Chapter 24: Problem 1
Find, using Table 2.1, the Fourier transform of $$ f(t)=\left\\{\begin{array}{lc} 1 & -1 \leq t \leq 1 \\ 0 & \text { otherwise } \end{array}\right. $$
Short Answer
Expert verified
Answer: The Fourier transform of the given function f(t) is F(ω) = (2/ω) sin(ω).
Step by step solution
01
Determine the form of the function
The given function is a rectangular function defined as follows:
$$
f(t)=\left\\{\begin{array}{lc}
1 & -1 \leq t \leq 1 \\\
0 & \text { otherwise }
\end{array}\right.
$$
02
Find the corresponding Fourier transform in Table 2.1
The function f(t) we determined in Step 1 can be rewritten as a product of the unit step function, as follows:
$$
f(t) = u(t+1) - u(t-1)
$$
With u(t) being the unit step function. Now, we can find the corresponding Fourier transform in Table 2.1.
03
Calculate Fourier Transform
Since f(t) = u(t+1) - u(t-1), the linearity property of the Fourier transform allows us to calculate the Fourier transform of f(t) by subtracting the transforms of u(t-1) and u(t+1). Let's denote the Fourier Transform of f(t) as F(ω).
The Fourier transform of the unit step function is given in Table 2.1 as:
$$
\mathcal{F}\left\\{u(t-a)\\right\\} = \frac{1}{j\omega} e^{-j\omega a}
$$
For a=1, we have the Fourier transform of u(t-1) and u(t+1):
$$
\mathcal{F}\left\\{u(t-1)\\right\\} = \frac{1}{j\omega} e^{-j\omega}
$$
$$
\mathcal{F}\left\\{u(t+1)\\right\\} = \frac{1}{j\omega} e^{j\omega}
$$
Now, we can find the Fourier transform of f(t):
$$
F(ω) = \mathcal{F}\left\\{f(t)\\right\\} = \mathcal{F}\left\\{u(t+1) - u(t-1)\\right\\}
$$
Applying the linearity property, we get:
$$
F(ω) = \frac{1}{j\omega} e^{j\omega} - \frac{1}{j\omega} e^{-j\omega}
$$
Finally, we can simplify the expression to obtain the Fourier transform of f(t):
$$
F(ω) = \frac{2}{ω} \sin(\omega)
$$
Thus, the Fourier transform of the given function f(t) is:
$$
F(ω) = \frac{2}{ω} \sin(\omega)
$$
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Key Concepts
These are the key concepts you need to understand to accurately answer the question.
Rectangular Function
The rectangular function is one of the fundamental concepts in signal processing and Fourier analysis. It refers to a waveform that has a constant value over a finite interval and zero elsewhere. In mathematical terms, for the interval \( -a \leq t \leq a \) it is defined as 1, and 0 outside this range.
When the rectangular function is the subject for a Fourier transform, it converts into a sinc function in the frequency domain. The sinc function is defined as \( \text{sinc}(x) = \frac{\sin(\pi x)}{\pi x} \), with the Fourier transform providing the exact shape and frequency characteristics of the sinc function based on the original rectangular function's duration and amplitude.
When the rectangular function is the subject for a Fourier transform, it converts into a sinc function in the frequency domain. The sinc function is defined as \( \text{sinc}(x) = \frac{\sin(\pi x)}{\pi x} \), with the Fourier transform providing the exact shape and frequency characteristics of the sinc function based on the original rectangular function's duration and amplitude.
Unit Step Function
The unit step function, often symbolized as \(u(t)\), is a piecewise function that jumps from 0 to 1 at a specified value of time \(t\). This function is pivotal in control systems and signal processing as it represents the idea of a system suddenly starting or stopping.
In terms of Fourier transform, the unit step function transforms into a function involving a complex exponential and a delta function. The delta function component signifies an infinite spike at zero frequency, denoting the DC component present due to the non-zero average of the step function signal.
In terms of Fourier transform, the unit step function transforms into a function involving a complex exponential and a delta function. The delta function component signifies an infinite spike at zero frequency, denoting the DC component present due to the non-zero average of the step function signal.
Linearity Property of Fourier Transform
Fourier transforms boast a variety of useful properties, with linearity being one of the most important. This property states that the Fourier transform of a linear combination of functions is the same combination of the Fourier transforms of the individual functions.
Mathematically, for any two functions \(f(t)\) and \(g(t)\), and constants \(a\) and \(b\), the Fourier transform of the linear combination \(a f(t) + b g(t)\) is \(a \mathcal{F}\{f(t)\} + b \mathcal{F}\{g(t)\}\). Because of the linearity property, solving complex waveforms becomes manageable as we can break them into simpler components, transform these individually, and then combine the results.
Mathematically, for any two functions \(f(t)\) and \(g(t)\), and constants \(a\) and \(b\), the Fourier transform of the linear combination \(a f(t) + b g(t)\) is \(a \mathcal{F}\{f(t)\} + b \mathcal{F}\{g(t)\}\). Because of the linearity property, solving complex waveforms becomes manageable as we can break them into simpler components, transform these individually, and then combine the results.
Complex Exponential Function
Complex exponential functions form the backbone of Fourier analysis. They are represented in the form \(e^{j\theta}\), where \(j\) denotes the imaginary unit and \(\theta\) is a real-valued phase angle.
The significance of the complex exponential lies in Euler's formula, which states that \(\(e^{j\theta} = \text{cos}(\theta) + j\text{sin}(\theta)\)\). In the context of Fourier transforms, complex exponentials describe the frequency components of a signal—both their magnitudes and phases. When a signal undergoes a Fourier transformation, it’s decomposed into a spectrum of these complex exponentials, which shows how much of each frequency is present in the original signal and at what phase.
The significance of the complex exponential lies in Euler's formula, which states that \(\(e^{j\theta} = \text{cos}(\theta) + j\text{sin}(\theta)\)\). In the context of Fourier transforms, complex exponentials describe the frequency components of a signal—both their magnitudes and phases. When a signal undergoes a Fourier transformation, it’s decomposed into a spectrum of these complex exponentials, which shows how much of each frequency is present in the original signal and at what phase.