Now we can do something similar with the two 2-point DFT's to get equations for a; for j = 0,1,2,3: (DFT (αo, α2)o + DFT (αo, α2)1) (DFT (ao, a₂)o-DFT (αo, α₂)1) (DFT (α₁, α3)0 + DFT (α₁, α3)1) -(DFT (a1, a2)3 — DFT (α₁, α3)1). Exercise 1. Using the definition of DFT in Equation (3), prove Equations (11). αo 4₂= a₁ =

Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
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Please do Exercise 1 and please show step by step and explain

This text provides an explanation of the mathematical concepts related to the Discrete Fourier Transform (DFT) and its inverse (IDFT).

Equations (1) and (2) define the relationship between sequences of complex numbers and their transformations.

1. **Equation (1):**  
   \( s_j = \sum_{k=0}^{N-1} a_k e^{2\pi i kj/N} \)  
   This equation shows how to compute the sequence \(\{s_j\}\) from the sequence \(\{a_k\}\) using the DFT.

2. **Equation (2):**  
   \( a_n = \frac{1}{N} \sum_{j=0}^{N-1} s_j e^{-2\pi i jn/N} \)  
   This equation shows how to recover the original sequence \(\{a_k\}\) using the IDFT.

The text explains the symmetric relationship between the sequences \(\{a_n\}\) and \(\{s_j\}\). The notations introduced for convenience are:

- **DFT Notation:**  
  \( DFT(a_0, \ldots, a_{N-1}) := \sum_{k=0}^{N-1} a_k e^{2\pi i kj/N}, \quad j = 0, \ldots, N-1 \)  

- **IDFT Notation:**  
  \( IDFT(s_0, \ldots, s_{N-1}) := \frac{1}{N} \sum_{j=0}^{N-1} s_j e^{-2\pi i jn/N}, \quad n = 0, \ldots, N-1 \)  

These notations simplify expressions for DFT and IDFT as:

- \( \{s_j\} = DFT(\{a_j\}) \)
- \( \{a_i\} = IDFT(\{s_j\}) \)  

where \(\{s_j\}\) and \(\{a_j\}\) denote the sequences \(\{s_0, ..., s_{N-1}\}\) and \(\{a_0, ..., a_{N-1}\}\), respectively.

The importance of DFT and IDFT is emphasized in various fields such as modern science, engineering, and mathematics, where they are fundamental tools
Transcribed Image Text:This text provides an explanation of the mathematical concepts related to the Discrete Fourier Transform (DFT) and its inverse (IDFT). Equations (1) and (2) define the relationship between sequences of complex numbers and their transformations. 1. **Equation (1):** \( s_j = \sum_{k=0}^{N-1} a_k e^{2\pi i kj/N} \) This equation shows how to compute the sequence \(\{s_j\}\) from the sequence \(\{a_k\}\) using the DFT. 2. **Equation (2):** \( a_n = \frac{1}{N} \sum_{j=0}^{N-1} s_j e^{-2\pi i jn/N} \) This equation shows how to recover the original sequence \(\{a_k\}\) using the IDFT. The text explains the symmetric relationship between the sequences \(\{a_n\}\) and \(\{s_j\}\). The notations introduced for convenience are: - **DFT Notation:** \( DFT(a_0, \ldots, a_{N-1}) := \sum_{k=0}^{N-1} a_k e^{2\pi i kj/N}, \quad j = 0, \ldots, N-1 \) - **IDFT Notation:** \( IDFT(s_0, \ldots, s_{N-1}) := \frac{1}{N} \sum_{j=0}^{N-1} s_j e^{-2\pi i jn/N}, \quad n = 0, \ldots, N-1 \) These notations simplify expressions for DFT and IDFT as: - \( \{s_j\} = DFT(\{a_j\}) \) - \( \{a_i\} = IDFT(\{s_j\}) \) where \(\{s_j\}\) and \(\{a_j\}\) denote the sequences \(\{s_0, ..., s_{N-1}\}\) and \(\{a_0, ..., a_{N-1}\}\), respectively. The importance of DFT and IDFT is emphasized in various fields such as modern science, engineering, and mathematics, where they are fundamental tools
Now we can do something similar with the two 2-point DFT’s to get equations for \( a_j \) for \( j = 0, 1, 2, 3 \):

\[
\begin{align*}
a_0 &= \frac{1}{2} \left( \text{DFT}(a_0, a_2)_0 + \text{DFT}(a_0, a_2)_1 \right) \\
a_2 &= \frac{1}{2} \left( \text{DFT}(a_0, a_2)_0 - \text{DFT}(a_0, a_2)_1 \right) \\
a_1 &= \frac{1}{2} \left( \text{DFT}(a_1, a_3)_0 + \text{DFT}(a_1, a_3)_1 \right) \\
a_3 &= \frac{1}{2} \left( \text{DFT}(a_1, a_3)_0 - \text{DFT}(a_1, a_3)_1 \right)
\end{align*}
\]

Exercise 1. Using the definition of DFT in Equation (3), prove Equations (11). 

◊
Transcribed Image Text:Now we can do something similar with the two 2-point DFT’s to get equations for \( a_j \) for \( j = 0, 1, 2, 3 \): \[ \begin{align*} a_0 &= \frac{1}{2} \left( \text{DFT}(a_0, a_2)_0 + \text{DFT}(a_0, a_2)_1 \right) \\ a_2 &= \frac{1}{2} \left( \text{DFT}(a_0, a_2)_0 - \text{DFT}(a_0, a_2)_1 \right) \\ a_1 &= \frac{1}{2} \left( \text{DFT}(a_1, a_3)_0 + \text{DFT}(a_1, a_3)_1 \right) \\ a_3 &= \frac{1}{2} \left( \text{DFT}(a_1, a_3)_0 - \text{DFT}(a_1, a_3)_1 \right) \end{align*} \] Exercise 1. Using the definition of DFT in Equation (3), prove Equations (11). ◊
Expert Solution
Step 1

Given: D.F.T.α0,.....αN-1j=k=0N-1αke2πikjN,   j=0,1,....N-1
To find: Expressions of two 2-point Discrete Fourier transform in terms of the 4-point Discrete Fourier transform.

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