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Lecture 8

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Fourier Analysis.

- Aims:
- Fourier Theory:
- Description of waveforms in terms of a superposition of harmonic waves.
- Fourier series (periodic functions);
- Fourier transforms (aperiodic functions).

- Wavepackets

- Description of waveforms in terms of a superposition of harmonic waves.
- Convolution
- convolution theorem.

- Fourier Theory:

- It is possible to represent (almost) any function as a superposition of harmonic functions.

- Fourier series

- Fourier transforms

- Function f(x), which is periodic in x, can be written:where,
- Expressions for An and Bn follow from the “orthogonality” of the “basis functions”, sin and cos.

- Example: simple case of 3 terms
- Exponential representation:
- with k=2pn/l.

- Periodic top-hat:
- N.B.

Fourier transform

f(x)

Zero when n

is a multiple of 4

- x and k are conjugate variables.
- Analysis applies to a periodic function in any variable.

- t and w are conjugate.
- Example: Forced oscillator
- Response to an arbitrary, periodic, forcing function F(t). We can represent F(t) using [6.1].
- If the response at frequency nwf is R(nwf), then the total response is

Linear in both response and driving amplitude

Linear in both response and driving amplitude

- Non-periodic functions:
- limiting case of periodic function as period ®¥. The component wavenumbers get closer and merge to form a continuum. (Sum becomes an integral)
- This is called Fourier Analysis.
- f(x) and g(k) are Fourier Transforms of each other.

- Example:Top hat
- Similar to Fourier series but now a continuous function of k.

- Gaussain with r.m.s. deviation Dx=s.
- Note
- Fourier transform
- Integration can be performed by completing the square of the exponent -(x2/2s2+ikx).
- where,

=Öp

- The Fourier transform of a Gaussian is a Gaussian.
- Note: Dk=1/s. i.e. DxDk=1
- Important general result:
- “Width” in Fourier space is inversely related to “width” in real space. (same for top hat)

- d-functionÛconstantcosineÛ2 d-functions sineÛ2 d-functions infinite lattice Û infinite lattice of d-functions of d-functions top-hatÛsinc functionGaussianÛGaussian
- In pictures………...

d-function

- Common transforms

- Localised waves
- A wave localised in space can be created by superposing harmonic waves with a narrow range of k values.
- The component harmonic waves have amplitude
- At time t later, the phase of component k will be kx-wt, so
- Provided w/k=constant (independent of k) then the disturbance is unchanged i.e. f(x-vt).
- We have a non-dispersive wave.
- When w/k=f(k) the wave packet changes shape as it propagates.
- We have a dispersive wave.

- Convolution: a central concept in Physics.
- It is the “smearing” or “blurring” of one function by the other.
- Examples occur in all experimental situations where the limited resolution of the apparatus results in a measurement “broader” than the original.
- In this case, f1 (say) represents the true signal and f2 is the effect of the measurement. f2 is the point spread function.

Convolution symbol

Convolution integral

h is the convolution of f1 and f2

h is the convolution of f1 and f2

h is the convolution of f1 and f2

- Convolution and Fourier transforms

- The Fourier transform of a PRODUCT of two functions is the CONVOLUTION of their Fourier transforms.
- Conversely:The Fourier transform of the CONVOLUTION of two functions is a PRODUCT of their Fourier transforms.
- Proof:

F.T.

of

f1.f2

Convolution

of g1 and g2

- Summary:
- If,thenand

- Examples:
- Optical instruments and resolution
- 1-D idealised spectrum of “lines” broadened to give measured spectrum
- 2-D: Response of camera, telescope. Each point in the object is broadened in the image.

- Crystallography. Far field diffraction pattern is a Fourier transform. A perfect crystal is a convolution of “the lattice” and “the basis”.

- Optical instruments and resolution

- Must know….
- Convolution theorem
- How to convolute the following functions.
- d-function and any other function.
- Two top-hats
- Two Gaussians.