aliasing occur?

## Amount and difficulty

• Working time: approx. xx minutes
• Difficulty: ?

## Solution

sampling period $$T_S$$.

1. Consideration of $$v_o(n T_s)$$ and the corresponding spectrum $$V(e^{j\Omega})$$. \begin{equation*} v_o(nT_s) = e^{-\alpha n T_s} \cdot \delta_{-1} (nT_s) = {\underbrace{(e^{-\alpha T_s})}_{= \rho }}^n \cdot \delta_{-1} (nT_s) \end{equation*} Using the substitution with $$\rho$$ and the geometric series, like explained in the previous task (see formula of geometric series) for the infinite case, it follows that \begin{equation*} V(e^{j\Omega}) = \lim \limits _{n \rightarrow \infty} \frac{\left(\rho \cdot e^{-j\Omega}\right)^n-1}{\rho \cdot e^{-j\Omega} - 1} = \frac{e^{j\Omega}}{e^{j\Omega} - \rho} \end{equation*} At this point, it is not obvious that $$V(e^{j\Omega})$$ is the periodically repeated spectrum $$V_o(j\omega)= \frac{1}{j\omega + \alpha}$$. See (b) for further considerations.

2. Rewrite $$V(e^{j\Omega})$$ as an infinite sum of $$\Omega$$ dependent terms. At first expand $$e^{j\Omega}$$ around 0 using a Taylor series. In general \begin{equation*} e^x = \sum \limits_{n=0}^\infty \frac{x^n}{n!} \ \ \ \text{here} \ \ \ e^{j\Omega} = \sum \limits_{n=0}^\infty \frac{(j\Omega)^n}{n!} \text{ .} \end{equation*} It follows that \begin{equation*} V(e^{j\Omega}) = \frac{e^{j\Omega}}{e^{j\Omega} - \rho} = \frac{e^{j\Omega}}{ \sum \limits_{n=0}^\infty \frac{(j\Omega)^n}{n!} - \rho} \text{ .} \end{equation*} The denominator is a polynomial. So expansion into partial fractions is possible. \begin{equation*} V(e^{j\Omega}) = \frac{e^{j\Omega}}{e^{j\Omega} - \rho} = \frac{e^{j\Omega}}{ \sum \limits_{n=0}^\infty \frac{(j\Omega)^n}{n!} - \rho} = \sum \limits_{\lambda=-\infty}^\infty \frac{B_\lambda}{\Omega - \Omega_{\infty \lambda}}\text{ .} \end{equation*}
1. Roots of denominator: \begin{align*} e^{j\Omega} - \rho = 0 &\Rightarrow e^{j\Omega} = \rho \ \ \ e^{j(\Omega-\lambda2\pi)} = \rho \ \ \ \lambda \in \mathbb{Z} \\ & \Rightarrow j\Omega = \ln \rho + j2\pi\lambda \\ & \Rightarrow \Omega_{\infty \lambda} = 2\pi \cdot \lambda- j \ln \rho \ \ \ \lambda\in\mathbb{Z} \end{align*}
2. Coefficients $$B_\lambda$$: \begin{equation*} B_\lambda = \lim \limits_{\Omega \rightarrow \Omega_{\infty \lambda}} [V(e^{j\Omega})(\Omega - \Omega_{\infty \lambda})] \end{equation*} By applying L'Hospital's rule, it can be concluded that \begin{equation*} B_\lambda = \frac{1}{j} \end{equation*} \end{enumerate} Inserting the poles and zeros into the partial fraction expansion it follows \begin{equation*} V(e^{j\Omega}) = \sum \limits_{\lambda=-\infty}^\infty \frac{B_\lambda}{\Omega - \Omega_{\infty \lambda}} = \sum \limits_{\lambda=-\infty}^\infty \frac{1}{j\Omega - j2\pi\lambda -\ln\rho} \text{ .} \end{equation*} Using $$\Omega = \omega T_s$$ and $$\alpha T_s = - \ln \rho$$, the spectrum can be rearranged to $$V(e^{j\Omega}) = \frac{1}{T_s} \sum \limits_{\lambda=-\infty}^\infty \frac{1}{j(\omega - \lambda \frac{2\pi}{T_s}) + \alpha} \text{ .} \label{eq:resultRightSided}$$ We know that in general that $$V(e^{j\Omega}) = \frac{1}{T_s} \sum \limits_{\lambda=-\infty}^\infty V_o \left( j \left( \omega + \lambda \frac{2\pi}{T_s} \right) \right) = \frac{1}{T_s} \sum \limits_{\lambda=-\infty}^\infty \frac{1}{j(\omega - \lambda \frac{2\pi}{T_s}) + \alpha} \text{ .} \label{eq:assumptionRightSided}$$ As equation \eqref{eq:resultRightSided} and \eqref{eq:assumptionRightSided} are equal, we have shown that $$V(e^{j\Omega})$$ is the periodically repeated spectrum of $$V_o(e^{j\Omega}) = \frac{1}{j\omega +\alpha}$$.

3. Aliasing is unavoidable since $$V_o(e^{j\Omega})$$ is not band limited.

### 11. Inverse Fourier Transform.

Find the time-domain signal $$r_0(t)$$ of the following rectangular spectrum

\begin{eqnarray*} R_0(j\omega) = \left\{\begin{array}{r@{\quad,\qquad}l} 1 & \hspace{0.2cm} |\omega| \leq \omega_C\\ 0 & \hspace{0.3cm}\mbox{otherwise.} \end{array} \right. \end{eqnarray*}

Let $$V(e^{j\Omega})$$ be the alias-free periodic repetition of the rectangular spectrum as illustrated below. Determine the corresponding time-domain sequence $$v(n)$$.

## Solution

This problem can be solved in various ways, one is given in this solution. We know that \begin{equation*} v_R(t) = \begin{cases} 1 &, |t| \leq T\\ 0 &, \text{ otherwise} \end{cases} \ \ \ \circ-\bullet \ \ \ V_R(j\omega) = 2T \cdot \text{si} (\omega T) \text{ .} \end{equation*} Using symmetry relations of the Fourier transform \begin{align*} v(t) \ \ \ &\circ-\bullet \ \ \ V(j\omega)\\ V(jt) \ \ \ &\circ-\bullet \ \ \ 2\pi \cdot V(-\omega) \end{align*} we can conclude that \begin{equation*} V_R(jt) = 2T \cdot \text{si} (tT) \ \ \ \circ-\bullet \ \ \ 2\pi \cdot v_R(-\omega) = 2\pi \cdot v_R(\omega) \text { .} \end{equation*} After some rearrangements and substitution of $$T=\omega_c$$, the inverse transformation is given by \begin{equation*} \underbrace{\frac{\omega_c}{\pi} \cdot \text{si}(\omega_ct)}_{= \, r_0(t)} \ \ \ \circ-\bullet \ \ \ \underbrace{v_R(\omega)}_{= \, R_0(j\omega)} \end{equation*} The given rectangular function can be used to define the spectrum of the second part. $$V(e^{j\Omega}) = \sum \limits_{\lambda=-\infty}^{\infty} R_{\Omega_c}\left( j\left( \Omega - \lambda \cdot 2\pi \right) \right) \label{eq:rectPeriodSpec}$$ For determining the sequence $$v(n)$$, we will first determine the spectrum $$V_o(j\omega)$$, then $$v_o(t)$$ and by sampling this, $$v(n)$$.
1. Determination of $$V_o(j\omega)$$:\\ By setting $$\lambda=0$$ in equation \eqref{eq:rectPeriodSpec} we can define the spectrum \begin{equation*} V_o(j\omega) = T_s \cdot R_{\Omega_c}\left(j(\omega T_s)\right) = T_s \cdot R_0(j\omega) \end{equation*}
2. Determination of $$v_o(t)$$: \begin{equation*} v_o(t) = T_s \cdot \frac{\omega_c}{\pi} \cdot \text{si}(\omega_ct) \end{equation*}
3. Determination of $$v(n)$$: \begin{equation*} v(n) = \frac{\Omega_c}{\pi} \cdot \text{si}(\Omega_c n) \end{equation*}

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### Recent Publications

J. Reermann, P. Durdaut, S. Salzer, T. Demming, A. Piorra, E. Quandt, N. Frey, M. Höft, and G. Schmidt: Evaluation of Magnetoelectric Sensor Systems for Cardiological Applications, Measurement (Elsevier), ISSN 0263-2241, https://doi.org/­10.1016/­j.measurement.2017.09.047, 2017

S. Graf, T. Herbig, M. Buck, G. Schmidt: Low-Complexity Pitch Estimation Based on Phase Differences Between Low-Resolution Spectra, Proc. Interspeech, pp. 2316 -2320, 2017

### Contact

Prof. Dr.-Ing. Gerhard Schmidt

E-Mail: gus@tf.uni-kiel.de

Christian-Albrechts-Universität zu Kiel
Faculty of Engineering
Institute for Electrical Engineering and Information Engineering
Digital Signal Processing and System Theory

Kaiserstr. 2
24143 Kiel, Germany

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