The auto-correlation function a(t) associated with the signal x(t) is given as y(t) x(t) * x*(-t). - Using CTFT properties, derive an expression for Y(jw), the CTFT of y(t), in terms of X(jw) and other relevant variables.

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**Auto-Correlation Function and CTFT Explanation**

The auto-correlation function \( a(t) \) associated with the signal \( x(t) \) is given as:

\[ y(t) = x(t) \ast x^*(-t) \]

Using Continuous-Time Fourier Transform (CTFT) properties, derive an expression for \( Y(j\omega) \), the CTFT of \( y(t) \), in terms of \( X(j\omega) \) and other relevant variables. 

**Explanation**:

- **Auto-correlation Function**: This mathematical operation measures the similarity of a signal with a time-shifted version of itself. For a signal \( x(t) \), the auto-correlation is expressed as \( y(t) = x(t) \ast x^*(-t) \), where \( x^*(-t) \) is the complex conjugate and time-reversal of \( x(t) \).
  
- **CTFT and its Use**: The Continuous-Time Fourier Transform (CTFT) is a tool used to analyze the frequency content of continuous signals. Deriving \( Y(j\omega) \) involves utilizing CTFT properties to express the frequency representation of the auto-correlation function \( y(t) \).
Transcribed Image Text:**Auto-Correlation Function and CTFT Explanation** The auto-correlation function \( a(t) \) associated with the signal \( x(t) \) is given as: \[ y(t) = x(t) \ast x^*(-t) \] Using Continuous-Time Fourier Transform (CTFT) properties, derive an expression for \( Y(j\omega) \), the CTFT of \( y(t) \), in terms of \( X(j\omega) \) and other relevant variables. **Explanation**: - **Auto-correlation Function**: This mathematical operation measures the similarity of a signal with a time-shifted version of itself. For a signal \( x(t) \), the auto-correlation is expressed as \( y(t) = x(t) \ast x^*(-t) \), where \( x^*(-t) \) is the complex conjugate and time-reversal of \( x(t) \). - **CTFT and its Use**: The Continuous-Time Fourier Transform (CTFT) is a tool used to analyze the frequency content of continuous signals. Deriving \( Y(j\omega) \) involves utilizing CTFT properties to express the frequency representation of the auto-correlation function \( y(t) \).
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