DFT and FFT

 

4. DFT and convolution

Task

Let \(h(n)\) be the sequence \(\{1,1,0,0,0,0,0,0\}\) and \(y(n)=\{1,1,1,1,0,0,0,0\}\).

  1. Calculate the DFT of length \(8\) for both sequences.
  2. Determine with help of the DFT a sequence \(v(n)\) such that \(y(n)= h(n) \circledast v(n)\).
  3. Let \(z(n)\) be the result of the linear convolution of \(h(n)\) and \(v(n)\): \(z(n)=h(n)*v(n)\). Is \(z(n)=y(n)\)?

Amount and difficulty

  • Working time: approx. 15 minutes
  • Difficulty: easy

 

5. DFT

Task

The time-limited signal

\( v_0(t)=\left\{\begin{array}{ll} \sin(\omega_0 t) & for\quad 0\le t< 4\pi/\omega_0 \\ 0 & otherwise \end{array}\right. \)

is sampled with \(t_n=n T_{\textrm{A}}=n\frac{ \pi}{4 \omega_0}\) to produce the time-limited sequence \(v(n)\) .

  1. Sketch \(v_0(t)\).
  2. Determine the DFT of \(v(n)\).
  3. Determine the Fourier Transform \(V(e^{j\Omega})\) of \(v(n)\).
  4. Explain the connection between the DFT\(\{v(n)\}\) and \(V(e^{j\Omega})\).

Amount and difficulty

  • Working time: approx. 20 minutes
  • Difficulty: easy

 

6. DFT, zero padding, leakage

Task

Let \(v_{\textrm{a}}(t)\) be a time-continuous periodic signal \begin{equation} v_{\textrm{a}}(t)= 1+ cos(2\pi 40t) + 3\cdot cos(2\pi 120t). \nonumber \end{equation} The signal is sampled (\(\omega_s = 2\pi 280 s^{-1}\)) to produce the sequence \(v(n)\). For practical purposes (delay, complexity) the sequence is limited to \(L\) samples. \(M\) is the length of the DFT. Use MATLAB to solve the following subproblems.

  1. Sketch \(v_{\textrm{a}}(t)\), \(v(n)\), the Fourier transform \(V(e^{j\Omega})\) and the DFT \(V_M(\mu)\) for \(L=7\) and \(M=7\).
  2. Sketch \(v_{\textrm{a}}(t)\), \(v(n)\), the Fourier transform \(V(e^{j\Omega})\) and the DFT \(V_M(\mu)\) for \(L=7\) and \(M=14\) (zero padding).
  3. Sketch \(v_{\textrm{a}}(t)\), \(v(n)\), the Fourier transform \(V(e^{j\Omega})\) and the DFT \(V_M(\mu)\) for \(L=28\) and \(M=28\).
  4. Sketch \(v_{\textrm{a}}(t)\), \(v(n)\), the Fourier transform \(V(e^{j\Omega})\) and the DFT \(V_M(\mu)\) for \(L=7\) and \(M=7\).
  5. Sketch \(v_{\textrm{a}}(t)\), \(v(n)\), the Fourier transform \(V(e^{j\Omega})\) and the DFT \(V_M(\mu)\) for \(L=28\) and \(M=56\) (zero padding).
  6. Sketch \(v_{\textrm{a}}(t)\), \(v(n)\), the Fourier transform \(V(e^{j\Omega})\) and the DFT \(V_M(\mu)\) for \(L=14\) and \(M=15\) (zero padding).
  7. Sketch \(v_{\textrm{a}}(t)\), \(v(n)\), the Fourier transform \(V(e^{j\Omega})\) and the DFT \(V_M(\mu)\) for \(L=14\) and \(M=21\) (zero padding).
  8. Sketch \(v_{\textrm{a}}(t)\), \(v(n)\), the Fourier transform \(V(e^{j\Omega})\) and the DFT \(V_M(\mu)\) for \(L=30\) and \(M=30\).
  9. Sketch \(v_{\textrm{a}}(t)\), \(v(n)\), the Fourier transform \(V(e^{j\Omega})\) and the DFT \(V_M(\mu)\) for \(L=15\) and \(M=30\) (zero padding).

Amount and difficulty

  • Working time: approx. 25 minutes
  • Difficulty: middle

 

7. FFT

Task

Let \(v(n)\) be a time-discrete signal \(v(n) = [v(0), v(1), v(2), v(3), v(4), v(5), v(6), v(7)]\).

  1. Separate the signal \(v(n)\) into even and odd time-indices \(v_1(n)\) and \(v_2(n)\) respectively and find the DFT expression for each separated sequence.
  2. Now compute the DFT of \(v(n)\) using the above expressions.
  3. Sketch the signal flow diagrams when DFT is directly applied to \(v(n)\) and as shown in part (b). Show the reduction in complexity by computing the number of complex multiplications for each method.
  4. Can the complexity be reduced further? If yes then find the final expression.
  5. Sketch the complete signal flow for part (d).

Amount and difficulty

  • Working time: approx. 15 minutes
  • Difficulty: middle

 

8. FFT

Task

The \(M\)-point DFT of the \(M\)-point sequence \(x(n) = e^{-j(\pi/M)n^2}\), for \(M\) even, is

\(X(\mu) = \sqrt{M}e^{-j\pi/4}e^{j(\pi/M)\mu^2}\).

Determine the \(2M\)-point of sequence \(y(n) = e^{-j(\pi/M)n^2}\), assuming that \(M\) is even.

Amount and difficulty

  • Working time: approx. 15 minutes
  • Difficulty: middle

 

9. FFT of real and complex sequences

Task

Suppose that an FFT program is available that computes the DFT of a complex sequence. If we wish to compute the DFT of a real sequence, we may simply specify the imaginary part to be zero and use the program directly. However, the symmetry of the DFT of a real sequence can be used to reduce the amount of computation.

    1. Let \(x(n)\) be a real-valued sequence of length \(M\), and let \(X(\mu)\) be its DFT with real and imaginary parts denoted \(X_R(\mu)\) and \(X_I(\mu)\), respectively; i.e.,

      \(X(\mu) = X_R(\mu) + j\,X_I(\mu)\).

      Show that if \(x(n)\) is real, then \(X_R(\mu) = X_R(M - \mu)\) and \(X_I(\mu) = -X_I(M - \mu)\) for \(\mu = 1,...,M-1\).
    2. Now consider two real-valued sequences \(x_1(n)\) and \(x_2(n)\) with DFTs \(X_1(\mu)\) and \(X_2(\mu)\), respectively. Let \(g(n)\) be the complex sequence \(g(n) = x_1(n) + j\,x_2(n)\), with corresponding DFT \(G(\mu) = G_R(\mu) + j\,G_I(\mu)\). Also, let \(G_{OR}(\mu)\), \(G_{ER}(\mu)\), \(G_{OI}(\mu)\) and \(G_{EI}(\mu)\) denote, respectively, the odd part of the real part, the even part of the real part, the odd part of the imaginary part, and the even part of the imaginary part of \(G(\mu)\). Specifically, for \(1 \leq \mu \leq M-1\),

      \(G_{OR}(\mu) = 1/2\{G_R(\mu) - G_R(M - \mu)\}\),

      \(G_{ER}(\mu) = 1/2\{G_R(\mu) + G_R(M - \mu)\}\),

      \(G_{OI}(\mu) = 1/2\{G_I(\mu) - G_I(M - \mu)\}\),

      \(G_{EI}(\mu) = 1/2\{G_I(\mu) + G_I(M - \mu)\}\),

      and \(G_{OR}(0) = G_{OI}(0) = 0\), \(G_{ER}(0) = G_{R}(0)\), \(G_{EI}(0) = G_{I}(0)\). Determine expressions for \(X_1(\mu)\) and \(X_2(\mu)\) in terms of \(G_{OR}(\mu)\), \(G_{ER}(\mu)\), \(G_{OI}(\mu)\) and \(G_{EI}(\mu)\).

Amount and difficulty

  • Working time: approx. 15 minutes
  • Difficulty: middle

Recent Publications

P. Durdaut, J. Reermann, S. Zabel, Ch. Kirchhof, E. Quandt, F. Faupel, G. Schmidt, R. Knöchel, and M. Höft: Modeling and Analysis of Noise Sources for Thin-Film Magnetoelectric Sensors Based on the Delta-E Effect, IEEE Transactions on Instrumentation and Measurement, published online, 2017

P. Durdaut, S. Salzer, J. Reermann, V. Röbisch, J. McCord, D. Meyners, E. Quandt, G. Schmidt, R. Knöchel, and M. Höft: Improved Magnetic Frequency Conversion Approach for Magnetoelectric Sensors, IEEE Sensors Letters, published online, 2017

 

Website News

18.06.2017: Page about KiRAT news added (also visible in KiRAT).

31.05.2017: Some pictures added.

23.04.2017: Time line for the lecture "Adaptive Filters" added.

13.04.2017: List of PhD theses added.

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

Recent News

Alexej Namenas - A New Guy in the Team

In June Alexej Namenas started in the DSS Team. He will work on real-time tracking algorithms for SONAR applications. Alexej has done both theses (Bachelor and Master) with us. The Bachelor thesis in audio processing (beamforming) and the Master thesis in the medical field (real-time electro- and magnetocardiography). In addition, he has intership erperience in SONAR processing.

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