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Let \(\operatorname{sinc} x=(\sin \pi x) / \pi x(\operatorname{sinc} 0=1)\). a. If \(a>0, \widehat{\chi}_{|-a, a|}(x)=\chi_{[a, a]}^{V}(x)=2 a \operatorname{sinc} 2 a x\). b. Let \(\mathcal{H}_{a}=\left\\{f \in L^{2}: \hat{f}(\xi)=0\right.\) (a.c.) for \(\left.|\xi|>a\right\\}\). Then \(\mathcal{T}\) is a Hilbert space and \(\\{\sqrt{2 a}\) sinc \((2 a x-k): k \in Z\\}\) is an orthonormal basis for \(\mathcal{H}\). c. (The Sampling Theorem) If \(f \in \mathcal{T}_{a}\), then \(f \in C_{0}\) (after modification on a null set), and \(f(x)=\sum_{-\infty}^{\infty} f(k / 2 a) \operatorname{sinc}(2 a x-k)\), where the series converges both uniformly and in \(L^{2}\). (In the terminology of signal analysis, a signal of bandwidth \(2 a\) is completely determined by sampling its values at a sequence of points \(\\{k / 2 a\\}\) whose spacing is the reciprocal of the bandwidth.)

Short Answer

Expert verified
The sinc function defines the spread of frequencies for a rectangular signal and enables the reconstruction of signals from discrete samples.

Step by step solution

01

Understand the Sinc Function

The sinc function is given by \( \operatorname{sinc} x = \frac{\sin \pi x}{\pi x} \), with the value \( \operatorname{sinc} 0 = 1 \). This function is crucial in signal processing, especially for interpolation and sampling. Note that for small values of \( x \), \( \operatorname{sinc} x \approx 1 \) since \( \sin \pi x \approx \pi x \).
02

Analyze the Fourier Transform of a Rectangular Function

The Fourier transform \( \widehat{\chi}_{|-a, a|}(x) \) of a gate function \( \chi_{[a, a]}^V(x) \) is given as \( 2a \operatorname{sinc}(2ax) \). This represents the transform of the rectangular signal in the frequency domain, and the sinc function demonstrates the spread of frequencies.
03

Understand the Hilbert Space \( \mathcal{H}_{a} \)

The space \( \mathcal{H}_{a} \) contains all functions \( f \) in \( L^{2} \) such that their Fourier transform \( \hat{f}(\xi) = 0 \) outside \([-a, a]\). The set \( \{\sqrt{2a} \operatorname{sinc} (2ax - k) : k \in \mathbb{Z}\} \) forms an orthonormal basis for this space, meaning each function can be represented uniquely by a linear combination of these sinc functions.
04

Apply the Sampling Theorem

If \( f \in \mathcal{T}_{a} \), which consists of functions in \( \mathcal{H}_{a} \), then \( f \) must also be in \( C_{0} \). This implies that \( f \) vanishes at infinity. The sampling theorem states \( f(x) = \sum_{k=-\infty}^{\infty} f\left(\frac{k}{2a}\right) \operatorname{sinc}(2ax - k) \), meaning \( f \) can be reconstructed completely from its samples at points \( \frac{k}{2a} \), spaced inversely to the bandwidth \( 2a \).

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Sinc Function
The sinc function is a fundamental concept in various fields such as signal processing, and it plays a vital role in interpolation and sampling. The mathematical formula for the sinc function is given by \[ \operatorname{sinc} x = \frac{\sin \pi x}{\pi x} \]where the value at zero is defined as 1, i.e., \( \operatorname{sinc} 0 = 1 \). This definition ensures that the sinc function is continuous at zero.
In practical terms, the sinc function is used for reconstructing a continuous signal from its samples. This is because it acts as an "ideal" filter for both analog and digital signal processing. The shape of the sinc function resembles a wave that oscillates and decays as it moves away from the center.
When analyzing small values of \( x \), \( \operatorname{sinc} x \) approximates to 1 since \( \sin \pi x \approx \pi x \). This property allows sinc functions to be particularly useful as building blocks for reconstructing and manipulating signals by acting like a smooth windowing function over a certain spectral range.
In the context of Fourier transforms, the sinc function emerges naturally when working with rectangular pulses in the time domain, highlighting its importance in frequency analysis systems.
Hilbert Spaces
Hilbert spaces are abstract vector spaces equipped with an inner product, allowing length and angle measure computations. They extend the familiar concept of Euclidean spaces to infinite dimensions, making them useful in functional analysis and quantum mechanics.
The Hilbert space \( \mathcal{H}_{a} \) defined in the context of Fourier analysis and signal processing includes all \( L^{2} \) functions whose Fourier transform decreases to zero outside the interval \([-a, a]\). This confines the signal's frequency components within a limited bandwidth, effectively characterizing band-limited functions.
The set \[ \{\sqrt{2a} \operatorname{sinc} (2ax - k) : k \in \mathbb{Z}\} \]forms an orthonormal basis for \( \mathcal{H}_{a} \). This means any function within \( \mathcal{H}_{a} \) can be expressed as a unique sum of these sinc functions. The concept of orthogonal basis in Hilbert spaces enables representation, reconstruction, and transformation of signals effectively, facilitating many signal processing tasks.
The attributes of Hilbert spaces, such as completeness and orthogonality, make them incredibly useful for solving differential equations, optimization problems, and various applications that require functional decomposition into straightforward and manageable components.
Sampling Theorem
The Sampling Theorem is a fundamental bridge between continuous-time signals and discrete-time representations. It states that a band-limited signal can be completely reconstructed from its samples if the sampling frequency is sufficiently high. Specifically, a function \( f \) in \( \mathcal{T}_{a} \) is also in \( C_{0} \), meaning it vanishes at infinity, ensuring smooth decay.
The theorem is fundamentally expressed as: \[ f(x) = \sum_{k=-\infty}^{\infty} f\left(\frac{k}{2a}\right) \operatorname{sinc}(2ax - k) \]Here, the spacing \( \frac{1}{2a} \) is crucial as it relates inversely to the signal's bandwidth \( 2a \). The function \( f(x) \) can hence be reconstructed by summing a series of sinc functions modulated by the signal's samples at points \( \frac{k}{2a} \).
Known in communication technology as the Shannon-Nyquist Sampling Theorem, this result underpins technologies such as audio digitization and digital image processing. By ensuring the signal is sampled above the Nyquist rate, distortions like aliasing can be avoided, thereby retaining the signal's integrity post-reconstruction. This theorem showcases how integrating mathematical concepts like sinc functions leads to practical solutions for signal reproduction in real-world applications.

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Most popular questions from this chapter

If \(f\) is locally integrable on \(\mathbb{R}^{n}\) and \(g \in C^{k}\) has compact support, then \(f * g \in C^{k}\).

Let \(\eta(t)=e^{-1 / t}\) for \(t>0, \eta(t)=0\) for \(t \leq 0\). a. For \(k \in \mathbb{N}\) and \(t>0, \eta^{(k)}(t)=P_{k}(1 / t) e^{-1 / t}\) where \(P_{k}\) is a polynomial of degree \(2 k\). b. \(\eta^{(k)}(0)\) exists and equals zero for all \(k \in \mathbb{N}\).

22\. Since \(F\) commutes with rotations, the Fourier transform of a radial function is radial; that is, if \(F \in L^{1}\left(\mathbb{R}^{n}\right)\) and \(F(x)=f(|x|)\), then \(\widehat{F}(\xi)=g(|\xi|)\), where \(f\) and \(g\) are related as follows. a. Let \(J(\xi)=\int_{S} e^{i x \xi} d \sigma(x)\) where \(\sigma\) is surface measure on the unit sphere \(S\) in \(\mathbb{R}^{n}\) (Theorem 2.49). Then \(J\) is radial - say, \(J(\xi)=j(|\xi|)\) - and \(g(\rho)=\int_{0}^{\infty} j(2 \pi r \rho) f(r) r^{n-1} d r .\) b. \(J\) satisfies \(\sum_{1}^{n} \partial_{k}^{2} J+J=0\). c. \(j\) satisfies \(\rho j^{\prime \prime}(\rho)+(n-1) j^{\prime}(\rho)+\rho j(\rho)=0\). (This equation is a variant of Bessel's equation. The function \(j\) is completely determined by the fact that it is a solution of this equation, is smooth at \(\rho=0\), and satisfies \(j(0)=\sigma(S)=\) \(2 \pi^{n / 2} / \Gamma(n / 2)\). In fact, \(j(\rho)=(2 \pi)^{n / 2} \rho^{(2-n) / 2} J_{(n-2) / 2}(\rho)\) where \(J_{\alpha}\) is the Bessel function of the first kind of order \(\alpha .)\) d. If \(n=3, j(\rho)=4 \pi \rho^{-1} \sin \rho\). (Set \(f(\rho)=\rho j(\rho)\) and use (c) to show that \(f^{\prime \prime}+f=0\). Alternatively, use spherical coordinates to compute the integral defining \(J(0,0, \rho)\) directly.) (use induction on \(k\) ), and in particular, $$ h_{k}(x)=\frac{(-1)^{k}}{\left[\pi^{1 / 2} 2^{k} k !\right]^{1 / 2}} e^{x^{2} / 2}\left(\frac{d}{d x}\right)^{k} e^{-x^{2}} . $$ f. Let \(H_{k}(x)=e^{x^{2} / 2} h_{k}(x)\). Then \(H_{k}\) is a polynomial of degree \(k\), called the \(k\) th normalized Hermite polynomial. The linear span of \(H_{0}, \ldots, H_{m}\) is the set of all polynomials of degree \(\leq m\). (The kth Hermite polynomial as usually defined is \(\left[\pi^{1 / 2} 2^{k} k !\right]^{1 / 2} H_{k}\).) g. \(\left\\{h_{k}\right\\}_{0}^{\infty}\) is an orthonormal basis for \(L^{2}(\mathbb{R})\). (Suppose \(f \perp h_{k}\) for all \(k\), and let \(g(x)=f(x) e^{-x^{2} / 2}\). Show that \(\widehat{g}=0\) by expanding \(e^{-2 \pi i \xi \cdot x}\) in its Maclaurin series and using (f).) h. Define \(A: L^{2} \rightarrow L^{2}\) by \(A f(x)=(2 \pi)^{1 / 4} f(x \sqrt{2 \pi})\), and define \(\tilde{f}=\) \(A^{-1} \mathcal{F} A f\) for \(f \in L^{2}\). Then \(A\) is unitary and \(\widetilde{f}(\xi)=(2 \pi)^{-1 / 2} \int f(x) e^{-i \xi x} d x\). Moreover, \(\widetilde{T f}=-i T(\widetilde{f})\) for \(f \in \mathcal{S}\), and \(\widetilde{h}_{0}=h_{0}\); hence \(\widetilde{h}_{k}=(-i)^{k} h_{k}\). Therefore, if \(\phi_{k}=A h_{k} .\left\\{\phi_{k}\right\\}_{0}^{\infty}\) is an orthonormal basis for \(L^{2}\) consisting of eigenfunctions for \(\mathcal{F}\); namely, \(\widehat{\phi}_{k}=(-i)^{k} \phi_{k}\).

Given \(a>0\), let \(f(x)=e^{-2 \pi x} x^{a-1}\) for \(x>0\) and \(f(x)=0\) for \(x \leq 0\). a. \(f \in L^{1}\), and \(f \in L^{2}\) if \(a>\frac{1}{2}\). b. \(\hat{f}(\xi)=\Gamma(a)[(2 \pi)(1+i \xi)]^{-a}\). (Here we are using the branch of \(z^{a}\) in the right half plane that is positive when \(z\) is positive. Cauchy's theorem may be used to justify the complex substitution \(y=(1+i \xi) x\) in the integral defining \(\widehat{f_{0}}\) ) c. If \(a, b>\frac{1}{2}\) then $$ \int_{-\infty}^{\infty}(1-i x)^{-a}(1+i x)^{-b} d x=\frac{2^{2-a-b} \pi \Gamma(a+b-1)}{\Gamma(a) \Gamma(b)} $$

Let \(\phi(x)=e^{-|x| / 2}\) on \(\mathbb{R}\). Use the Fourier transform to derive the solution \(u=f * \phi\) of the differential equation \(u-u^{\prime \prime}=f\), and then check directly that it works. What hypotheses are needed on \(f\) ?

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